System

Abstract

A system is provided.SOLUTION: A system comprising: input means for a user to input an emotion or experience; emotion analysis means for receiving data from the input means and analyzing the user's emotion; response generation means for generating an empathic response based on the emotion analyzed by the emotion analysis means; response providing means for providing the generated response to the user; and expert introduction means for introducing an expert as needed based on the emotion analysis means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, people often face deep sadness, stress, and other emotional difficulties. However, many feel anxious about sharing these feelings with those around them, often feeling lonely and helpless. Furthermore, there are limited opportunities to process emotions and receive appropriate support, which increases the psychological burden. To solve these issues, a system is needed that allows users to safely share their emotions and provides appropriate support or referrals to specialists. [Means for solving the problem]

[0005] The present invention provides a system including an input means for a user to input emotions and experiences, an emotion analysis means for receiving the input data and analyzing the user's emotions, a response generation means for generating an empathetic response based on the analyzed emotions, a response provision means for providing the generated response to the user, and an expert referral means for introducing an expert to the user as needed based on the emotion analysis. This system allows the user to share their emotions in a safe environment and receive empathetic and professional feedback. Furthermore, the system can also refer the user to a professional counselor as needed, allowing the user to receive further professional support.

[0006] "Input means" refers to an interface that allows a user to input emotions and experiences in the form of text or the like.

[0007] "Emotion analysis means" refers to a module or algorithm that has the function of analyzing text data entered by a user and determining the user's emotions based on the content of that data.

[0008] The "response generation means" refers to a module or algorithm that generates an empathetic and appropriate response to the user based on the emotions analyzed by the emotion analysis means.

[0009] "Response providing means" refers to an interface for displaying or communicating the generated response to the user.

[0010] "Expert referral means" refers to a function or module for introducing an appropriate expert, such as a counselor, to a user as needed based on the results of the emotion analysis means. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0012] 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.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0015] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0016] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0017] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0024] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0031] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0032] The present invention provides a system that allows users to safely share their feelings, such as deep sadness or stress, and provides empathetic and professional feedback. The system analyzes the user's input, generates a response based on the emotion, and, if necessary, refers the user to a specialist.

[0033] System configuration

[0034] The system includes the following main components:

[0035] 1. Input method:

[0036] The device provides an interface for users to input their feelings and experiences in text form. The device provides this input means to the user, such as a web form or a chat box in a mobile application.

[0037] 2. Emotion analysis means:

[0038] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0039] 3. Response Generation Method:

[0040] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[0041] 4. Means of providing response:

[0042] This is an interface for providing the generated response to the user. The terminal uses this means to display the response to the user. Specifically, the response message is displayed on the chat screen.

[0043] 5. Specialist referral methods:

[0044] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[0045] Program processing (natural language explanation)

[0046] When the server receives text data from the device, it first analyzes the emotions of the input text data using emotion analysis means. If the analysis results in the user being "stressed," it passes this result to the next step.

[0047] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0048] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0049] Specific examples

[0050] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0051] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user inputs their feelings and experiences. For example, they might write, "I've been feeling very stressed at work lately, and I'm very tired," into an input form.

[0055] Step 2:

[0056] The terminal sends the text data entered in step 1 to the server. At this time, the text data is sent to the server as an HTTP POST request.

[0057] Step 3:

[0058] The server receives the text data sent from the device and stores it in JSON format.

[0059] Step 4:

[0060] The server uses emotion analysis means to analyze the emotion of the received text data. For example, it analyzes "I'm stressed at work" and determines the user's emotion as "stressed."

[0061] Step 5:

[0062] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0063] Step 6:

[0064] The server transmits the generated response to the terminal based on the response providing means, and at this time, the response message is transmitted to the terminal in JSON format.

[0065] Step 7:

[0066] The device analyzes the response message received from the server and displays it to the user. The user sees the message displayed on the device screen: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0067] Step 8:

[0068] The server further uses the expert referral means based on the sentiment analysis results to generate a message to introduce an appropriate expert as needed. For example, a suggestion message such as "Consider talking to a professional counselor about your current condition" may be generated.

[0069] Step 9:

[0070] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[0071] Step 10:

[0072] The device analyzes the expert introduction message received from the server and displays it to the user. The user confirms the message "Consider talking to a professional counselor about your current condition" displayed on the device screen.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] In modern society, users often experience deep emotions such as sadness and stress, and they need a safe place to share their feelings. However, there is no system that provides empathetic and professional feedback on these emotions, which means users cannot receive appropriate support.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a means for the emotion analysis means to generate a message for introducing an expert when specific conditions are met based on the analysis result, and a means for the response generation means to generate a response using a generative AI model. This enables users to safely share their emotions, receive empathetic responses, and also obtain support from experts as needed.

[0078] "User" refers to an individual who uses the system to input their feelings and experiences and receive responses and support from experts.

[0079] "Input means" refers to a means that provides an interface for users to input their feelings or experiences in text format. Examples include web forms and chat boxes in mobile applications.

[0080] The "emotion analysis means" is a means by which the server receives text data from the input means and analyzes the user's emotions using natural language processing technology.

[0081] The "response generation means" is a module for generating empathetic responses based on the emotions determined by the emotion analysis means. Specifically, it refers to creating responses using a generative AI model.

[0082] The "response providing means" refers to an interface for providing the generated empathetic response to the user. The terminal plays a role in displaying the response to the user.

[0083] The "expert introduction means" is a module that introduces an appropriate expert to the user when the emotion determined by the emotion analysis means satisfies a specific condition.

[0084] "Generative AI model" refers to artificial intelligence technology for generating responses through natural language processing, for example, using a Transformer model (such as GPT-3).

[0085] A "prompt" is a document provided as input to a generative AI model that contains instructions for the AI ​​to generate a response based on that input.

[0086] An embodiment of the present invention is a system for users to safely share their feelings and receive empathetic and professional feedback. The system performs sentiment analysis on user input, generates responses based on the sentiment analysis, and provides referrals to experts as needed.

[0087] System configuration

[0088] The system includes the following main components:

[0089] 1. Input Method

[0090] An interface that allows users to input their feelings and experiences in text format. The device provides the user with a means for this input, and examples include web forms and chat boxes in mobile applications.

[0091] 2. Emotion analysis method

[0092] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0093] 3. Response Generation Method

[0094] This module generates empathetic responses based on the emotions determined by the emotion analysis means. For example, the server uses a Transformer model (such as GPT-3) to create empathetic responses appropriate to the user's emotional state.

[0095] 4. Means of providing a response

[0096] This is an interface for providing the generated response to the user. The terminal uses this means to display the response on the user's screen. Specifically, the response message is displayed on the chat screen.

[0097] 5. Specialist referral channels

[0098] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user according to the conditions.

[0099] System Operation

[0100] When the server receives text data from the terminal, it first analyzes the emotion of the input text data using emotion analysis means. If the analysis result indicates that the user is "stressed," it passes this result to the next step.

[0101] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0102] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0103] Specific examples

[0104] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0105] Example prompts to input to the generative AI model

[0106] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[0107] User Input: I've been under a lot of stress at work lately and I'm really tired.

[0108] Response Generation Prompt: Generate an empathetic message for the user based on the following: "Emotion Label: stressed"

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] The user inputs their emotions and experiences in text format. Specifically, the user uses a device to enter text such as "I've been feeling very stressed at work lately, and I'm very tired" into a chat box or web form and presses the send button. This text data is sent from the device to the server as input. The input data is passed to the server in, for example, JSON format.

[0112] Step 2:

[0113] The server uses sentiment analysis to analyze the text data received from the device. Specifically, the server uses natural language processing technology (e.g., the NLTK library or SpaCy) to analyze the text content. Here, the input data is text information, and the server analyzes this text to assign an sentiment label, such as "stressed." The sentiment label obtained as a result of the analysis is "stressed," which is passed on as output to the next step.

[0114] Step 3:

[0115] The server generates an empathetic response using a response generation method based on the results of the emotion analysis. Specifically, the server inputs a prompt sentence into a generative AI model (e.g., GPT-3). The prompt sentence is "emotion label: stressed," and the AIS model generates a response based on the prompt sentence: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed on as output to the next step.

[0116] Step 4:

[0117] The server sends the generated empathetic response to the terminal. Specifically, the server uses the response providing means to send the generated response in JSON format to the terminal. The terminal displays the received response on the user's screen. For example, the output may be a message on the chat screen saying, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0118] Step 5:

[0119] If the result of the emotion analysis is determined to be "stressed," the server uses the expert referral means to generate a message introducing an appropriate counselor. Specifically, the server uses the expert referral means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition." This message is also sent to the device in JSON format. The device displays the received suggestion message on the user's screen, allowing the user to contact the suggested counselor.

[0120] (Application example 1)

[0121] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] In modern society, mental health issues are becoming more serious in the workplace, schools, and other settings. However, many users lack a place to appropriately share their emotions and stress, and often lack support for self-improvement. Furthermore, the lack of an environment for expert analysis of emotions, immediate empathetic feedback, and rapid referral to specialists prevents efficient mental health care.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0124] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a recommendation means for recommending an expert when the emotions analyzed by the emotion analysis means satisfy certain conditions, and a display means for displaying the generated response and the recommended expert to the user. This enables users to safely share their emotions, receive empathetic feedback, and obtain expert support as needed.

[0125] "Input means" refers to an interface that allows users to input emotions and experiences in text format. Examples include the input screen of a smartphone app or a web form.

[0126] The "emotion analysis means" is a module that analyzes text data from the input means and determines the user's emotions. It uses natural language processing technology to assign emotion labels based on the input data.

[0127] The "response generation means" is a module that generates an empathetic response based on the emotions analyzed by the emotion analysis means. For example, it generates appropriate feedback using a generative AI model.

[0128] The "response providing means" refers to an interface for displaying the generated response to the user. Specifically, this corresponds to the chat screen of a smartphone app.

[0129] The "expert introduction means" is a module that introduces appropriate experts to users as needed based on the sentiment analysis means. It recommends appropriate people from a list of experts according to the analysis results.

[0130] The "recommendation means" is a module that recommends experts when the emotions analyzed by the emotion analysis means meet certain conditions. It selects and recommends appropriate experts to support the user's mental health.

[0131] The "display means" refers to an interface for displaying the generated response and the recommended expert information to the user. Specifically, this corresponds to the display screen of a smartphone or the like.

[0132] The present invention provides a system that allows users to safely share their feelings and experiences and provides empathetic and expert feedback. The system analyzes the user's input, generates a response based on the emotion analysis, and introduces an expert if necessary. Each processing step is performed based on the roles of the server, the terminal, and the user.

[0133] System configuration

[0134] Input Method

[0135] The device provides an interface for users to input their feelings and experiences in text form, such as a smartphone app or a web form.

[0136] Emotion analysis means

[0137] The server analyzes the text data received from the device using emotion analysis. This analysis uses natural language processing technology. Using Hugging Face's Transformers library, emotion labels can be assigned based on the input data. For example, an input such as "I'm very stressed at work and very tired" would be judged as "NEGATIVE."

[0138] Response Generation Method

[0139] Based on the emotion label obtained by the emotion analysis means, the server generates an empathetic response. For example, if the emotion label is "NEGATIVE," a response such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself" is generated. A generative AI model (e.g., GPT-3) is used to generate this response.

[0140] Response delivery method

[0141] The generated response is displayed to the user through a response providing means, specifically, a chat screen on a smartphone app.

[0142] Expert referral methods

[0143] If certain conditions are met as a result of the sentiment analysis, the server will use the expert referral mechanism to recommend an appropriate expert to the user. Based on the analysis results, an appropriate person will be selected from the list of experts and introduced to the user. For example, a message such as "Consider speaking with a professional counselor for your current situation. Recommended expert: Yamada Taro - contact@example.com" will be generated.

[0144] Recommendation and display methods

[0145] The server includes a recommendation unit that recommends an expert when the user's emotion meets a specific condition. The generated response and information about the recommended expert are presented to the user through a display unit, such as a smartphone display screen.

[0146] Specific examples

[0147] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server performs emotion analysis on this text data and determines it to be "NEGATIVE." The server then uses the response generation means to generate an empathetic response such as "It seems like you're feeling very stressed at work. It's important that you take some time to rest for yourself." This response is then displayed to the user via the response providing means.

[0148] Furthermore, the server uses the expert introduction means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com," which is also sent to the terminal. The terminal displays this message, and the user can contact the suggested counselor.

[0149] Prompt Sentence Examples

[0150] TXT

[0151] "I've been stressed out at work lately and I'm really tired."

[0152] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[0153] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0154] Step 1:

[0155] Users input their emotions and experiences in text format. Specifically, they use the input screen of a smartphone app or a web form to freely enter the stress and worries they are feeling. This text data is then passed to the next processing step.

[0156] Step 2:

[0157] The device receives text data entered by the user and sends it to the server. At this time, the data is transmitted to the server via an endpoint (API). The server then receives the user's input data and prepares it for analysis.

[0158] Step 3:

[0159] The server analyzes the received text data using a sentiment analysis tool. Natural language processing techniques are used to determine the user's sentiment from the text data. The specific software used is the Hugging Face Transformers library. Based on the input data, an emotional label (e.g., "NEGATIVE") is assigned.

[0160] Step 4:

[0161] The server generates an empathetic response using the response generation means based on the emotion label obtained by the emotion analysis means. In this process, a generative AI model (e.g., GPT-3) is used to generate a natural-spoken response that matches the emotion label. The emotion label is used as input, and an appropriate response sentence is generated as output.

[0162] Step 5:

[0163] The generated response is sent from the server to the terminal through the response providing means, where the response is converted into a format that can be displayed to the user. The user can check the generated response on the chat screen of the terminal.

[0164] Step 6:

[0165] Based on the results of the emotion analysis, the server uses expert referral methods to recommend appropriate experts as needed. If the emotion label meets certain conditions (e.g., "high stress level"), the server selects an appropriate counselor or mental health professional from a list of experts, allowing the user to receive appropriate support.

[0166] Step 7:

[0167] The server also transmits the expert recommendation information generated through the recommendation means to the terminal. The terminal presents this to the user using the display means. For example, a message such as "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com" is displayed. The user can take the next action, such as counseling, based on this information.

[0168] These steps allow users to receive appropriate feedback based on their emotions and access professional support if needed.

[0169] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0170] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[0171] System configuration

[0172] The system includes the following main components:

[0173] 1. Input method:

[0174] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides this input means to users, such as web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[0175] 2. Emotion analysis means:

[0176] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0177] 3. Emotion Engine:

[0178] This is a specialized module for recognizing emotions from user input data. When there is voice input, it uses the voice recognition module, and when there is image input, it uses the facial expression recognition module to accurately grasp the user's emotional state.

[0179] 4. Response Generation Method:

[0180] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[0181] 5. Means of providing response:

[0182] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[0183] 6. Specialist Referral Methods:

[0184] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[0185] Program processing (natural language explanation)

[0186] When the server receives text, voice, or facial expression data from the device, it first uses emotion analysis to analyze the emotion of the input data. For example, if a user inputs text like "I've been stressed at work lately, and I'm very tired," it might determine the emotion as "stressed." It can then more accurately identify the user's emotional state through facial expression analysis of voice data and facial images.

[0187] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0188] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0189] Specific examples

[0190] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0191] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[0192] The processing flow will be explained below.

[0193] Step 1:

[0194] The user inputs their emotions and experiences, for example, by typing "I've been feeling very stressed at work lately, and I'm very tired" into a text field, or by using a voice input interface to say, "I've been feeling very stressed at work lately, and I'm very tired." Alternatively, facial expression data can be captured by pointing their face through the camera.

[0195] Step 2:

[0196] The terminal sends the text data, voice data, and facial expression data entered in step 1 to the server. The text data and voice data are sent as an HTTP POST request. Similarly, the facial expression data is sent as an image.

[0197] Step 3:

[0198] The server receives the data sent from the device and stores it in JSON or image format.

[0199] Step 4:

[0200] The server uses an emotion analysis means and emotion engine to analyze the emotions of the received text, voice, and facial expression data. For example, it analyzes the text data "I'm stressed at work" and determines the user's emotion as "stressed." In the case of voice data, it converts the voice into text through a voice recognition module and analyzes emotions based on that text. In the case of facial expression data, it uses a facial expression recognition module to determine emotions from changes in facial expressions.

[0201] Step 5:

[0202] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0203] Step 6:

[0204] The server transmits the generated response to the terminal based on the response providing means, and the response message is transmitted to the terminal in JSON format.

[0205] Step 7:

[0206] The device analyzes the response message received from the server and conveys it to the user by display or voice. The user sees the message on the device screen or hears the message, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0207] Step 8:

[0208] The server further uses the expert referral means based on the sentiment analysis result to generate a message to introduce an appropriate expert as needed, for example, a suggestion message such as "Consider talking to a professional counselor about your current condition."

[0209] Step 9:

[0210] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[0211] Step 10:

[0212] The device analyzes the expert introduction message received from the server and displays it to the user. The user sees the message "Consider speaking to a professional counselor about your current condition" on the device screen and can contact an expert if necessary.

[0213] Example 2

[0214] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] In modern society, users need a safe way to share their deep emotions, such as sadness or stress, and receive expert, empathetic feedback. However, existing systems often struggle to accurately analyze emotions and generate appropriate responses. They also lack the ability to quickly and appropriately refer users to experts. This can leave users frustrated and anxious, potentially exacerbating the problem.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0217] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response provision means for providing the generated response to the user, an expert referral means for introducing an expert as needed based on the emotion analysis means, a data transmission means for appropriately formatting data transmitted from the input means, and a generative AI model for generating an expert response based on the emotion label determined by the emotion analysis means. This allows the user to have their emotions accurately analyzed, receive an empathetic and expert response, and be referred to an appropriate expert as needed.

[0218] "Input means" refers to a means that provides an interface for a user to input emotions and experiences as text, voice, or facial expression data.

[0219] The "emotion analysis means" is a module that analyzes text, voice, or facial expression data received from the terminal and determines the user's emotions.

[0220] The "response generation means" includes a module that generates an empathetic response based on the emotion determined by the emotion analysis means.

[0221] The "response providing means" is an interface for providing the generated response to the user.

[0222] The "expert introduction means" is a module that introduces an appropriate expert (such as a counselor) to the user when the emotion determined by the emotion analysis means meets certain conditions.

[0223] The "data transmission means" has the function of appropriately formatting data transmitted from the input means and transmitting the formatted data to the server.

[0224] A "generative AI model" is an artificial intelligence model that generates specialized responses based on emotion labels determined by emotion analysis means, and utilizes natural language processing technology.

[0225] MODE FOR CARRYING OUT THE INVENTION

[0226] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[0227] System configuration

[0228] The system includes the following main components:

[0229] 1. Input Method

[0230] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides users with this input method, which may include web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[0231] 2. Emotion analysis method

[0232] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0233] 3. Data Transmission Method

[0234] The terminal has the function of appropriately formatting data sent from the input means and sending it to the server. Specifically, the terminal converts input text data and voice data into an appropriate format and sends it to the server.

[0235] 4. Generative AI Models

[0236] This is a specialized module for recognizing emotions from user input data and generating empathetic responses. It uses generative AI models (such as GPT-3, which utilizes natural language processing technology) to create empathetic responses that are appropriate for the user's emotional situation.

[0237] 5. Response Generation Methods

[0238] This module generates an empathetic response based on the emotions determined by the emotion analysis means. The generated response might be something like, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0239] 6. Means of providing a response

[0240] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[0241] 7. Specialist Referral Methods

[0242] This module introduces the user to an appropriate expert (e.g., a counselor) when the emotion determined by the emotion analysis means meets certain conditions. The server maintains a list of appropriate experts and generates an introduction message for the user depending on the conditions. For example, it generates a suggestion message such as "Consider talking to a professional counselor about your current condition," and sends this to the terminal.

[0243] Specific examples

[0244] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." The server then uses the generative AI model to generate a response such as "It seems like you're feeling very stressed at work. It's important to take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user.

[0245] Furthermore, the server generates a suggestion message using an expert introduction means, saying "Consider talking to a professional counselor about your current condition," which is also sent to the terminal. The user can then contact the suggested counselor.

[0246] Prompt Sentence Examples

[0247] "Generate an empathetic response about how the user has been experiencing a lot of stress at work lately."

[0248] "This user's emotion has been determined to be 'stressed'. Please generate a counselor introduction based on this."

[0249] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[0250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0251] Step 1:

[0252] User emotion input

[0253] Users use the device to input their emotions and experiences, for example, through a web form, a chat box in a mobile application, a voice input interface, or a camera to input emotional data.

[0254] Input: User emotions and experiences (text, voice, images)

[0255] Output: Temporarily storing and preparing data by the device

[0256] Specific operation example:

[0257] A user types into a chat box on a mobile app, "I've been feeling very stressed at work lately and am very tired."

[0258] Step 2:

[0259] Sending text data

[0260] The device sends the user's input data to the server, and in the process, the device converts the data into the appropriate format depending on the input format (text, audio, image).

[0261] Input: User-entered text, voice, and image data

[0262] Output: Formatted data is sent to the server

[0263] Specific operation example:

[0264] The device sends text data such as "I've been under a lot of stress at work lately and I'm very tired" to the server.

[0265] Step 3:

[0266] Emotion analysis

[0267] The server analyzes the received data using emotion analysis techniques, such as text analysis, voice analysis, and facial expression analysis, to determine the user's emotions.

[0268] Input: User data sent to the server

[0269] Output: Annotated emotion label (e.g. "stressed")

[0270] Specific operation example:

[0271] The server analyzes the text "I've been stressed at work lately and I'm very tired" and determines that it is "stressed."

[0272] Step 4:

[0273] Generating an empathic response

[0274] The server uses a generative AI model to generate an empathetic response based on the emotion determined by the emotion analysis means.

[0275] Input: Annotated emotion labels, user input data

[0276] Output: A generated empathetic response (e.g., "It sounds like you're under a lot of stress at work. It's important that you take some time off for yourself.")

[0277] Specific operation example:

[0278] The generative AI model generates the response, "It seems like you're under a lot of stress at work. It's important to take some time for yourself to relax."

[0279] Step 5:

[0280] Providing a response

[0281] The generated response is sent to the terminal through the response providing means and displayed to the user, and the terminal provides an interface for displaying the response in an easy-to-view manner to the user.

[0282] Input: Generated empathic response

[0283] Output: Response displayed to the user

[0284] Specific operation example:

[0285] The device displays the generated response on the chat screen, and the user sees the response: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0286] Step 6:

[0287] Generating and providing expert referrals

[0288] Based on the results of the sentiment analysis, the server generates a message introducing a specialist (such as a counselor) if the user meets certain conditions and provides this to the user.

[0289] Input: Sentiment analysis results, list of experts

[0290] Output: Expert introduction message

[0291] Specific operation example:

[0292] The server generates an introductory message for users who are judged to be "stressed," saying, "Consider talking to a professional counselor about your current condition." The server then sends this message to the user's device. The device then displays this message to the user.

[0293] Thus, the present invention provides a system that allows users to have their emotions accurately analyzed and receive empathetic and professional responses.

[0294] (Application example 2)

[0295] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0296] In modern food delivery services, users often experience stress and frustration during the ordering process. These emotions can negatively impact service ratings and user experience, ultimately leading to lower customer satisfaction. However, traditional delivery services lack a mechanism for analyzing user emotions in real time and providing appropriate feedback and support. To solve this problem, it is necessary to develop a system that can accurately grasp user emotions and provide necessary support and empathetic feedback.

[0297] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an interface means through which the user inputs emotions and experiences, an analysis means that receives data from the interface means and analyzes the user's emotions, a generation means that generates an empathetic response based on the emotions analyzed by the analysis means, a provision means that provides the generated response to the user, a referral means that introduces an expert as needed based on the analysis means, and a notification means that displays the generated response and referral message to the user via the response provision means. This makes it possible to analyze the user's emotions in real time and provide appropriate feedback and support.

[0298] "Interface means" refers to input devices and software that allow users to input emotions and experiences.

[0299] The "analysis means" is a function for analyzing data received from the interface means and determining the user's emotions.

[0300] The "generation means" is a function for generating an empathetic response based on the analyzed emotions.

[0301] The "means for providing" is a function for transmitting the generated response to the user.

[0302] "Introduction method" is a function that introduces users to appropriate experts based on the analysis results.

[0303] The "notification means" is a function for displaying to the user the response or introduction message generated through the response providing means.

[0304] The present invention is a system that allows users to share their feelings and experiences, provides empathetic responses based on those feelings and experiences, and refers users to experts as needed. The system mainly includes the following main components: an interface means, an analysis means, a generation means, a provision means, a referral means, and a notification means.

[0305] System configuration

[0306] The system operates as follows:

[0307] 1. Interface Method

[0308] Users can input their emotions and experiences through interface means, which are implemented through smartphone applications and include voice input, text input, or facial recognition via a camera.

[0309] 2. Analysis tools

[0310] The data received from the interface means is sent to a server, where emotion analysis is performed by the analysis means. This analysis uses an emotion recognition model, such as an emotion analysis algorithm by GPT-3 or TensorFlow, to determine whether the user's input is "stressed" or "neutral," etc.

[0311] 3. Generation means

[0312] The server generates an empathetic response based on the emotional information analyzed by the analytical means. For example, in response to an input such as "I've been stressed at work lately and I'm very tired," the generative AI model creates a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break."

[0313] 4. Means of provision

[0314] The generated response is transmitted to the user via a means of providing the response, which is also implemented on a smartphone application and displayed to the user as a text message or a voice message.

[0315] 5. Referral methods

[0316] If necessary, the system will refer the user to a specialist. For example, if the analysis results in a "stressed" condition, the system will generate a message such as "Consider talking to a professional counselor."

[0317] 6. Means of notification

[0318] To always provide timely feedback to the user, a notification means is used, which has the function of displaying the generated response or introduction message on the user's terminal.

[0319] Usage example

[0320] When a user types, "I've been stressed at work lately and I'm very tired," the data is sent to the server through the smartphone's input interface. The server receives this data, performs emotional analysis, and determines the result as "stressed." It then generates an empathetic response, "It seems you're feeling stressed while ordering. I recommend you take a short break," and provides this to the user. If necessary, an introductory message is also displayed, saying, "Consider talking to a professional counselor."

[0321] Prompt Sentence Examples

[0322] "Based on the text entered by the user, analyze their sentiment and generate empathetic feedback. Also, generate a message to connect them with the right expert."

[0323] Thus, by following the above components and processing steps, a system can be realized that allows users to safely share their feelings and provide empathetic and professional feedback.

[0324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0325] Step 1:

[0326] The device accepts input of emotions and experiences from the user. This input can be text input, voice input, or facial expression recognition. For example, the input text "I've been stressed at work lately and I'm very tired" is acquired by the device.

[0327] input:

[0328] User text input, voice input, or facial expression data.

[0329] output:

[0330] Emotional and experience data sent to the server.

[0331] Step 2:

[0332] The device sends the acquired emotion and experience data to a server, which then analyzes the received data using analytical means. For example, an emotion recognition algorithm using GPT-3 or TensorFlow is used for the analysis.

[0333] input:

[0334] Emotional and experience data sent from the device.

[0335] output:

[0336] The results of sentiment analysis.

[0337] Step 3:

[0338] The server analyzes the received data and determines the emotion. The analyzed emotion is determined as "stressed" or "neutral," etc. The specific analysis process involves using GPT-3 to analyze text data and assign emotion labels. Voice data and facial expression data are also analyzed in the same way.

[0339] input:

[0340] Emotional and experiential data.

[0341] output:

[0342] The determined emotion label (e.g., "stressed").

[0343] Step 4:

[0344] The server generates an empathetic response using a generative means based on the determined emotion label. A generative AI model (e.g., GPT-3) is used to create an appropriate empathetic response. Specifically, for input such as "I've been stressed at work lately and I'm very tired," a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break" is generated.

[0345] input:

[0346] An emotion label (e.g., "stressed").

[0347] output:

[0348] Generated empathic response messages.

[0349] Step 5:

[0350] The server provides the generated empathetic response to the terminal through the providing means, and the terminal communicates the generated response to the user by displaying or audibly displaying the response. Specifically, the response is displayed as a text message on the application.

[0351] input:

[0352] Generated empathic response messages.

[0353] output:

[0354] The response message is displayed on the terminal.

[0355] Step 6:

[0356] The server will refer the user to a specialist if necessary. For example, if the emotion label is determined to be "stressed," the server will use the referral mechanism to generate a referral message such as "Consider speaking to a professional counselor."

[0357] input:

[0358] An emotion label (e.g., "stressed").

[0359] output:

[0360] An expert introduction message is generated.

[0361] Step 7:

[0362] The server provides the generated introduction message to the terminal via a notification means, and the terminal displays the introduction message to the user. Specifically, the introduction message is displayed as an expert introduction message on the application.

[0363] input:

[0364] Generated expert introduction message.

[0365] output:

[0366] An introductory message will be displayed on your device.

[0367] Through these steps, we will create a system that can analyze users' emotions in real time and provide appropriate feedback and professional support.

[0368] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0369] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0370] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0371] [Second embodiment]

[0372] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0373] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0374] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0375] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0376] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0377] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0378] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0379] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0380] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0381] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0382] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0383] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0384] The present invention provides a system that allows users to safely share their feelings, such as deep sadness or stress, and provides empathetic and professional feedback. The system analyzes the user's input, generates a response based on the emotion, and, if necessary, refers the user to a specialist.

[0385] System configuration

[0386] The system includes the following main components:

[0387] 1. Input method:

[0388] The device provides an interface for users to input their feelings and experiences in text form. The device provides this input means to the user, such as a web form or a chat box in a mobile application.

[0389] 2. Emotion analysis means:

[0390] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0391] 3. Response Generation Method:

[0392] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[0393] 4. Means of providing response:

[0394] This is an interface for providing the generated response to the user. The terminal uses this means to display the response to the user. Specifically, the response message is displayed on the chat screen.

[0395] 5. Specialist referral methods:

[0396] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[0397] Program processing (natural language explanation)

[0398] When the server receives text data from the device, it first analyzes the emotions of the input text data using emotion analysis means. If the analysis results in the user being "stressed," it passes this result to the next step.

[0399] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0400] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0401] Specific examples

[0402] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0403] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[0404] The processing flow will be explained below.

[0405] Step 1:

[0406] The user inputs their feelings and experiences. For example, they might write, "I've been feeling very stressed at work lately, and I'm very tired," into an input form.

[0407] Step 2:

[0408] The terminal sends the text data entered in step 1 to the server. At this time, the text data is sent to the server as an HTTP POST request.

[0409] Step 3:

[0410] The server receives the text data sent from the device and stores it in JSON format.

[0411] Step 4:

[0412] The server uses emotion analysis means to analyze the emotion of the received text data. For example, it analyzes "I'm stressed at work" and determines the user's emotion as "stressed."

[0413] Step 5:

[0414] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0415] Step 6:

[0416] The server transmits the generated response to the terminal based on the response providing means, and at this time, the response message is transmitted to the terminal in JSON format.

[0417] Step 7:

[0418] The device analyzes the response message received from the server and displays it to the user. The user sees the message displayed on the device screen: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0419] Step 8:

[0420] The server further uses the expert referral means based on the sentiment analysis results to generate a message to introduce an appropriate expert as needed. For example, a suggestion message such as "Consider talking to a professional counselor about your current condition" may be generated.

[0421] Step 9:

[0422] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[0423] Step 10:

[0424] The device analyzes the expert introduction message received from the server and displays it to the user. The user confirms the message "Consider talking to a professional counselor about your current condition" displayed on the device screen.

[0425] Example 1

[0426] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0427] In modern society, users often experience deep emotions such as sadness and stress, and they need a safe place to share their feelings. However, there is no system that provides empathetic and professional feedback on these emotions, which means users cannot receive appropriate support.

[0428] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0429] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a means for the emotion analysis means to generate a message for introducing an expert when specific conditions are met based on the analysis result, and a means for the response generation means to generate a response using a generative AI model. This enables users to safely share their emotions, receive empathetic responses, and also obtain support from experts as needed.

[0430] "User" refers to an individual who uses the system to input their feelings and experiences and receive responses and support from experts.

[0431] "Input means" refers to a means that provides an interface for users to input their feelings or experiences in text format. Examples include web forms and chat boxes in mobile applications.

[0432] The "emotion analysis means" is a means by which the server receives text data from the input means and analyzes the user's emotions using natural language processing technology.

[0433] The "response generation means" is a module for generating empathetic responses based on the emotions determined by the emotion analysis means. Specifically, it refers to creating responses using a generative AI model.

[0434] The "response providing means" refers to an interface for providing the generated empathetic response to the user. The terminal plays a role in displaying the response to the user.

[0435] The "expert introduction means" is a module that introduces an appropriate expert to the user when the emotion determined by the emotion analysis means satisfies a specific condition.

[0436] "Generative AI model" refers to artificial intelligence technology for generating responses through natural language processing, for example, using a Transformer model (such as GPT-3).

[0437] A "prompt" is a document provided as input to a generative AI model that contains instructions for the AI ​​to generate a response based on that input.

[0438] An embodiment of the present invention is a system for users to safely share their feelings and receive empathetic and professional feedback. The system performs sentiment analysis on user input, generates responses based on the sentiment analysis, and provides referrals to experts as needed.

[0439] System configuration

[0440] The system includes the following main components:

[0441] 1. Input Method

[0442] An interface that allows users to input their feelings and experiences in text format. The device provides the user with a means for this input, and examples include web forms and chat boxes in mobile applications.

[0443] 2. Emotion analysis method

[0444] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0445] 3. Response Generation Method

[0446] This module generates empathetic responses based on the emotions determined by the emotion analysis means. For example, the server uses a Transformer model (such as GPT-3) to create empathetic responses appropriate to the user's emotional state.

[0447] 4. Means of providing a response

[0448] This is an interface for providing the generated response to the user. The terminal uses this means to display the response on the user's screen. Specifically, the response message is displayed on the chat screen.

[0449] 5. Specialist referral channels

[0450] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user according to the conditions.

[0451] System Operation

[0452] When the server receives text data from the terminal, it first analyzes the emotion of the input text data using emotion analysis means. If the analysis result indicates that the user is "stressed," it passes this result to the next step.

[0453] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0454] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0455] Specific examples

[0456] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0457] Example prompts to input to the generative AI model

[0458] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[0459] User Input: I've been under a lot of stress at work lately and I'm really tired.

[0460] Response Generation Prompt: Generate an empathetic message for the user based on the following: "Emotion Label: stressed"

[0461] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0462] Step 1:

[0463] The user inputs their emotions and experiences in text format. Specifically, the user uses a device to enter text such as "I've been feeling very stressed at work lately, and I'm very tired" into a chat box or web form and presses the send button. This text data is sent from the device to the server as input. The input data is passed to the server in, for example, JSON format.

[0464] Step 2:

[0465] The server uses sentiment analysis to analyze the text data received from the device. Specifically, the server uses natural language processing technology (e.g., the NLTK library or SpaCy) to analyze the text content. Here, the input data is text information, and the server analyzes this text to assign an sentiment label, such as "stressed." The sentiment label obtained as a result of the analysis is "stressed," which is passed on as output to the next step.

[0466] Step 3:

[0467] The server generates an empathetic response using a response generation method based on the results of the emotion analysis. Specifically, the server inputs a prompt sentence into a generative AI model (e.g., GPT-3). The prompt sentence is "emotion label: stressed," and the AIS model generates a response based on the prompt sentence: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed on as output to the next step.

[0468] Step 4:

[0469] The server sends the generated empathetic response to the terminal. Specifically, the server uses the response providing means to send the generated response in JSON format to the terminal. The terminal displays the received response on the user's screen. For example, the output may be a message on the chat screen saying, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0470] Step 5:

[0471] If the result of the emotion analysis is determined to be "stressed," the server uses the expert referral means to generate a message introducing an appropriate counselor. Specifically, the server uses the expert referral means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition." This message is also sent to the device in JSON format. The device displays the received suggestion message on the user's screen, allowing the user to contact the suggested counselor.

[0472] (Application example 1)

[0473] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0474] In modern society, mental health issues are becoming more serious in the workplace, schools, and other settings. However, many users lack a place to appropriately share their emotions and stress, and often lack support for self-improvement. Furthermore, the lack of an environment for expert analysis of emotions, immediate empathetic feedback, and rapid referral to specialists prevents efficient mental health care.

[0475] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0476] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a recommendation means for recommending an expert when the emotions analyzed by the emotion analysis means satisfy certain conditions, and a display means for displaying the generated response and the recommended expert to the user. This enables users to safely share their emotions, receive empathetic feedback, and obtain expert support as needed.

[0477] "Input means" refers to an interface that allows users to input emotions and experiences in text format. Examples include the input screen of a smartphone app or a web form.

[0478] The "emotion analysis means" is a module that analyzes text data from the input means and determines the user's emotions. It uses natural language processing technology to assign emotion labels based on the input data.

[0479] The "response generation means" is a module that generates an empathetic response based on the emotions analyzed by the emotion analysis means. For example, it generates appropriate feedback using a generative AI model.

[0480] The "response providing means" refers to an interface for displaying the generated response to the user. Specifically, this corresponds to the chat screen of a smartphone app.

[0481] The "expert introduction means" is a module that introduces appropriate experts to users as needed based on the sentiment analysis means. It recommends appropriate people from a list of experts according to the analysis results.

[0482] The "recommendation means" is a module that recommends experts when the emotions analyzed by the emotion analysis means meet certain conditions. It selects and recommends appropriate experts to support the user's mental health.

[0483] The "display means" refers to an interface for displaying the generated response and the recommended expert information to the user. Specifically, this corresponds to the display screen of a smartphone or the like.

[0484] The present invention provides a system that allows users to safely share their feelings and experiences and provides empathetic and expert feedback. The system analyzes the user's input, generates a response based on the emotion analysis, and introduces an expert if necessary. Each processing step is performed based on the roles of the server, the terminal, and the user.

[0485] System configuration

[0486] Input Method

[0487] The device provides an interface for users to input their feelings and experiences in text form, such as a smartphone app or a web form.

[0488] Emotion analysis means

[0489] The server analyzes the text data received from the device using emotion analysis. This analysis uses natural language processing technology. Using Hugging Face's Transformers library, emotion labels can be assigned based on the input data. For example, an input such as "I'm very stressed at work and very tired" would be judged as "NEGATIVE."

[0490] Response Generation Method

[0491] Based on the emotion label obtained by the emotion analysis means, the server generates an empathetic response. For example, if the emotion label is "NEGATIVE," a response such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself" is generated. A generative AI model (e.g., GPT-3) is used to generate this response.

[0492] Response delivery method

[0493] The generated response is displayed to the user through a response providing means, specifically, a chat screen on a smartphone app.

[0494] Expert referral methods

[0495] If certain conditions are met as a result of the sentiment analysis, the server will use the expert referral mechanism to recommend an appropriate expert to the user. Based on the analysis results, an appropriate person will be selected from the list of experts and introduced to the user. For example, a message such as "Consider speaking with a professional counselor for your current situation. Recommended expert: Yamada Taro - contact@example.com" will be generated.

[0496] Recommendation and display methods

[0497] The server includes a recommendation unit that recommends an expert when the user's emotion meets a specific condition. The generated response and information about the recommended expert are presented to the user through a display unit, such as a smartphone display screen.

[0498] Specific examples

[0499] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server performs emotion analysis on this text data and determines it to be "NEGATIVE." The server then uses the response generation means to generate an empathetic response such as "It seems like you're feeling very stressed at work. It's important that you take some time to rest for yourself." This response is then displayed to the user via the response providing means.

[0500] Furthermore, the server uses the expert introduction means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com," which is also sent to the terminal. The terminal displays this message, and the user can contact the suggested counselor.

[0501] Prompt Sentence Examples

[0502] TXT

[0503] "I've been stressed out at work lately and I'm really tired."

[0504] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[0505] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0506] Step 1:

[0507] Users input their emotions and experiences in text format. Specifically, they use the input screen of a smartphone app or a web form to freely enter the stress and worries they are feeling. This text data is then passed to the next processing step.

[0508] Step 2:

[0509] The device receives text data entered by the user and sends it to the server. At this time, the data is transmitted to the server via an endpoint (API). The server then receives the user's input data and prepares it for analysis.

[0510] Step 3:

[0511] The server analyzes the received text data using a sentiment analysis tool. Natural language processing techniques are used to determine the user's sentiment from the text data. The specific software used is the Hugging Face Transformers library. Based on the input data, an emotional label (e.g., "NEGATIVE") is assigned.

[0512] Step 4:

[0513] The server generates an empathetic response using the response generation means based on the emotion label obtained by the emotion analysis means. In this process, a generative AI model (e.g., GPT-3) is used to generate a natural-spoken response that matches the emotion label. The emotion label is used as input, and an appropriate response sentence is generated as output.

[0514] Step 5:

[0515] The generated response is sent from the server to the terminal through the response providing means, where the response is converted into a format that can be displayed to the user. The user can check the generated response on the chat screen of the terminal.

[0516] Step 6:

[0517] Based on the results of the emotion analysis, the server uses expert referral methods to recommend appropriate experts as needed. If the emotion label meets certain conditions (e.g., "high stress level"), the server selects an appropriate counselor or mental health professional from a list of experts, allowing the user to receive appropriate support.

[0518] Step 7:

[0519] The server also transmits the expert recommendation information generated through the recommendation means to the terminal. The terminal presents this to the user using the display means. For example, a message such as "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com" is displayed. The user can take the next action, such as counseling, based on this information.

[0520] These steps allow users to receive appropriate feedback based on their emotions and access professional support if needed.

[0521] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0522] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[0523] System configuration

[0524] The system includes the following main components:

[0525] 1. Input method:

[0526] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides this input means to users, such as web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[0527] 2. Emotion analysis means:

[0528] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0529] 3. Emotion Engine:

[0530] This is a specialized module for recognizing emotions from user input data. When there is voice input, it uses the voice recognition module, and when there is image input, it uses the facial expression recognition module to accurately grasp the user's emotional state.

[0531] 4. Response Generation Method:

[0532] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[0533] 5. Means of providing response:

[0534] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[0535] 6. Specialist Referral Methods:

[0536] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[0537] Program processing (natural language explanation)

[0538] When the server receives text, voice, or facial expression data from the device, it first uses emotion analysis to analyze the emotion of the input data. For example, if a user inputs text like "I've been stressed at work lately, and I'm very tired," it might determine the emotion as "stressed." It can then more accurately identify the user's emotional state through facial expression analysis of voice data and facial images.

[0539] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0540] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0541] Specific examples

[0542] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0543] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] The user inputs their emotions and experiences, for example, by typing "I've been feeling very stressed at work lately, and I'm very tired" into a text field, or by using a voice input interface to say, "I've been feeling very stressed at work lately, and I'm very tired." Alternatively, facial expression data can be captured by pointing their face through the camera.

[0547] Step 2:

[0548] The terminal sends the text data, voice data, and facial expression data entered in step 1 to the server. The text data and voice data are sent as an HTTP POST request. Similarly, the facial expression data is sent as an image.

[0549] Step 3:

[0550] The server receives the data sent from the device and stores it in JSON or image format.

[0551] Step 4:

[0552] The server uses an emotion analysis means and emotion engine to analyze the emotions of the received text, voice, and facial expression data. For example, it analyzes the text data "I'm stressed at work" and determines the user's emotion as "stressed." In the case of voice data, it converts the voice into text through a voice recognition module and analyzes emotions based on that text. In the case of facial expression data, it uses a facial expression recognition module to determine emotions from changes in facial expressions.

[0553] Step 5:

[0554] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0555] Step 6:

[0556] The server transmits the generated response to the terminal based on the response providing means, and the response message is transmitted to the terminal in JSON format.

[0557] Step 7:

[0558] The device analyzes the response message received from the server and conveys it to the user by display or voice. The user sees the message on the device screen or hears the message, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0559] Step 8:

[0560] The server further uses the expert referral means based on the sentiment analysis result to generate a message to introduce an appropriate expert as needed, for example, a suggestion message such as "Consider talking to a professional counselor about your current condition."

[0561] Step 9:

[0562] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[0563] Step 10:

[0564] The device analyzes the expert introduction message received from the server and displays it to the user. The user sees the message "Consider speaking to a professional counselor about your current condition" on the device screen and can contact an expert if necessary.

[0565] Example 2

[0566] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0567] In modern society, users need a safe way to share their deep emotions, such as sadness or stress, and receive expert, empathetic feedback. However, existing systems often struggle to accurately analyze emotions and generate appropriate responses. They also lack the ability to quickly and appropriately refer users to experts. This can leave users frustrated and anxious, potentially exacerbating the problem.

[0568] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0569] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response provision means for providing the generated response to the user, an expert referral means for introducing an expert as needed based on the emotion analysis means, a data transmission means for appropriately formatting data transmitted from the input means, and a generative AI model for generating an expert response based on the emotion label determined by the emotion analysis means. This allows the user to have their emotions accurately analyzed, receive an empathetic and expert response, and be referred to an appropriate expert as needed.

[0570] "Input means" refers to a means that provides an interface for a user to input emotions and experiences as text, voice, or facial expression data.

[0571] The "emotion analysis means" is a module that analyzes text, voice, or facial expression data received from the terminal and determines the user's emotions.

[0572] The "response generation means" includes a module that generates an empathetic response based on the emotion determined by the emotion analysis means.

[0573] The "response providing means" is an interface for providing the generated response to the user.

[0574] The "expert introduction means" is a module that introduces an appropriate expert (such as a counselor) to the user when the emotion determined by the emotion analysis means meets certain conditions.

[0575] The "data transmission means" has the function of appropriately formatting data transmitted from the input means and transmitting the formatted data to the server.

[0576] A "generative AI model" is an artificial intelligence model that generates specialized responses based on emotion labels determined by emotion analysis means, and utilizes natural language processing technology.

[0577] MODE FOR CARRYING OUT THE INVENTION

[0578] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[0579] System configuration

[0580] The system includes the following main components:

[0581] 1. Input Method

[0582] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides users with this input method, which may include web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[0583] 2. Emotion analysis method

[0584] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0585] 3. Data Transmission Method

[0586] The terminal has the function of appropriately formatting data sent from the input means and sending it to the server. Specifically, the terminal converts input text data and voice data into an appropriate format and sends it to the server.

[0587] 4. Generative AI Models

[0588] This is a specialized module for recognizing emotions from user input data and generating empathetic responses. It uses generative AI models (such as GPT-3, which utilizes natural language processing technology) to create empathetic responses that are appropriate for the user's emotional situation.

[0589] 5. Response Generation Methods

[0590] This module generates an empathetic response based on the emotions determined by the emotion analysis means. The generated response might be something like, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0591] 6. Means of providing a response

[0592] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[0593] 7. Specialist Referral Methods

[0594] This module introduces the user to an appropriate expert (e.g., a counselor) when the emotion determined by the emotion analysis means meets certain conditions. The server maintains a list of appropriate experts and generates an introduction message for the user depending on the conditions. For example, it generates a suggestion message such as "Consider talking to a professional counselor about your current condition," and sends this to the terminal.

[0595] Specific examples

[0596] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." The server then uses the generative AI model to generate a response such as "It seems like you're feeling very stressed at work. It's important to take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user.

[0597] Furthermore, the server generates a suggestion message using an expert introduction means, saying "Consider talking to a professional counselor about your current condition," which is also sent to the terminal. The user can then contact the suggested counselor.

[0598] Prompt Sentence Examples

[0599] "Generate an empathetic response about how the user has been experiencing a lot of stress at work lately."

[0600] "This user's emotion has been determined to be 'stressed'. Please generate a counselor introduction based on this."

[0601] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[0602] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0603] Step 1:

[0604] User emotion input

[0605] Users use the device to input their emotions and experiences, for example, through a web form, a chat box in a mobile application, a voice input interface, or a camera to input emotional data.

[0606] Input: User emotions and experiences (text, voice, images)

[0607] Output: Temporarily storing and preparing data by the device

[0608] Specific operation example:

[0609] A user types into a chat box on a mobile app, "I've been feeling very stressed at work lately and am very tired."

[0610] Step 2:

[0611] Sending text data

[0612] The device sends the user's input data to the server, and in the process, the device converts the data into the appropriate format depending on the input format (text, audio, image).

[0613] Input: User-entered text, voice, and image data

[0614] Output: Formatted data is sent to the server

[0615] Specific operation example:

[0616] The device sends text data such as "I've been under a lot of stress at work lately and I'm very tired" to the server.

[0617] Step 3:

[0618] Emotion analysis

[0619] The server analyzes the received data using emotion analysis techniques, such as text analysis, voice analysis, and facial expression analysis, to determine the user's emotions.

[0620] Input: User data sent to the server

[0621] Output: Annotated emotion label (e.g. "stressed")

[0622] Specific operation example:

[0623] The server analyzes the text "I've been stressed at work lately and I'm very tired" and determines that it is "stressed."

[0624] Step 4:

[0625] Generating an empathic response

[0626] The server uses a generative AI model to generate an empathetic response based on the emotion determined by the emotion analysis means.

[0627] Input: Annotated emotion labels, user input data

[0628] Output: A generated empathetic response (e.g., "It sounds like you're under a lot of stress at work. It's important that you take some time off for yourself.")

[0629] Specific operation example:

[0630] The generative AI model generates the response, "It seems like you're under a lot of stress at work. It's important to take some time for yourself to relax."

[0631] Step 5:

[0632] Providing a response

[0633] The generated response is sent to the terminal through the response providing means and displayed to the user, and the terminal provides an interface for displaying the response in an easy-to-view manner to the user.

[0634] Input: Generated empathic response

[0635] Output: Response displayed to the user

[0636] Specific operation example:

[0637] The device displays the generated response on the chat screen, and the user sees the response: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0638] Step 6:

[0639] Generating and providing expert referrals

[0640] Based on the results of the sentiment analysis, the server generates a message introducing a specialist (such as a counselor) if the user meets certain conditions and provides this to the user.

[0641] Input: Sentiment analysis results, list of experts

[0642] Output: Expert introduction message

[0643] Specific operation example:

[0644] The server generates an introductory message for users who are judged to be "stressed," saying, "Consider talking to a professional counselor about your current condition." The server then sends this message to the user's device. The device then displays this message to the user.

[0645] Thus, the present invention provides a system that allows users to have their emotions accurately analyzed and receive empathetic and professional responses.

[0646] (Application example 2)

[0647] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] In modern food delivery services, users often experience stress and frustration during the ordering process. These emotions can negatively impact service ratings and user experience, ultimately leading to lower customer satisfaction. However, traditional delivery services lack a mechanism for analyzing user emotions in real time and providing appropriate feedback and support. To solve this problem, it is necessary to develop a system that can accurately grasp user emotions and provide necessary support and empathetic feedback.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an interface means through which the user inputs emotions and experiences, an analysis means that receives data from the interface means and analyzes the user's emotions, a generation means that generates an empathetic response based on the emotions analyzed by the analysis means, a provision means that provides the generated response to the user, a referral means that introduces an expert as needed based on the analysis means, and a notification means that displays the generated response and referral message to the user via the response provision means. This makes it possible to analyze the user's emotions in real time and provide appropriate feedback and support.

[0650] "Interface means" refers to input devices and software that allow users to input emotions and experiences.

[0651] The "analysis means" is a function for analyzing data received from the interface means and determining the user's emotions.

[0652] The "generation means" is a function for generating an empathetic response based on the analyzed emotions.

[0653] The "means for providing" is a function for transmitting the generated response to the user.

[0654] "Introduction method" is a function that introduces users to appropriate experts based on the analysis results.

[0655] The "notification means" is a function for displaying to the user the response or introduction message generated through the response providing means.

[0656] The present invention is a system that allows users to share their feelings and experiences, provides empathetic responses based on those feelings and experiences, and refers users to experts as needed. The system mainly includes the following main components: an interface means, an analysis means, a generation means, a provision means, a referral means, and a notification means.

[0657] System configuration

[0658] The system operates as follows:

[0659] 1. Interface Method

[0660] Users can input their emotions and experiences through interface means, which are implemented through smartphone applications and include voice input, text input, or facial recognition via a camera.

[0661] 2. Analysis tools

[0662] The data received from the interface means is sent to a server, where emotion analysis is performed by the analysis means. This analysis uses an emotion recognition model, such as an emotion analysis algorithm by GPT-3 or TensorFlow, to determine whether the user's input is "stressed" or "neutral," etc.

[0663] 3. Generation means

[0664] The server generates an empathetic response based on the emotional information analyzed by the analytical means. For example, in response to an input such as "I've been stressed at work lately and I'm very tired," the generative AI model creates a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break."

[0665] 4. Means of provision

[0666] The generated response is transmitted to the user via a means of providing the response, which is also implemented on a smartphone application and displayed to the user as a text message or a voice message.

[0667] 5. Referral methods

[0668] If necessary, the system will refer the user to a specialist. For example, if the analysis results in a "stressed" condition, the system will generate a message such as "Consider talking to a professional counselor."

[0669] 6. Means of notification

[0670] To always provide timely feedback to the user, a notification means is used, which has the function of displaying the generated response or introduction message on the user's terminal.

[0671] Usage example

[0672] When a user types, "I've been stressed at work lately and I'm very tired," the data is sent to the server through the smartphone's input interface. The server receives this data, performs emotional analysis, and determines the result as "stressed." It then generates an empathetic response, "It seems you're feeling stressed while ordering. I recommend you take a short break," and provides this to the user. If necessary, an introductory message is also displayed, saying, "Consider talking to a professional counselor."

[0673] Prompt Sentence Examples

[0674] "Based on the text entered by the user, analyze their sentiment and generate empathetic feedback. Also, generate a message to connect them with the right expert."

[0675] Thus, by following the above components and processing steps, a system can be realized that allows users to safely share their feelings and provide empathetic and professional feedback.

[0676] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0677] Step 1:

[0678] The device accepts input of emotions and experiences from the user. This input can be text input, voice input, or facial expression recognition. For example, the input text "I've been stressed at work lately and I'm very tired" is acquired by the device.

[0679] input:

[0680] User text input, voice input, or facial expression data.

[0681] output:

[0682] Emotional and experience data sent to the server.

[0683] Step 2:

[0684] The device sends the acquired emotion and experience data to a server, which then analyzes the received data using analytical means. For example, an emotion recognition algorithm using GPT-3 or TensorFlow is used for the analysis.

[0685] input:

[0686] Emotional and experience data sent from the device.

[0687] output:

[0688] The results of sentiment analysis.

[0689] Step 3:

[0690] The server analyzes the received data and determines the emotion. The analyzed emotion is determined as "stressed" or "neutral," etc. The specific analysis process involves using GPT-3 to analyze text data and assign emotion labels. Voice data and facial expression data are also analyzed in the same way.

[0691] input:

[0692] Emotional and experiential data.

[0693] output:

[0694] The determined emotion label (e.g., "stressed").

[0695] Step 4:

[0696] The server generates an empathetic response using a generative means based on the determined emotion label. A generative AI model (e.g., GPT-3) is used to create an appropriate empathetic response. Specifically, for input such as "I've been stressed at work lately and I'm very tired," a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break" is generated.

[0697] input:

[0698] An emotion label (e.g., "stressed").

[0699] output:

[0700] Generated empathic response messages.

[0701] Step 5:

[0702] The server provides the generated empathetic response to the terminal through the providing means, and the terminal communicates the generated response to the user by displaying or audibly displaying the response. Specifically, the response is displayed as a text message on the application.

[0703] input:

[0704] Generated empathic response messages.

[0705] output:

[0706] The response message is displayed on the terminal.

[0707] Step 6:

[0708] The server will refer the user to a specialist if necessary. For example, if the emotion label is determined to be "stressed," the server will use the referral mechanism to generate a referral message such as "Consider speaking to a professional counselor."

[0709] input:

[0710] An emotion label (e.g., "stressed").

[0711] output:

[0712] An expert introduction message is generated.

[0713] Step 7:

[0714] The server provides the generated introduction message to the terminal via a notification means, and the terminal displays the introduction message to the user. Specifically, the introduction message is displayed as an expert introduction message on the application.

[0715] input:

[0716] Generated expert introduction message.

[0717] output:

[0718] An introductory message will be displayed on your device.

[0719] Through these steps, we will create a system that can analyze users' emotions in real time and provide appropriate feedback and professional support.

[0720] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0721] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0722] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0723] [Third embodiment]

[0724] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0725] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0726] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0727] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0728] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0729] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0730] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0731] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0732] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0733] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0734] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0735] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0736] The present invention provides a system that allows users to safely share their feelings, such as deep sadness or stress, and provides empathetic and professional feedback. The system analyzes the user's input, generates a response based on the emotion, and, if necessary, refers the user to a specialist.

[0737] System configuration

[0738] The system includes the following main components:

[0739] 1. Input method:

[0740] The device provides an interface for users to input their feelings and experiences in text form. The device provides this input means to the user, such as a web form or a chat box in a mobile application.

[0741] 2. Emotion analysis means:

[0742] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0743] 3. Response Generation Method:

[0744] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[0745] 4. Means of providing response:

[0746] This is an interface for providing the generated response to the user. The terminal uses this means to display the response to the user. Specifically, the response message is displayed on the chat screen.

[0747] 5. Specialist referral methods:

[0748] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[0749] Program processing (natural language explanation)

[0750] When the server receives text data from the device, it first analyzes the emotions of the input text data using emotion analysis means. If the analysis results in the user being "stressed," it passes this result to the next step.

[0751] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0752] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0753] Specific examples

[0754] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0755] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[0756] The processing flow will be explained below.

[0757] Step 1:

[0758] The user inputs their feelings and experiences. For example, they might write, "I've been feeling very stressed at work lately, and I'm very tired," into an input form.

[0759] Step 2:

[0760] The terminal sends the text data entered in step 1 to the server. At this time, the text data is sent to the server as an HTTP POST request.

[0761] Step 3:

[0762] The server receives the text data sent from the device and stores it in JSON format.

[0763] Step 4:

[0764] The server uses emotion analysis means to analyze the emotion of the received text data. For example, it analyzes "I'm stressed at work" and determines the user's emotion as "stressed."

[0765] Step 5:

[0766] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0767] Step 6:

[0768] The server transmits the generated response to the terminal based on the response providing means, and at this time, the response message is transmitted to the terminal in JSON format.

[0769] Step 7:

[0770] The device analyzes the response message received from the server and displays it to the user. The user sees the message displayed on the device screen: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0771] Step 8:

[0772] The server further uses the expert referral means based on the sentiment analysis results to generate a message to introduce an appropriate expert as needed. For example, a suggestion message such as "Consider talking to a professional counselor about your current condition" may be generated.

[0773] Step 9:

[0774] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[0775] Step 10:

[0776] The device analyzes the expert introduction message received from the server and displays it to the user. The user confirms the message "Consider talking to a professional counselor about your current condition" displayed on the device screen.

[0777] Example 1

[0778] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0779] In modern society, users often experience deep emotions such as sadness and stress, and they need a safe place to share their feelings. However, there is no system that provides empathetic and professional feedback on these emotions, which means users cannot receive appropriate support.

[0780] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0781] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a means for the emotion analysis means to generate a message for introducing an expert when specific conditions are met based on the analysis result, and a means for the response generation means to generate a response using a generative AI model. This enables users to safely share their emotions, receive empathetic responses, and also obtain support from experts as needed.

[0782] "User" refers to an individual who uses the system to input their feelings and experiences and receive responses and support from experts.

[0783] "Input means" refers to a means that provides an interface for users to input their feelings or experiences in text format. Examples include web forms and chat boxes in mobile applications.

[0784] The "emotion analysis means" is a means by which the server receives text data from the input means and analyzes the user's emotions using natural language processing technology.

[0785] The "response generation means" is a module for generating empathetic responses based on the emotions determined by the emotion analysis means. Specifically, it refers to creating responses using a generative AI model.

[0786] The "response providing means" refers to an interface for providing the generated empathetic response to the user. The terminal plays a role in displaying the response to the user.

[0787] The "expert introduction means" is a module that introduces an appropriate expert to the user when the emotion determined by the emotion analysis means satisfies a specific condition.

[0788] "Generative AI model" refers to artificial intelligence technology for generating responses through natural language processing, for example, using a Transformer model (such as GPT-3).

[0789] A "prompt" is a document provided as input to a generative AI model that contains instructions for the AI ​​to generate a response based on that input.

[0790] An embodiment of the present invention is a system for users to safely share their feelings and receive empathetic and professional feedback. The system performs sentiment analysis on user input, generates responses based on the sentiment analysis, and provides referrals to experts as needed.

[0791] System configuration

[0792] The system includes the following main components:

[0793] 1. Input Method

[0794] An interface that allows users to input their feelings and experiences in text format. The device provides the user with a means for this input, and examples include web forms and chat boxes in mobile applications.

[0795] 2. Emotion analysis method

[0796] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0797] 3. Response Generation Method

[0798] This module generates empathetic responses based on the emotions determined by the emotion analysis means. For example, the server uses a Transformer model (such as GPT-3) to create empathetic responses appropriate to the user's emotional state.

[0799] 4. Means of providing a response

[0800] This is an interface for providing the generated response to the user. The terminal uses this means to display the response on the user's screen. Specifically, the response message is displayed on the chat screen.

[0801] 5. Specialist referral channels

[0802] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user according to the conditions.

[0803] System Operation

[0804] When the server receives text data from the terminal, it first analyzes the emotion of the input text data using emotion analysis means. If the analysis result indicates that the user is "stressed," it passes this result to the next step.

[0805] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0806] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0807] Specific examples

[0808] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0809] Example prompts to input to the generative AI model

[0810] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[0811] User Input: I've been under a lot of stress at work lately and I'm really tired.

[0812] Response Generation Prompt: Generate an empathetic message for the user based on the following: "Emotion Label: stressed"

[0813] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0814] Step 1:

[0815] The user inputs their emotions and experiences in text format. Specifically, the user uses a device to enter text such as "I've been feeling very stressed at work lately, and I'm very tired" into a chat box or web form and presses the send button. This text data is sent from the device to the server as input. The input data is passed to the server in, for example, JSON format.

[0816] Step 2:

[0817] The server uses sentiment analysis to analyze the text data received from the device. Specifically, the server uses natural language processing technology (e.g., the NLTK library or SpaCy) to analyze the text content. Here, the input data is text information, and the server analyzes this text to assign an sentiment label, such as "stressed." The sentiment label obtained as a result of the analysis is "stressed," which is passed on as output to the next step.

[0818] Step 3:

[0819] The server generates an empathetic response using a response generation method based on the results of the emotion analysis. Specifically, the server inputs a prompt sentence into a generative AI model (e.g., GPT-3). The prompt sentence is "emotion label: stressed," and the AIS model generates a response based on the prompt sentence: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed on as output to the next step.

[0820] Step 4:

[0821] The server sends the generated empathetic response to the terminal. Specifically, the server uses the response providing means to send the generated response in JSON format to the terminal. The terminal displays the received response on the user's screen. For example, the output may be a message on the chat screen saying, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0822] Step 5:

[0823] If the result of the emotion analysis is determined to be "stressed," the server uses the expert referral means to generate a message introducing an appropriate counselor. Specifically, the server uses the expert referral means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition." This message is also sent to the device in JSON format. The device displays the received suggestion message on the user's screen, allowing the user to contact the suggested counselor.

[0824] (Application example 1)

[0825] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0826] In modern society, mental health issues are becoming more serious in the workplace, schools, and other settings. However, many users lack a place to appropriately share their emotions and stress, and often lack support for self-improvement. Furthermore, the lack of an environment for expert analysis of emotions, immediate empathetic feedback, and rapid referral to specialists prevents efficient mental health care.

[0827] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0828] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a recommendation means for recommending an expert when the emotions analyzed by the emotion analysis means satisfy certain conditions, and a display means for displaying the generated response and the recommended expert to the user. This enables users to safely share their emotions, receive empathetic feedback, and obtain expert support as needed.

[0829] "Input means" refers to an interface that allows users to input emotions and experiences in text format. Examples include the input screen of a smartphone app or a web form.

[0830] The "emotion analysis means" is a module that analyzes text data from the input means and determines the user's emotions. It uses natural language processing technology to assign emotion labels based on the input data.

[0831] The "response generation means" is a module that generates an empathetic response based on the emotions analyzed by the emotion analysis means. For example, it generates appropriate feedback using a generative AI model.

[0832] The "response providing means" refers to an interface for displaying the generated response to the user. Specifically, this corresponds to the chat screen of a smartphone app.

[0833] The "expert introduction means" is a module that introduces appropriate experts to users as needed based on the sentiment analysis means. It recommends appropriate people from a list of experts according to the analysis results.

[0834] The "recommendation means" is a module that recommends experts when the emotions analyzed by the emotion analysis means meet certain conditions. It selects and recommends appropriate experts to support the user's mental health.

[0835] The "display means" refers to an interface for displaying the generated response and the recommended expert information to the user. Specifically, this corresponds to the display screen of a smartphone or the like.

[0836] The present invention provides a system that allows users to safely share their feelings and experiences and provides empathetic and expert feedback. The system analyzes the user's input, generates a response based on the emotion analysis, and introduces an expert if necessary. Each processing step is performed based on the roles of the server, the terminal, and the user.

[0837] System configuration

[0838] Input Method

[0839] The device provides an interface for users to input their feelings and experiences in text form, such as a smartphone app or a web form.

[0840] Emotion analysis means

[0841] The server analyzes the text data received from the device using emotion analysis. This analysis uses natural language processing technology. Using Hugging Face's Transformers library, emotion labels can be assigned based on the input data. For example, an input such as "I'm very stressed at work and very tired" would be judged as "NEGATIVE."

[0842] Response Generation Method

[0843] Based on the emotion label obtained by the emotion analysis means, the server generates an empathetic response. For example, if the emotion label is "NEGATIVE," a response such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself" is generated. A generative AI model (e.g., GPT-3) is used to generate this response.

[0844] Response delivery method

[0845] The generated response is displayed to the user through a response providing means, specifically, a chat screen on a smartphone app.

[0846] Expert referral methods

[0847] If certain conditions are met as a result of the sentiment analysis, the server will use the expert referral mechanism to recommend an appropriate expert to the user. Based on the analysis results, an appropriate person will be selected from the list of experts and introduced to the user. For example, a message such as "Consider speaking with a professional counselor for your current situation. Recommended expert: Yamada Taro - contact@example.com" will be generated.

[0848] Recommendation and display methods

[0849] The server includes a recommendation unit that recommends an expert when the user's emotion meets a specific condition. The generated response and information about the recommended expert are presented to the user through a display unit, such as a smartphone display screen.

[0850] Specific examples

[0851] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server performs emotion analysis on this text data and determines it to be "NEGATIVE." The server then uses the response generation means to generate an empathetic response such as "It seems like you're feeling very stressed at work. It's important that you take some time to rest for yourself." This response is then displayed to the user via the response providing means.

[0852] Furthermore, the server uses the expert introduction means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com," which is also sent to the terminal. The terminal displays this message, and the user can contact the suggested counselor.

[0853] Prompt Sentence Examples

[0854] TXT

[0855] "I've been stressed out at work lately and I'm really tired."

[0856] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[0857] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0858] Step 1:

[0859] Users input their emotions and experiences in text format. Specifically, they use the input screen of a smartphone app or a web form to freely enter the stress and worries they are feeling. This text data is then passed to the next processing step.

[0860] Step 2:

[0861] The device receives text data entered by the user and sends it to the server. At this time, the data is transmitted to the server via an endpoint (API). The server then receives the user's input data and prepares it for analysis.

[0862] Step 3:

[0863] The server analyzes the received text data using a sentiment analysis tool. Natural language processing techniques are used to determine the user's sentiment from the text data. The specific software used is the Hugging Face Transformers library. Based on the input data, an emotional label (e.g., "NEGATIVE") is assigned.

[0864] Step 4:

[0865] The server generates an empathetic response using the response generation means based on the emotion label obtained by the emotion analysis means. In this process, a generative AI model (e.g., GPT-3) is used to generate a natural-spoken response that matches the emotion label. The emotion label is used as input, and an appropriate response sentence is generated as output.

[0866] Step 5:

[0867] The generated response is sent from the server to the terminal through the response providing means, where the response is converted into a format that can be displayed to the user. The user can check the generated response on the chat screen of the terminal.

[0868] Step 6:

[0869] Based on the results of the emotion analysis, the server uses expert referral methods to recommend appropriate experts as needed. If the emotion label meets certain conditions (e.g., "high stress level"), the server selects an appropriate counselor or mental health professional from a list of experts, allowing the user to receive appropriate support.

[0870] Step 7:

[0871] The server also transmits the expert recommendation information generated through the recommendation means to the terminal. The terminal presents this to the user using the display means. For example, a message such as "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com" is displayed. The user can take the next action, such as counseling, based on this information.

[0872] These steps allow users to receive appropriate feedback based on their emotions and access professional support if needed.

[0873] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0874] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[0875] System configuration

[0876] The system includes the following main components:

[0877] 1. Input method:

[0878] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides this input means to users, such as web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[0879] 2. Emotion analysis means:

[0880] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0881] 3. Emotion Engine:

[0882] This is a specialized module for recognizing emotions from user input data. When there is voice input, it uses the voice recognition module, and when there is image input, it uses the facial expression recognition module to accurately grasp the user's emotional state.

[0883] 4. Response Generation Method:

[0884] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[0885] 5. Means of providing response:

[0886] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[0887] 6. Specialist Referral Methods:

[0888] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[0889] Program processing (natural language explanation)

[0890] When the server receives text, voice, or facial expression data from the device, it first uses emotion analysis to analyze the emotion of the input data. For example, if a user inputs text like "I've been stressed at work lately, and I'm very tired," it might determine the emotion as "stressed." It can then more accurately identify the user's emotional state through facial expression analysis of voice data and facial images.

[0891] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[0892] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[0893] Specific examples

[0894] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[0895] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[0896] The processing flow will be explained below.

[0897] Step 1:

[0898] The user inputs their emotions and experiences, for example, by typing "I've been feeling very stressed at work lately, and I'm very tired" into a text field, or by using a voice input interface to say, "I've been feeling very stressed at work lately, and I'm very tired." Alternatively, facial expression data can be captured by pointing their face through the camera.

[0899] Step 2:

[0900] The terminal sends the text data, voice data, and facial expression data entered in step 1 to the server. The text data and voice data are sent as an HTTP POST request. Similarly, the facial expression data is sent as an image.

[0901] Step 3:

[0902] The server receives the data sent from the device and stores it in JSON or image format.

[0903] Step 4:

[0904] The server uses an emotion analysis means and emotion engine to analyze the emotions of the received text, voice, and facial expression data. For example, it analyzes the text data "I'm stressed at work" and determines the user's emotion as "stressed." In the case of voice data, it converts the voice into text through a voice recognition module and analyzes emotions based on that text. In the case of facial expression data, it uses a facial expression recognition module to determine emotions from changes in facial expressions.

[0905] Step 5:

[0906] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0907] Step 6:

[0908] The server transmits the generated response to the terminal based on the response providing means, and the response message is transmitted to the terminal in JSON format.

[0909] Step 7:

[0910] The device analyzes the response message received from the server and conveys it to the user by display or voice. The user sees the message on the device screen or hears the message, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0911] Step 8:

[0912] The server further uses the expert referral means based on the sentiment analysis result to generate a message to introduce an appropriate expert as needed, for example, a suggestion message such as "Consider talking to a professional counselor about your current condition."

[0913] Step 9:

[0914] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[0915] Step 10:

[0916] The device analyzes the expert introduction message received from the server and displays it to the user. The user sees the message "Consider speaking to a professional counselor about your current condition" on the device screen and can contact an expert if necessary.

[0917] Example 2

[0918] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0919] In modern society, users need a safe way to share their deep emotions, such as sadness or stress, and receive expert, empathetic feedback. However, existing systems often struggle to accurately analyze emotions and generate appropriate responses. They also lack the ability to quickly and appropriately refer users to experts. This can leave users frustrated and anxious, potentially exacerbating the problem.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0921] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response provision means for providing the generated response to the user, an expert referral means for introducing an expert as needed based on the emotion analysis means, a data transmission means for appropriately formatting data transmitted from the input means, and a generative AI model for generating an expert response based on the emotion label determined by the emotion analysis means. This allows the user to have their emotions accurately analyzed, receive an empathetic and expert response, and be referred to an appropriate expert as needed.

[0922] "Input means" refers to a means that provides an interface for a user to input emotions and experiences as text, voice, or facial expression data.

[0923] The "emotion analysis means" is a module that analyzes text, voice, or facial expression data received from the terminal and determines the user's emotions.

[0924] The "response generation means" includes a module that generates an empathetic response based on the emotion determined by the emotion analysis means.

[0925] The "response providing means" is an interface for providing the generated response to the user.

[0926] The "expert introduction means" is a module that introduces an appropriate expert (such as a counselor) to the user when the emotion determined by the emotion analysis means meets certain conditions.

[0927] The "data transmission means" has the function of appropriately formatting data transmitted from the input means and transmitting the formatted data to the server.

[0928] A "generative AI model" is an artificial intelligence model that generates specialized responses based on emotion labels determined by emotion analysis means, and utilizes natural language processing technology.

[0929] MODE FOR CARRYING OUT THE INVENTION

[0930] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[0931] System configuration

[0932] The system includes the following main components:

[0933] 1. Input Method

[0934] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides users with this input method, which may include web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[0935] 2. Emotion analysis method

[0936] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[0937] 3. Data Transmission Method

[0938] The terminal has the function of appropriately formatting data sent from the input means and sending it to the server. Specifically, the terminal converts input text data and voice data into an appropriate format and sends it to the server.

[0939] 4. Generative AI Models

[0940] This is a specialized module for recognizing emotions from user input data and generating empathetic responses. It uses generative AI models (such as GPT-3, which utilizes natural language processing technology) to create empathetic responses that are appropriate for the user's emotional situation.

[0941] 5. Response Generation Methods

[0942] This module generates an empathetic response based on the emotions determined by the emotion analysis means. The generated response might be something like, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0943] 6. Means of providing a response

[0944] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[0945] 7. Specialist Referral Methods

[0946] This module introduces the user to an appropriate expert (e.g., a counselor) when the emotion determined by the emotion analysis means meets certain conditions. The server maintains a list of appropriate experts and generates an introduction message for the user depending on the conditions. For example, it generates a suggestion message such as "Consider talking to a professional counselor about your current condition," and sends this to the terminal.

[0947] Specific examples

[0948] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." The server then uses the generative AI model to generate a response such as "It seems like you're feeling very stressed at work. It's important to take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user.

[0949] Furthermore, the server generates a suggestion message using an expert introduction means, saying "Consider talking to a professional counselor about your current condition," which is also sent to the terminal. The user can then contact the suggested counselor.

[0950] Prompt Sentence Examples

[0951] "Generate an empathetic response about how the user has been experiencing a lot of stress at work lately."

[0952] "This user's emotion has been determined to be 'stressed'. Please generate a counselor introduction based on this."

[0953] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[0954] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0955] Step 1:

[0956] User emotion input

[0957] Users use the device to input their emotions and experiences, for example, through a web form, a chat box in a mobile application, a voice input interface, or a camera to input emotional data.

[0958] Input: User emotions and experiences (text, voice, images)

[0959] Output: Temporarily storing and preparing data by the device

[0960] Specific operation example:

[0961] A user types into a chat box on a mobile app, "I've been feeling very stressed at work lately and am very tired."

[0962] Step 2:

[0963] Sending text data

[0964] The device sends the user's input data to the server, and in the process, the device converts the data into the appropriate format depending on the input format (text, audio, image).

[0965] Input: User-entered text, voice, and image data

[0966] Output: Formatted data is sent to the server

[0967] Specific operation example:

[0968] The device sends text data such as "I've been under a lot of stress at work lately and I'm very tired" to the server.

[0969] Step 3:

[0970] Emotion analysis

[0971] The server analyzes the received data using emotion analysis techniques, such as text analysis, voice analysis, and facial expression analysis, to determine the user's emotions.

[0972] Input: User data sent to the server

[0973] Output: Annotated emotion label (e.g. "stressed")

[0974] Specific operation example:

[0975] The server analyzes the text "I've been stressed at work lately and I'm very tired" and determines that it is "stressed."

[0976] Step 4:

[0977] Generating an empathic response

[0978] The server uses a generative AI model to generate an empathetic response based on the emotion determined by the emotion analysis means.

[0979] Input: Annotated emotion labels, user input data

[0980] Output: A generated empathetic response (e.g., "It sounds like you're under a lot of stress at work. It's important that you take some time off for yourself.")

[0981] Specific operation example:

[0982] The generative AI model generates the response, "It seems like you're under a lot of stress at work. It's important to take some time for yourself to relax."

[0983] Step 5:

[0984] Providing a response

[0985] The generated response is sent to the terminal through the response providing means and displayed to the user, and the terminal provides an interface for displaying the response in an easy-to-view manner to the user.

[0986] Input: Generated empathic response

[0987] Output: Response displayed to the user

[0988] Specific operation example:

[0989] The device displays the generated response on the chat screen, and the user sees the response: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[0990] Step 6:

[0991] Generating and providing expert referrals

[0992] Based on the results of the sentiment analysis, the server generates a message introducing a specialist (such as a counselor) if the user meets certain conditions and provides this to the user.

[0993] Input: Sentiment analysis results, list of experts

[0994] Output: Expert introduction message

[0995] Specific operation example:

[0996] The server generates an introductory message for users who are judged to be "stressed," saying, "Consider talking to a professional counselor about your current condition." The server then sends this message to the user's device. The device then displays this message to the user.

[0997] Thus, the present invention provides a system that allows users to have their emotions accurately analyzed and receive empathetic and professional responses.

[0998] (Application example 2)

[0999] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] In modern food delivery services, users often experience stress and frustration during the ordering process. These emotions can negatively impact service ratings and user experience, ultimately leading to lower customer satisfaction. However, traditional delivery services lack a mechanism for analyzing user emotions in real time and providing appropriate feedback and support. To solve this problem, it is necessary to develop a system that can accurately grasp user emotions and provide necessary support and empathetic feedback.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an interface means through which the user inputs emotions and experiences, an analysis means that receives data from the interface means and analyzes the user's emotions, a generation means that generates an empathetic response based on the emotions analyzed by the analysis means, a provision means that provides the generated response to the user, a referral means that introduces an expert as needed based on the analysis means, and a notification means that displays the generated response and referral message to the user via the response provision means. This makes it possible to analyze the user's emotions in real time and provide appropriate feedback and support.

[1002] "Interface means" refers to input devices and software that allow users to input emotions and experiences.

[1003] The "analysis means" is a function for analyzing data received from the interface means and determining the user's emotions.

[1004] The "generation means" is a function for generating an empathetic response based on the analyzed emotions.

[1005] The "means for providing" is a function for transmitting the generated response to the user.

[1006] "Introduction method" is a function that introduces users to appropriate experts based on the analysis results.

[1007] The "notification means" is a function for displaying to the user the response or introduction message generated through the response providing means.

[1008] The present invention is a system that allows users to share their feelings and experiences, provides empathetic responses based on those feelings and experiences, and refers users to experts as needed. The system mainly includes the following main components: an interface means, an analysis means, a generation means, a provision means, a referral means, and a notification means.

[1009] System configuration

[1010] The system operates as follows:

[1011] 1. Interface Method

[1012] Users can input their emotions and experiences through interface means, which are implemented through smartphone applications and include voice input, text input, or facial recognition via a camera.

[1013] 2. Analysis tools

[1014] The data received from the interface means is sent to a server, where emotion analysis is performed by the analysis means. This analysis uses an emotion recognition model, such as an emotion analysis algorithm by GPT-3 or TensorFlow, to determine whether the user's input is "stressed" or "neutral," etc.

[1015] 3. Generation means

[1016] The server generates an empathetic response based on the emotional information analyzed by the analytical means. For example, in response to an input such as "I've been stressed at work lately and I'm very tired," the generative AI model creates a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break."

[1017] 4. Means of provision

[1018] The generated response is transmitted to the user via a means of providing the response, which is also implemented on a smartphone application and displayed to the user as a text message or a voice message.

[1019] 5. Referral methods

[1020] If necessary, the system will refer the user to a specialist. For example, if the analysis results in a "stressed" condition, the system will generate a message such as "Consider talking to a professional counselor."

[1021] 6. Means of notification

[1022] To always provide timely feedback to the user, a notification means is used, which has the function of displaying the generated response or introduction message on the user's terminal.

[1023] Usage example

[1024] When a user types, "I've been stressed at work lately and I'm very tired," the data is sent to the server through the smartphone's input interface. The server receives this data, performs emotional analysis, and determines the result as "stressed." It then generates an empathetic response, "It seems you're feeling stressed while ordering. I recommend you take a short break," and provides this to the user. If necessary, an introductory message is also displayed, saying, "Consider talking to a professional counselor."

[1025] Prompt Sentence Examples

[1026] "Based on the text entered by the user, analyze their sentiment and generate empathetic feedback. Also, generate a message to connect them with the right expert."

[1027] Thus, by following the above components and processing steps, a system can be realized that allows users to safely share their feelings and provide empathetic and professional feedback.

[1028] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1029] Step 1:

[1030] The device accepts input of emotions and experiences from the user. This input can be text input, voice input, or facial expression recognition. For example, the input text "I've been stressed at work lately and I'm very tired" is acquired by the device.

[1031] input:

[1032] User text input, voice input, or facial expression data.

[1033] output:

[1034] Emotional and experience data sent to the server.

[1035] Step 2:

[1036] The device sends the acquired emotion and experience data to a server, which then analyzes the received data using analytical means. For example, an emotion recognition algorithm using GPT-3 or TensorFlow is used for the analysis.

[1037] input:

[1038] Emotional and experience data sent from the device.

[1039] output:

[1040] The results of sentiment analysis.

[1041] Step 3:

[1042] The server analyzes the received data and determines the emotion. The analyzed emotion is determined as "stressed" or "neutral," etc. The specific analysis process involves using GPT-3 to analyze text data and assign emotion labels. Voice data and facial expression data are also analyzed in the same way.

[1043] input:

[1044] Emotional and experiential data.

[1045] output:

[1046] The determined emotion label (e.g., "stressed").

[1047] Step 4:

[1048] The server generates an empathetic response using a generative means based on the determined emotion label. A generative AI model (e.g., GPT-3) is used to create an appropriate empathetic response. Specifically, for input such as "I've been stressed at work lately and I'm very tired," a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break" is generated.

[1049] input:

[1050] An emotion label (e.g., "stressed").

[1051] output:

[1052] Generated empathic response messages.

[1053] Step 5:

[1054] The server provides the generated empathetic response to the terminal through the providing means, and the terminal communicates the generated response to the user by displaying or audibly displaying the response. Specifically, the response is displayed as a text message on the application.

[1055] input:

[1056] Generated empathic response messages.

[1057] output:

[1058] The response message is displayed on the terminal.

[1059] Step 6:

[1060] The server will refer the user to a specialist if necessary. For example, if the emotion label is determined to be "stressed," the server will use the referral mechanism to generate a referral message such as "Consider speaking to a professional counselor."

[1061] input:

[1062] An emotion label (e.g., "stressed").

[1063] output:

[1064] An expert introduction message is generated.

[1065] Step 7:

[1066] The server provides the generated introduction message to the terminal via a notification means, and the terminal displays the introduction message to the user. Specifically, the introduction message is displayed as an expert introduction message on the application.

[1067] input:

[1068] Generated expert introduction message.

[1069] output:

[1070] An introductory message will be displayed on your device.

[1071] Through these steps, we will create a system that can analyze users' emotions in real time and provide appropriate feedback and professional support.

[1072] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1074] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1075] [Fourth embodiment]

[1076] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1077] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1078] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1079] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1080] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1081] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1082] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1083] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1084] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1085] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1087] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1088] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1089] The present invention provides a system that allows users to safely share their feelings, such as deep sadness or stress, and provides empathetic and professional feedback. The system analyzes the user's input, generates a response based on the emotion, and, if necessary, refers the user to a specialist.

[1090] System configuration

[1091] The system includes the following main components:

[1092] 1. Input method:

[1093] The device provides an interface for users to input their feelings and experiences in text form. The device provides this input means to the user, such as a web form or a chat box in a mobile application.

[1094] 2. Emotion analysis means:

[1095] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[1096] 3. Response Generation Method:

[1097] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[1098] 4. Means of providing response:

[1099] This is an interface for providing the generated response to the user. The terminal uses this means to display the response to the user. Specifically, the response message is displayed on the chat screen.

[1100] 5. Specialist referral methods:

[1101] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[1102] Program processing (natural language explanation)

[1103] When the server receives text data from the device, it first analyzes the emotions of the input text data using emotion analysis means. If the analysis results in the user being "stressed," it passes this result to the next step.

[1104] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[1105] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[1106] Specific examples

[1107] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[1108] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[1109] The processing flow will be explained below.

[1110] Step 1:

[1111] The user inputs their feelings and experiences. For example, they might write, "I've been feeling very stressed at work lately, and I'm very tired," into an input form.

[1112] Step 2:

[1113] The terminal sends the text data entered in step 1 to the server. At this time, the text data is sent to the server as an HTTP POST request.

[1114] Step 3:

[1115] The server receives the text data sent from the device and stores it in JSON format.

[1116] Step 4:

[1117] The server uses emotion analysis means to analyze the emotion of the received text data. For example, it analyzes "I'm stressed at work" and determines the user's emotion as "stressed."

[1118] Step 5:

[1119] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[1120] Step 6:

[1121] The server transmits the generated response to the terminal based on the response providing means, and at this time, the response message is transmitted to the terminal in JSON format.

[1122] Step 7:

[1123] The device analyzes the response message received from the server and displays it to the user. The user sees the message displayed on the device screen: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[1124] Step 8:

[1125] The server further uses the expert referral means based on the sentiment analysis results to generate a message to introduce an appropriate expert as needed. For example, a suggestion message such as "Consider talking to a professional counselor about your current condition" may be generated.

[1126] Step 9:

[1127] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[1128] Step 10:

[1129] The device analyzes the expert introduction message received from the server and displays it to the user. The user confirms the message "Consider talking to a professional counselor about your current condition" displayed on the device screen.

[1130] Example 1

[1131] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1132] In modern society, users often experience deep emotions such as sadness and stress, and they need a safe place to share their feelings. However, there is no system that provides empathetic and professional feedback on these emotions, which means users cannot receive appropriate support.

[1133] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1134] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a means for the emotion analysis means to generate a message for introducing an expert when specific conditions are met based on the analysis result, and a means for the response generation means to generate a response using a generative AI model. This enables users to safely share their emotions, receive empathetic responses, and also obtain support from experts as needed.

[1135] "User" refers to an individual who uses the system to input their feelings and experiences and receive responses and support from experts.

[1136] "Input means" refers to a means that provides an interface for users to input their feelings or experiences in text format. Examples include web forms and chat boxes in mobile applications.

[1137] The "emotion analysis means" is a means by which the server receives text data from the input means and analyzes the user's emotions using natural language processing technology.

[1138] The "response generation means" is a module for generating empathetic responses based on the emotions determined by the emotion analysis means. Specifically, it refers to creating responses using a generative AI model.

[1139] The "response providing means" refers to an interface for providing the generated empathetic response to the user. The terminal plays a role in displaying the response to the user.

[1140] The "expert introduction means" is a module that introduces an appropriate expert to the user when the emotion determined by the emotion analysis means satisfies a specific condition.

[1141] "Generative AI model" refers to artificial intelligence technology for generating responses through natural language processing, for example, using a Transformer model (such as GPT-3).

[1142] A "prompt" is a document provided as input to a generative AI model that contains instructions for the AI ​​to generate a response based on that input.

[1143] An embodiment of the present invention is a system for users to safely share their feelings and receive empathetic and professional feedback. The system performs sentiment analysis on user input, generates responses based on the sentiment analysis, and provides referrals to experts as needed.

[1144] System configuration

[1145] The system includes the following main components:

[1146] 1. Input Method

[1147] An interface that allows users to input their feelings and experiences in text format. The device provides the user with a means for this input, and examples include web forms and chat boxes in mobile applications.

[1148] 2. Emotion analysis method

[1149] This module analyzes text data received from the device and determines the user's emotions. The server uses natural language processing technology to analyze the content of the text data and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[1150] 3. Response Generation Method

[1151] This module generates empathetic responses based on the emotions determined by the emotion analysis means. For example, the server uses a Transformer model (such as GPT-3) to create empathetic responses appropriate to the user's emotional state.

[1152] 4. Means of providing a response

[1153] This is an interface for providing the generated response to the user. The terminal uses this means to display the response on the user's screen. Specifically, the response message is displayed on the chat screen.

[1154] 5. Specialist referral channels

[1155] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user according to the conditions.

[1156] System Operation

[1157] When the server receives text data from the terminal, it first analyzes the emotion of the input text data using emotion analysis means. If the analysis result indicates that the user is "stressed," it passes this result to the next step.

[1158] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[1159] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[1160] Specific examples

[1161] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[1162] Example prompts to input to the generative AI model

[1163] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[1164] User Input: I've been under a lot of stress at work lately and I'm really tired.

[1165] Response Generation Prompt: Generate an empathetic message for the user based on the following: "Emotion Label: stressed"

[1166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1167] Step 1:

[1168] The user inputs their emotions and experiences in text format. Specifically, the user uses a device to enter text such as "I've been feeling very stressed at work lately, and I'm very tired" into a chat box or web form and presses the send button. This text data is sent from the device to the server as input. The input data is passed to the server in, for example, JSON format.

[1169] Step 2:

[1170] The server uses sentiment analysis to analyze the text data received from the device. Specifically, the server uses natural language processing technology (e.g., the NLTK library or SpaCy) to analyze the text content. Here, the input data is text information, and the server analyzes this text to assign an sentiment label, such as "stressed." The sentiment label obtained as a result of the analysis is "stressed," which is passed on as output to the next step.

[1171] Step 3:

[1172] The server generates an empathetic response using a response generation method based on the results of the emotion analysis. Specifically, the server inputs a prompt sentence into a generative AI model (e.g., GPT-3). The prompt sentence is "emotion label: stressed," and the AIS model generates a response based on the prompt sentence: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed on as output to the next step.

[1173] Step 4:

[1174] The server sends the generated empathetic response to the terminal. Specifically, the server uses the response providing means to send the generated response in JSON format to the terminal. The terminal displays the received response on the user's screen. For example, the output may be a message on the chat screen saying, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[1175] Step 5:

[1176] If the result of the emotion analysis is determined to be "stressed," the server uses the expert referral means to generate a message introducing an appropriate counselor. Specifically, the server uses the expert referral means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition." This message is also sent to the device in JSON format. The device displays the received suggestion message on the user's screen, allowing the user to contact the suggested counselor.

[1177] (Application example 1)

[1178] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1179] In modern society, mental health issues are becoming more serious in the workplace, schools, and other settings. However, many users lack a place to appropriately share their emotions and stress, and often lack support for self-improvement. Furthermore, the lack of an environment for expert analysis of emotions, immediate empathetic feedback, and rapid referral to specialists prevents efficient mental health care.

[1180] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1181] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response providing means for providing the generated response to the user, an expert introduction means for introducing an expert as needed based on the emotion analysis means, a recommendation means for recommending an expert when the emotions analyzed by the emotion analysis means satisfy certain conditions, and a display means for displaying the generated response and the recommended expert to the user. This enables users to safely share their emotions, receive empathetic feedback, and obtain expert support as needed.

[1182] "Input means" refers to an interface that allows users to input emotions and experiences in text format. Examples include the input screen of a smartphone app or a web form.

[1183] The "emotion analysis means" is a module that analyzes text data from the input means and determines the user's emotions. It uses natural language processing technology to assign emotion labels based on the input data.

[1184] The "response generation means" is a module that generates an empathetic response based on the emotions analyzed by the emotion analysis means. For example, it generates appropriate feedback using a generative AI model.

[1185] The "response providing means" refers to an interface for displaying the generated response to the user. Specifically, this corresponds to the chat screen of a smartphone app.

[1186] The "expert introduction means" is a module that introduces appropriate experts to users as needed based on the sentiment analysis means. It recommends appropriate people from a list of experts according to the analysis results.

[1187] The "recommendation means" is a module that recommends experts when the emotions analyzed by the emotion analysis means meet certain conditions. It selects and recommends appropriate experts to support the user's mental health.

[1188] The "display means" refers to an interface for displaying the generated response and the recommended expert information to the user. Specifically, this corresponds to the display screen of a smartphone or the like.

[1189] The present invention provides a system that allows users to safely share their feelings and experiences and provides empathetic and expert feedback. The system analyzes the user's input, generates a response based on the emotion analysis, and introduces an expert if necessary. Each processing step is performed based on the roles of the server, the terminal, and the user.

[1190] System configuration

[1191] Input Method

[1192] The device provides an interface for users to input their feelings and experiences in text form, such as a smartphone app or a web form.

[1193] Emotion analysis means

[1194] The server analyzes the text data received from the device using emotion analysis. This analysis uses natural language processing technology. Using Hugging Face's Transformers library, emotion labels can be assigned based on the input data. For example, an input such as "I'm very stressed at work and very tired" would be judged as "NEGATIVE."

[1195] Response Generation Method

[1196] Based on the emotion label obtained by the emotion analysis means, the server generates an empathetic response. For example, if the emotion label is "NEGATIVE," a response such as "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself" is generated. A generative AI model (e.g., GPT-3) is used to generate this response.

[1197] Response delivery method

[1198] The generated response is displayed to the user through a response providing means, specifically, a chat screen on a smartphone app.

[1199] Expert referral methods

[1200] If certain conditions are met as a result of the sentiment analysis, the server will use the expert referral mechanism to recommend an appropriate expert to the user. Based on the analysis results, an appropriate person will be selected from the list of experts and introduced to the user. For example, a message such as "Consider speaking with a professional counselor for your current situation. Recommended expert: Yamada Taro - contact@example.com" will be generated.

[1201] Recommendation and display methods

[1202] The server includes a recommendation unit that recommends an expert when the user's emotion meets a specific condition. The generated response and information about the recommended expert are presented to the user through a display unit, such as a smartphone display screen.

[1203] Specific examples

[1204] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server performs emotion analysis on this text data and determines it to be "NEGATIVE." The server then uses the response generation means to generate an empathetic response such as "It seems like you're feeling very stressed at work. It's important that you take some time to rest for yourself." This response is then displayed to the user via the response providing means.

[1205] Furthermore, the server uses the expert introduction means to generate a suggestion message saying, "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com," which is also sent to the terminal. The terminal displays this message, and the user can contact the suggested counselor.

[1206] Prompt Sentence Examples

[1207] TXT

[1208] "I've been stressed out at work lately and I'm really tired."

[1209] In this way, the present invention provides a system that allows users to safely share their feelings and receive professional support if necessary.

[1210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1211] Step 1:

[1212] Users input their emotions and experiences in text format. Specifically, they use the input screen of a smartphone app or a web form to freely enter the stress and worries they are feeling. This text data is then passed to the next processing step.

[1213] Step 2:

[1214] The device receives text data entered by the user and sends it to the server. At this time, the data is transmitted to the server via an endpoint (API). The server then receives the user's input data and prepares it for analysis.

[1215] Step 3:

[1216] The server analyzes the received text data using a sentiment analysis tool. Natural language processing techniques are used to determine the user's sentiment from the text data. The specific software used is the Hugging Face Transformers library. Based on the input data, an emotional label (e.g., "NEGATIVE") is assigned.

[1217] Step 4:

[1218] The server generates an empathetic response using the response generation means based on the emotion label obtained by the emotion analysis means. In this process, a generative AI model (e.g., GPT-3) is used to generate a natural-spoken response that matches the emotion label. The emotion label is used as input, and an appropriate response sentence is generated as output.

[1219] Step 5:

[1220] The generated response is sent from the server to the terminal through the response providing means, where the response is converted into a format that can be displayed to the user. The user can check the generated response on the chat screen of the terminal.

[1221] Step 6:

[1222] Based on the results of the emotion analysis, the server uses expert referral methods to recommend appropriate experts as needed. If the emotion label meets certain conditions (e.g., "high stress level"), the server selects an appropriate counselor or mental health professional from a list of experts, allowing the user to receive appropriate support.

[1223] Step 7:

[1224] The server also transmits the expert recommendation information generated through the recommendation means to the terminal. The terminal presents this to the user using the display means. For example, a message such as "Consider talking to a professional counselor about your current condition. Recommended expert: Yamada Taro - contact@example.com" is displayed. The user can take the next action, such as counseling, based on this information.

[1225] These steps allow users to receive appropriate feedback based on their emotions and access professional support if needed.

[1226] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1227] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[1228] System configuration

[1229] The system includes the following main components:

[1230] 1. Input method:

[1231] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides this input means to users, such as web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[1232] 2. Emotion analysis means:

[1233] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[1234] 3. Emotion Engine:

[1235] This is a specialized module for recognizing emotions from user input data. When there is voice input, it uses the voice recognition module, and when there is image input, it uses the facial expression recognition module to accurately grasp the user's emotional state.

[1236] 4. Response Generation Method:

[1237] The server includes a module for generating an empathetic response based on the emotion determined by the emotion analysis means. The server uses, for example, a Transformer model (such as GPT-3) to generate an empathetic response appropriate to the user's emotional state.

[1238] 5. Means of providing response:

[1239] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[1240] 6. Specialist Referral Methods:

[1241] This module introduces appropriate experts (e.g., counselors) to users when the emotions determined by the emotion analysis means meet certain conditions. The server maintains a list of appropriate experts and generates an introduction message to the user depending on the conditions.

[1242] Program processing (natural language explanation)

[1243] When the server receives text, voice, or facial expression data from the device, it first uses emotion analysis to analyze the emotion of the input data. For example, if a user inputs text like "I've been stressed at work lately, and I'm very tired," it might determine the emotion as "stressed." It can then more accurately identify the user's emotional state through facial expression analysis of voice data and facial images.

[1244] Next, the server generates an empathetic response using the response generation means, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself." This generated response is passed to the response providing means and displayed on the terminal.

[1245] Furthermore, the server uses the expert introduction means to generate a message introducing an appropriate counselor when the user's emotion is determined to be "stressed." For example, the server generates a suggestion message such as "Consider talking to a professional counselor about your current condition" and sends it to the terminal. The terminal displays this message to the user, and the user can contact the suggested counselor.

[1246] Specific examples

[1247] For example, if a user inputs "I've been under a lot of stress at work lately, and I'm very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." Next, the server uses a response generation means to generate a response such as "It seems like you're under a lot of stress at work. It's important that you take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user. Furthermore, the server uses an expert referral means to generate a suggestion message such as "Consider talking to a professional counselor about your current condition," which is also sent to the device. The device displays these messages to the user, allowing the user to receive appropriate support.

[1248] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[1249] The processing flow will be explained below.

[1250] Step 1:

[1251] The user inputs their emotions and experiences, for example, by typing "I've been feeling very stressed at work lately, and I'm very tired" into a text field, or by using a voice input interface to say, "I've been feeling very stressed at work lately, and I'm very tired." Alternatively, facial expression data can be captured by pointing their face through the camera.

[1252] Step 2:

[1253] The terminal sends the text data, voice data, and facial expression data entered in step 1 to the server. The text data and voice data are sent as an HTTP POST request. Similarly, the facial expression data is sent as an image.

[1254] Step 3:

[1255] The server receives the data sent from the device and stores it in JSON or image format.

[1256] Step 4:

[1257] The server uses an emotion analysis means and emotion engine to analyze the emotions of the received text, voice, and facial expression data. For example, it analyzes the text data "I'm stressed at work" and determines the user's emotion as "stressed." In the case of voice data, it converts the voice into text through a voice recognition module and analyzes emotions based on that text. In the case of facial expression data, it uses a facial expression recognition module to determine emotions from changes in facial expressions.

[1258] Step 5:

[1259] The server uses the response generation means to generate an empathetic response based on the emotion analysis results, for example, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[1260] Step 6:

[1261] The server transmits the generated response to the terminal based on the response providing means, and the response message is transmitted to the terminal in JSON format.

[1262] Step 7:

[1263] The device analyzes the response message received from the server and conveys it to the user by display or voice. The user sees the message on the device screen or hears the message, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[1264] Step 8:

[1265] The server further uses the expert referral means based on the sentiment analysis result to generate a message to introduce an appropriate expert as needed, for example, a suggestion message such as "Consider talking to a professional counselor about your current condition."

[1266] Step 9:

[1267] The server sends an expert introduction message to the terminal, which is also sent in JSON format.

[1268] Step 10:

[1269] The device analyzes the expert introduction message received from the server and displays it to the user. The user sees the message "Consider speaking to a professional counselor about your current condition" on the device screen and can contact an expert if necessary.

[1270] Example 2

[1271] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1272] In modern society, users need a safe way to share their deep emotions, such as sadness or stress, and receive expert, empathetic feedback. However, existing systems often struggle to accurately analyze emotions and generate appropriate responses. They also lack the ability to quickly and appropriately refer users to experts. This can leave users frustrated and anxious, potentially exacerbating the problem.

[1273] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1274] In this invention, the server includes an input means for a user to input emotions and experiences, an emotion analysis means for receiving data from the input means and analyzing the user's emotions, a response generation means for generating an empathetic response based on the emotions analyzed by the emotion analysis means, a response provision means for providing the generated response to the user, an expert referral means for introducing an expert as needed based on the emotion analysis means, a data transmission means for appropriately formatting data transmitted from the input means, and a generative AI model for generating an expert response based on the emotion label determined by the emotion analysis means. This allows the user to have their emotions accurately analyzed, receive an empathetic and expert response, and be referred to an appropriate expert as needed.

[1275] "Input means" refers to a means that provides an interface for a user to input emotions and experiences as text, voice, or facial expression data.

[1276] The "emotion analysis means" is a module that analyzes text, voice, or facial expression data received from the terminal and determines the user's emotions.

[1277] The "response generation means" includes a module that generates an empathetic response based on the emotion determined by the emotion analysis means.

[1278] The "response providing means" is an interface for providing the generated response to the user.

[1279] The "expert introduction means" is a module that introduces an appropriate expert (such as a counselor) to the user when the emotion determined by the emotion analysis means meets certain conditions.

[1280] The "data transmission means" has the function of appropriately formatting data transmitted from the input means and transmitting the formatted data to the server.

[1281] A "generative AI model" is an artificial intelligence model that generates specialized responses based on emotion labels determined by emotion analysis means, and utilizes natural language processing technology.

[1282] MODE FOR CARRYING OUT THE INVENTION

[1283] This invention realizes a system that allows users to safely share emotions such as deep sadness or stress and provides empathetic and professional feedback. This system analyzes the user's input, generates a response based on that, and even refers the user to an expert if necessary. In addition, by combining it with an emotion engine, it can recognize and analyze the user's emotions more accurately.

[1284] System configuration

[1285] The system includes the following main components:

[1286] 1. Input Method

[1287] The device provides an interface for users to input their emotions and experiences as text, voice, or facial expression data. The device provides users with this input method, which may include web forms, chat boxes in mobile applications, voice input interfaces, and camera-based facial expression recognition interfaces.

[1288] 2. Emotion analysis method

[1289] This module analyzes text, voice, or facial expression data received from the device to determine the user's emotions. The server uses an emotion engine to analyze the data from multiple angles and assigns an emotion label (e.g., "sad," "stressed," "neutral," etc.).

[1290] 3. Data Transmission Method

[1291] The terminal has the function of appropriately formatting data sent from the input means and sending it to the server. Specifically, the terminal converts input text data and voice data into an appropriate format and sends it to the server.

[1292] 4. Generative AI Models

[1293] This is a specialized module for recognizing emotions from user input data and generating empathetic responses. It uses generative AI models (such as GPT-3, which utilizes natural language processing technology) to create empathetic responses that are appropriate for the user's emotional situation.

[1294] 5. Response Generation Methods

[1295] This module generates an empathetic response based on the emotions determined by the emotion analysis means. The generated response might be something like, "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[1296] 6. Means of providing a response

[1297] An interface for providing the generated response to the user. The device uses this means to display or communicate the response to the user. Specifically, it includes interfaces that combine chat screens, voice messages, and facial feedback.

[1298] 7. Specialist Referral Methods

[1299] This module introduces the user to an appropriate expert (e.g., a counselor) when the emotion determined by the emotion analysis means meets certain conditions. The server maintains a list of appropriate experts and generates an introduction message for the user depending on the conditions. For example, it generates a suggestion message such as "Consider talking to a professional counselor about your current condition," and sends this to the terminal.

[1300] Specific examples

[1301] For example, if a user inputs "I've been feeling very stressed at work lately and am very tired," the device sends this text data to the server. The server analyzes this text data using an emotion analysis means and determines the emotion label as "stressed." The server then uses the generative AI model to generate a response such as "It seems like you're feeling very stressed at work. It's important to take some time to rest for yourself." This response is sent to the device via a response provision means and displayed to the user.

[1302] Furthermore, the server generates a suggestion message using an expert introduction means, saying "Consider talking to a professional counselor about your current condition," which is also sent to the terminal. The user can then contact the suggested counselor.

[1303] Prompt Sentence Examples

[1304] "Generate an empathetic response about how the user has been experiencing a lot of stress at work lately."

[1305] "This user's emotion has been determined to be 'stressed'. Please generate a counselor introduction based on this."

[1306] The present invention provides a system that allows users to safely share their emotions and receive professional support as needed. The introduction of an emotion engine makes it possible to more accurately recognize users' emotions and provide more appropriate feedback and support.

[1307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1308] Step 1:

[1309] User emotion input

[1310] Users use the device to input their emotions and experiences, for example, through a web form, a chat box in a mobile application, a voice input interface, or a camera to input emotional data.

[1311] Input: User emotions and experiences (text, voice, images)

[1312] Output: Temporarily storing and preparing data by the device

[1313] Specific operation example:

[1314] A user types into a chat box on a mobile app, "I've been feeling very stressed at work lately and am very tired."

[1315] Step 2:

[1316] Sending text data

[1317] The device sends the user's input data to the server, and in the process, the device converts the data into the appropriate format depending on the input format (text, audio, image).

[1318] Input: User-entered text, voice, and image data

[1319] Output: Formatted data is sent to the server

[1320] Specific operation example:

[1321] The device sends text data such as "I've been under a lot of stress at work lately and I'm very tired" to the server.

[1322] Step 3:

[1323] Emotion analysis

[1324] The server analyzes the received data using emotion analysis techniques, such as text analysis, voice analysis, and facial expression analysis, to determine the user's emotions.

[1325] Input: User data sent to the server

[1326] Output: Annotated emotion label (e.g. "stressed")

[1327] Specific operation example:

[1328] The server analyzes the text "I've been stressed at work lately and I'm very tired" and determines that it is "stressed."

[1329] Step 4:

[1330] Generating an empathic response

[1331] The server uses a generative AI model to generate an empathetic response based on the emotion determined by the emotion analysis means.

[1332] Input: Annotated emotion labels, user input data

[1333] Output: A generated empathetic response (e.g., "It sounds like you're under a lot of stress at work. It's important that you take some time off for yourself.")

[1334] Specific operation example:

[1335] The generative AI model generates the response, "It seems like you're under a lot of stress at work. It's important to take some time for yourself to relax."

[1336] Step 5:

[1337] Providing a response

[1338] The generated response is sent to the terminal through the response providing means and displayed to the user, and the terminal provides an interface for displaying the response in an easy-to-view manner to the user.

[1339] Input: Generated empathic response

[1340] Output: Response displayed to the user

[1341] Specific operation example:

[1342] The device displays the generated response on the chat screen, and the user sees the response: "It seems like you're under a lot of stress at work. It's important to take some time to rest for yourself."

[1343] Step 6:

[1344] Generating and providing expert referrals

[1345] Based on the results of the sentiment analysis, the server generates a message introducing a specialist (such as a counselor) if the user meets certain conditions and provides this to the user.

[1346] Input: Sentiment analysis results, list of experts

[1347] Output: Expert introduction message

[1348] Specific operation example:

[1349] The server generates an introductory message for users who are judged to be "stressed," saying, "Consider talking to a professional counselor about your current condition." The server then sends this message to the user's device. The device then displays this message to the user.

[1350] Thus, the present invention provides a system that allows users to have their emotions accurately analyzed and receive empathetic and professional responses.

[1351] (Application example 2)

[1352] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1353] In modern food delivery services, users often experience stress and frustration during the ordering process. These emotions can negatively impact service ratings and user experience, ultimately leading to lower customer satisfaction. However, traditional delivery services lack a mechanism for analyzing user emotions in real time and providing appropriate feedback and support. To solve this problem, it is necessary to develop a system that can accurately grasp user emotions and provide necessary support and empathetic feedback.

[1354] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an interface means through which the user inputs emotions and experiences, an analysis means that receives data from the interface means and analyzes the user's emotions, a generation means that generates an empathetic response based on the emotions analyzed by the analysis means, a provision means that provides the generated response to the user, a referral means that introduces an expert as needed based on the analysis means, and a notification means that displays the generated response and referral message to the user via the response provision means. This makes it possible to analyze the user's emotions in real time and provide appropriate feedback and support.

[1355] "Interface means" refers to input devices and software that allow users to input emotions and experiences.

[1356] The "analysis means" is a function for analyzing data received from the interface means and determining the user's emotions.

[1357] The "generation means" is a function for generating an empathetic response based on the analyzed emotions.

[1358] The "means for providing" is a function for transmitting the generated response to the user.

[1359] "Introduction method" is a function that introduces users to appropriate experts based on the analysis results.

[1360] The "notification means" is a function for displaying to the user the response or introduction message generated through the response providing means.

[1361] The present invention is a system that allows users to share their feelings and experiences, provides empathetic responses based on those feelings and experiences, and refers users to experts as needed. The system mainly includes the following main components: an interface means, an analysis means, a generation means, a provision means, a referral means, and a notification means.

[1362] System configuration

[1363] The system operates as follows:

[1364] 1. Interface Method

[1365] Users can input their emotions and experiences through interface means, which are implemented through smartphone applications and include voice input, text input, or facial recognition via a camera.

[1366] 2. Analysis tools

[1367] The data received from the interface means is sent to a server, where emotion analysis is performed by the analysis means. This analysis uses an emotion recognition model, such as an emotion analysis algorithm by GPT-3 or TensorFlow, to determine whether the user's input is "stressed" or "neutral," etc.

[1368] 3. Generation means

[1369] The server generates an empathetic response based on the emotional information analyzed by the analytical means. For example, in response to an input such as "I've been stressed at work lately and I'm very tired," the generative AI model creates a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break."

[1370] 4. Means of provision

[1371] The generated response is transmitted to the user via a means of providing the response, which is also implemented on a smartphone application and displayed to the user as a text message or a voice message.

[1372] 5. Referral methods

[1373] If necessary, the system will refer the user to a specialist. For example, if the analysis results in a "stressed" condition, the system will generate a message such as "Consider talking to a professional counselor."

[1374] 6. Means of notification

[1375] To always provide timely feedback to the user, a notification means is used, which has the function of displaying the generated response or introduction message on the user's terminal.

[1376] Usage example

[1377] When a user types, "I've been stressed at work lately and I'm very tired," the data is sent to the server through the smartphone's input interface. The server receives this data, performs emotional analysis, and determines the result as "stressed." It then generates an empathetic response, "It seems you're feeling stressed while ordering. I recommend you take a short break," and provides this to the user. If necessary, an introductory message is also displayed, saying, "Consider talking to a professional counselor."

[1378] Prompt Sentence Examples

[1379] "Based on the text entered by the user, analyze their sentiment and generate empathetic feedback. Also, generate a message to connect them with the right expert."

[1380] Thus, by following the above components and processing steps, a system can be realized that allows users to safely share their feelings and provide empathetic and professional feedback.

[1381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1382] Step 1:

[1383] The device accepts input of emotions and experiences from the user. This input can be text input, voice input, or facial expression recognition. For example, the input text "I've been stressed at work lately and I'm very tired" is acquired by the device.

[1384] input:

[1385] User text input, voice input, or facial expression data.

[1386] output:

[1387] Emotional and experience data sent to the server.

[1388] Step 2:

[1389] The device sends the acquired emotion and experience data to a server, which then analyzes the received data using analytical means. For example, an emotion recognition algorithm using GPT-3 or TensorFlow is used for the analysis.

[1390] input:

[1391] Emotional and experience data sent from the device.

[1392] output:

[1393] The results of sentiment analysis.

[1394] Step 3:

[1395] The server analyzes the received data and determines the emotion. The analyzed emotion is determined as "stressed" or "neutral," etc. The specific analysis process involves using GPT-3 to analyze text data and assign emotion labels. Voice data and facial expression data are also analyzed in the same way.

[1396] input:

[1397] Emotional and experiential data.

[1398] output:

[1399] The determined emotion label (e.g., "stressed").

[1400] Step 4:

[1401] The server generates an empathetic response using a generative means based on the determined emotion label. A generative AI model (e.g., GPT-3) is used to create an appropriate empathetic response. Specifically, for input such as "I've been stressed at work lately and I'm very tired," a response such as "It seems like you're feeling stressed while ordering. I recommend you take a short break" is generated.

[1402] input:

[1403] An emotion label (e.g., "stressed").

[1404] output:

[1405] Generated empathic response messages.

[1406] Step 5:

[1407] The server provides the generated empathetic response to the terminal through the providing means, and the terminal communicates the generated response to the user by displaying or audibly displaying the response. Specifically, the response is displayed as a text message on the application.

[1408] input:

[1409] Generated empathic response messages.

[1410] output:

[1411] The response message is displayed on the terminal.

[1412] Step 6:

[1413] The server will refer the user to a specialist if necessary. For example, if the emotion label is determined to be "stressed," the server will use the referral mechanism to generate a referral message such as "Consider speaking to a professional counselor."

[1414] input:

[1415] An emotion label (e.g., "stressed").

[1416] output:

[1417] An expert introduction message is generated.

[1418] Step 7:

[1419] The server provides the generated introduction message to the terminal via a notification means, and the terminal displays the introduction message to the user. Specifically, the introduction message is displayed as an expert introduction message on the application.

[1420] input:

[1421] Generated expert introduction message.

[1422] output:

[1423] An introductory message will be displayed on your device.

[1424] Through these steps, we will create a system that can analyze users' emotions in real time and provide appropriate feedback and professional support.

[1425] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1426] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1427] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1428] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1429] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1430] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1431] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1432] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1433] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1434] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1435] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1436] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1437] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1438] 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.

[1439] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1440] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1441] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1442] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1443] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1444] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1445] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1446] The following is further disclosed regarding the above embodiment.

[1447] (Claim 1)

[1448] An input means for users to input their emotions and experiences;

[1449] emotion analysis means for receiving data from the input means and analyzing the emotion of the user;

[1450] a response generating means for generating an empathetic response based on the emotion analyzed by the emotion analyzing means;

[1451] a response providing means for providing the generated response to the user;

[1452] an expert introduction means for introducing an expert as needed based on the emotion analysis means;

[1453] A system including:

[1454] (Claim 2)

[1455] 2. The system of claim 1, wherein the input means includes a text input means.

[1456] (Claim 3)

[1457] 2. The system of claim 1, wherein the response generating means uses natural language processing techniques.

[1458] "Example 1"

[1459] (Claim 1)

[1460] an input means for a user to input emotions and experiences;

[1461] emotion analysis means for receiving data from the input means and analyzing the emotion of the user;

[1462] a response generating means for generating an empathetic response based on the emotion analyzed by the emotion analyzing means;

[1463] a response providing means for providing the generated response to the user;

[1464] an expert introduction means for introducing an expert as needed based on the emotion analysis means;

[1465] a means for generating a message for introducing an expert when a specific condition is met based on the analysis result of the emotion analysis means;

[1466] and means for generating a response using a generative AI model.

[1467] (Claim 2)

[1468] 2. The system of claim 1, wherein the input means includes means for inputting data in text form.

[1469] (Claim 3)

[1470] 2. The system of claim 1, wherein the response generation means includes means for generating a response using a prompt sentence to a generative AI model.

[1471] "Application Example 1"

[1472] (Claim 1)

[1473] An input means for users to input their emotions and experiences;

[1474] emotion analysis means for receiving data from the input means and analyzing the emotion of the user;

[1475] a response generating means for generating an empathetic response based on the emotion analyzed by the emotion analyzing means;

[1476] a response providing means for providing the generated response to the user;

[1477] an expert introduction means for introducing an expert as needed based on the emotion analysis means;

[1478] a recommendation means for recommending an expert when the emotion analyzed by the emotion analysis means satisfies a specific condition;

[1479] display means for displaying the generated responses and recommended experts to the user;

[1480] A system including:

[1481] (Claim 2)

[1482] 2. The system of claim 1, wherein the input means includes a text input means.

[1483] (Claim 3)

[1484] 2. The system of claim 1, wherein the response generating means uses natural language processing techniques.

[1485] "Example 2: Combining Emotion Engines"

[1486] (Claim 1)

[1487] An input means for users to input their emotions and experiences;

[1488] emotion analysis means for receiving data from the input means and analyzing the emotion of the user;

[1489] a response generating means for generating an empathetic response based on the emotion analyzed by the emotion analyzing means;

[1490] a response providing means for providing the generated response to the user;

[1491] an expert introduction means for introducing an expert as needed based on the emotion analysis means;

[1492] data transmission means for appropriately formatting data transmitted from said input means;

[1493] a generative AI model that generates specialized responses based on the emotion labels determined by the emotion analysis means;

[1494] A system including:

[1495] (Claim 2)

[1496] 2. The system of claim 1, wherein the input means includes a text input means.

[1497] (Claim 3)

[1498] 2. The system of claim 1, wherein the response generating means uses natural language processing techniques.

[1499] (Claim 4)

[1500] 10. The system of claim 1, wherein the generative AI model includes a prompt sentence for providing the generated response to the user.

[1501] "Application example 2 when combining emotion engines"

[1502] (Claim 1)

[1503] an interface means for users to input their emotions and experiences;

[1504] analysis means for receiving data from the interface means and analyzing user emotions;

[1505] a generating means for generating an empathetic response based on the emotion analyzed by the analyzing means;

[1506] a providing means for providing the generated response to a user;

[1507] A referral means for introducing an expert as needed based on the analysis means;

[1508] a notification means for displaying the generated response or introduction message to the user through the response providing means;

[1509] A system including:

[1510] (Claim 2)

[1511] 2. The system according to claim 1, wherein the interface means includes text input, voice input, and facial expression recognition means.

[1512] (Claim 3)

[1513] 2. The system of claim 1, wherein the response generation means generates empathetic responses using a generative AI model. [Explanation of symbols]

[1514] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. An input means for users to input their emotions and experiences; emotion analysis means for receiving data from the input means and analyzing the emotion of the user; a response generating means for generating an empathetic response based on the emotion analyzed by the emotion analyzing means; a response providing means for providing the generated response to the user; an expert introduction means for introducing an expert as needed based on the emotion analysis means; A system including:

2. 2. The system of claim 1, wherein said input means includes a text input means.

3. 2. The system of claim 1, wherein the response generating means uses natural language processing techniques.

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