system

Abstract

Provide a system. [Solution] A means for receiving a concern input by a user; A means for transmitting the received concerns to a natural language processing engine; means for receiving advice generated by the natural language processing engine; means for displaying the received advice to the user; A system including:

Description

[Technical Field]

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

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

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

[0004] In conventional problem-solving systems, in order for users to receive appropriate advice for their problems, they must be dealt with by a human with specialized knowledge and experience, which is time-consuming and costly. Furthermore, in order to receive appropriate advice, users themselves must be able to accurately describe their problems. Therefore, there is a demand for a system that allows general users to receive appropriate advice quickly and easily. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. A system is provided that includes means for receiving a problem input by a user, means for transmitting the received problem to a natural language processing engine, means for receiving advice generated by the natural language processing engine, and means for displaying the received advice to the user. Furthermore, by using an artificial intelligence text generation engine as the natural language processing engine, advice corresponding to the user's problem can be generated quickly and effectively. Furthermore, by providing means for analyzing the problem input by the user and converting it into JSON format data, the processing efficiency of the entire system is improved.

[0006] "User" refers to a person who uses the system to input their concerns and receive advice.

[0007] "Input" refers to the act of a user providing information to a system using a keyboard or other device.

[0008] A "natural language processing engine" refers to software or a system that analyzes input text and understands and generates human language.

[0009] An "artificial intelligence text generation engine" refers to an artificial intelligence-based system that has the ability to understand natural language and generate appropriate responses and advice.

[0010] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a text format that structures data to make it easier to store and transmit.

[0011] "Analysis" refers to the process of breaking down input data and understanding its contents.

[0012] "Advice" refers to advice or suggestions provided by the system in response to a user's concerns.

[0013] "Receiving" refers to the terminal or server taking in data or information.

[0014] "Transmission" refers to the act of a terminal or server sending data or information to another system or device.

[0015] "Display" refers to the visual presentation of information on a terminal monitor or screen. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] To implement this invention, a system is required in which a user inputs their concerns through a terminal, the server receives the input, performs a series of processes, and then displays advice to the user. The main components of this system include a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[0038] Program processing overview

[0039] 1. The user inputs their concerns on their device

[0040] The user enters their concerns into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0041] 2. The device sends the worries to the server

[0042] The device converts the worries entered into the input form into JSON format data and sends it to the server via an HTTP POST request. For example, the data sent to the server is {"trouble": "I'm stressed out at work and I don't know what to do"}.

[0043] 3. The server receives the message

[0044] The server receives the JSON data sent from the device, parses it, and extracts the parsed worry text.

[0045] 4. The server uses a natural language processing engine

[0046] The server sends the received text of the problem to a natural language processing engine, which understands the problem and generates prompts that provide appropriate advice.

[0047] 5. The server sends a request to the AI ​​text generation engine

[0048] The server uses the generated prompt to request an AI text generation engine to generate advice. For example, it sends a prompt such as "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0049] 6. AI text generation engine generates advice

[0050] The AI ​​text generation engine generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "I understand you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what's most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0051] 7. The server receives the advice and returns it to the device.

[0052] The server receives the advice returned by the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."}

[0053] 8. The device displays advice to the user

[0054] The device parses the JSON data received from the server and displays the advice to the user, who can then interpret the advice and use it to take action.

[0055] In this way, the system according to the present invention can analyze the worries entered by the user and quickly provide advice based on the analysis. This process allows the user to receive effective advice even without specialized knowledge or experience, thereby enabling rapid problem resolution.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] Users input their concerns from their device

[0059] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0060] Step 2:

[0061] The device converts the concerns into JSON format.

[0062] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0063] Step 3:

[0064] The device sends JSON data to the server

[0065] The device sends the generated JSON data to the server using an HTTP POST request.

[0066] Step 4:

[0067] The server receives the JSON data

[0068] The server receives an HTTP POST request from the device and parses the JSON data containing the problem.

[0069] Step 5:

[0070] The server uses a natural language processing engine

[0071] The server sends the analyzed problem text to a natural language processing engine, which analyzes the text and generates a prompt.

[0072] Step 6:

[0073] The server sends a request to the artificial intelligence text generation engine

[0074] The server then requests the AI ​​text generation engine to generate advice based on the generated prompt. For example, it sends the prompt "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0075] Step 7:

[0076] Artificial intelligence text generation engine generates advice

[0077] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0078] Step 8:

[0079] The server receives the generated advice

[0080] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0081] Step 9:

[0082] The server sends JSON data to the terminal.

[0083] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0084] Step 10:

[0085] The device receives the advice

[0086] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."}

[0087] Step 11:

[0088] The device displays advice to the user

[0089] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0090] The above steps realize a system that allows users to receive prompt and appropriate advice on their concerns.

[0091] Example 1

[0092] 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."

[0093] In modern society, individual users have a wide range of problems, and there is a need for methods to quickly and effectively address them. However, users often need time and effort to seek professional advice, making it difficult to quickly resolve their problems. Furthermore, existing systems often lack the accuracy and efficiency to generate and provide appropriate advice for the problems entered by users.

[0094] 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.

[0095] In this invention, the server includes a means for receiving a problem input by a user, a means for transmitting the received problem to a natural language processing engine, and a means for receiving advice generated by the natural language processing engine. This makes it possible to analyze the problem input by the user and provide quick and effective advice using a generative AI model. Furthermore, by adding a means for converting the generated advice into JSON format and transmitting it to the terminal, the user can receive the advice in a format that is easy to understand and implement. This enables quick and effective problem solving for each user's problem.

[0096] A "user" is an individual or group that uses the system to input their concerns and receive advice.

[0097] A "terminal" is a hardware device or software application that a user uses to input concerns and receive advice.

[0098] A "server" is a central processing unit or system that receives input of concerns from users and performs processing for analysis and advice generation.

[0099] A "natural language processing engine" is a processing engine that includes algorithms and technologies for analyzing text data and understanding its meaning.

[0100] A "generative AI model" is an artificial intelligence algorithm and model that generates appropriate advice based on input text data.

[0101] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data description language widely used as a data exchange format.

[0102] A "prompt" refers to pre-defined text used to prompt a generative AI model to generate advice.

[0103] "Means of analysis" refers to the processes and techniques used to interpret the text of the user's concerns and extract the necessary information.

[0104] "Advice" refers to useful information or suggestions that the generative AI model provides to address the user's concerns.

[0105] A "transmission means" is a method or protocol for moving data from one device or system to another.

[0106] This invention is a system that analyzes user-entered concerns and generates and provides advice based on the analysis. This system includes a user terminal, a server, a natural language processing engine, and a generative AI model.

[0107] Users input their concerns using a device. User devices can be smartphones, tablets, personal computers, etc. By typing their concerns into the input form and submitting it, the concerns are imported into the system. For example, a user might input, "I'm stressed out at work and I don't know what to do."

[0108] The terminal receives input from the user and converts it into JSON format, generating the following data: {"trouble": "I'm stressed at work and I don't know what to do"}. The generated JSON data is then sent to the server using an HTTP POST request.

[0109] The server receives and analyzes the JSON data sent from the device. A natural language processing engine is used for the analysis to understand the meaning of the text data. At this time, the server sends the text data of the worries to the natural language processing engine and requests it to analyze it to understand the content of the worries.

[0110] The server generates a prompt based on the analysis results and sends it to the generative AI model. The generative AI model uses the prompt to generate advice. For example, a prompt could be "Worry: I'm stressed out at work and I don't know what to do.\nAdvice from my future self:"

[0111] Based on this prompt, the generative AI model generates appropriate advice and sends it back to the server. An example of generated advice is, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. It is also important not to keep it to yourself, but to talk to your colleagues or superiors."

[0112] The server converts the received advice into JSON format and sends it to the device. For example, the following data is generated: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."

[0113] Finally, the device analyzes the received JSON data and displays advice to the user. The user can then confirm the displayed advice and use it to take practical action. In this way, the system according to the present invention can analyze the concerns entered by the user and quickly provide advice based on the analysis. This allows users to receive effective advice and quickly solve problems, even if they do not have specialized knowledge or experience.

[0114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0115] Step 1:

[0116] The user enters their worries into an input form on the device. The entered worries are saved as text data on the device. As an example of input, the user might type, "I'm stressed out at work and I don't know what to do." The specific user interaction is the action of clicking the "Send" button on the device screen. The input data is the worries in text format.

[0117] Step 2:

[0118] The device converts the received text data into JSON format. For example, it generates JSON data such as {"trouble": "I'm stressed at work and I don't know what to do"}. The device then sends this JSON data to the server via an HTTP POST request. The input data is the text-formatted problem, and the output is JSON format data.

[0119] Step 3:

[0120] The server receives the JSON data sent from the device. The server parses this data and extracts the relevant text data. Specifically, it uses the JSON.parse function to parse the data {"trouble": "I'm stressed at work and I don't know what to do"}. The input data is JSON format data, and the output is the parsed text data.

[0121] Step 4:

[0122] The server sends the extracted text data to a natural language processing engine. The natural language processing engine analyzes the received text data and understands its meaning. In this process, for example, a POST request is sent to the API endpoint of the natural language processing engine. The input data is the text-formatted problem data, and the output is the analysis results.

[0123] Step 5:

[0124] The server generates a prompt based on the analysis results returned by the natural language processing engine. The generated prompt is then sent to a generative AI model, requesting it to generate advice. Specifically, it generates a prompt in the format "Problem: I'm stressed at work and don't know what to do.\nAdvice from my future self:" and sends it to the generative AI model. The input data is the text of the analysis results, and the output is the generated prompt.

[0125] Step 6:

[0126] The generative AI model generates advice based on the received prompt. For example, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors." The generated advice is then sent back to the server. The input data is the prompt, and the output is the generated advice text.

[0127] Step 7:

[0128] The server receives the advice returned from the generative AI model. It converts the received advice back into JSON format and sends it to the device. For example, it converts it into the following format: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."} and sends it. The input data is the advice text, and the output is the advice data in JSON format.

[0129] Step 8:

[0130] The device parses the JSON data received from the server. The parsed data is displayed to the user through the user interface. The user reads the displayed advice and puts it into action. The input data is advice data in JSON format, and the output is text advice that is displayed to the user. Specifically, the device application displays the advice on the screen.

[0131] (Application example 1)

[0132] 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."

[0133] The purpose of this invention is to provide a support system that enables users with home security concerns to solve their problems effectively and quickly. Specifically, the objective is to establish a technical means for providing appropriate and professional advice immediately to users in response to their anxieties and concerns.

[0134] 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.

[0135] In this invention, the server includes means for receiving a concern input by a user, means for transmitting the received concern to a natural language processing engine, means for receiving advice generated by the natural language processing engine, means for displaying the received advice to the user, and means for providing a solution to the home security concern. This enables the security concern input by the user to be analyzed and appropriate advice provided by AI to be promptly received.

[0136] A "user" is someone who uses the system to input their concerns and receive advice.

[0137] A "problem" is a problem or anxiety that a user has, or an issue that the user wants to solve.

[0138] "Means for receiving" refers to the method or process by which the server obtains the worries entered by the user and the advice generated by the AI ​​engine.

[0139] A "natural language processing engine" is a collection of information processing technologies that analyzes text information entered by a user and understands its meaning and intent.

[0140] An "artificial intelligence text generation engine" is a technology that generates appropriate advice for users based on the analysis results of a natural language processing engine.

[0141] "Advice" refers to advice or solutions provided to users for their concerns.

[0142] "Home security" refers to the efforts and measures taken to ensure the safety of the home environment.

[0143] "Solution provision means" refers to a method or process that uses a natural language processing engine or an artificial intelligence text generation engine to provide specific advice or instructions based on the problem entered by the user.

[0144] To implement this invention, a system is required to provide appropriate advice to users regarding their home security concerns. This system is composed of a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[0145] 1. Program Generation

[0146] In this invention, the server includes the following means:

[0147] A means of receiving user-entered concerns

[0148] A means of sending received concerns to a natural language processing engine

[0149] A means of generating advice based on prompts generated by a natural language processing engine

[0150] A means of displaying received advice to the user

[0151] A means of providing solutions to home security concerns

[0152] 2. Hardware and Software Used

[0153] Hardware:

[0154] User devices: smartphones, tablets, computers, etc.

[0155] Server: Linux-based server or cloud server (e.g., AWS EC2 instance)

[0156] software:

[0157] Natural language processing engine: an engine for analyzing user input text (e.g., SpaCy, NLTK)

[0158] Artificial intelligence text generation engine: An engine for generating advice based on analysis results (e.g., OpenAI (registered trademark) GPT-3 (registered trademark))

[0159] Data processing and calculation:

[0160] Converts user input into JSON format and sends it to the server

[0161] The server uses a natural language processing engine to analyze the input text and generate an appropriate prompt.

[0162] Use prompts to let an artificial intelligence text generation engine generate advice

[0163] The generated advice is converted to JSON format and sent back to the user device.

[0164] The user device analyzes the received advice and displays it on the screen.

[0165] 3. Specific Examples

[0166] When a user inputs "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried," the system operates as follows:

[0167] The server sends the user's input to a natural language processing engine, which generates a prompt such as, "I feel like there's a suspicious person walking around my house at night. I'm worried. What home security advice do I need?"

[0168] The AI ​​text generation engine generates advice based on the prompts, such as, "First, install an outdoor camera and set it up to send notifications when movement is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[0169] Example prompt sentence:

[0170] Problem: I feel like there is a suspicious person walking around my house in the middle of the night. It makes me uneasy.

[0171] Any advice please:

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1:

[0174] The user inputs their concerns. There is an input form on the user's device, and they can enter specific concerns, such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried." This input is sent to the server as JSON format data.

[0175] Step 2:

[0176] The device sends the worries to the server. The user device converts the worries into JSON format data and sends it to the server via an HTTP POST request. For example, the following data is sent to the server: {"trouble": "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried."}

[0177] Step 3:

[0178] The server receives the concerns. The server receives the JSON data sent from the device, analyzes it, and extracts the concerns. At this time, it classifies the concerns entered by the user into security-related categories.

[0179] Step 4:

[0180] The server uses a natural language processing engine. The server sends the received worry text to the natural language processing engine. This process analyzes the worry text and generates an appropriate prompt. For example, it generates a prompt such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried. Please tell me what home security advice I need."

[0181] Step 5:

[0182] The server sends a request to the artificial intelligence text generation engine. The server uses the generated prompt to request the artificial intelligence text generation engine to generate advice. The server sends a request with the prompt to the generative AI model and receives the generated advice text.

[0183] Step 6:

[0184] An artificial intelligence text generation engine generates advice. The generative AI model generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "First, install an outdoor camera and set it up to send notifications when motion is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[0185] Step 7:

[0186] The server receives the advice and sends it back to the device. The server receives the advice returned from the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends the following data: {"advice": "First, install an outdoor camera and set it up so that it sends notifications when it detects movement. It is also important to strengthen cooperation with neighboring residents and share information with each other."}

[0187] Step 8:

[0188] The device displays the advice to the user. The user's device parses the JSON data received from the server and displays the advice on the screen. The user can read the presented advice and use it to take action.

[0189] 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.

[0190] This invention is a system that analyzes user inputted worries and provides appropriate advice based on them. This system is characterized by its ability to recognize the user's emotional state and generate advice that takes this into consideration by combining it with an emotion engine. The main components of the system include a user terminal, a server, a natural language processing engine, an artificial intelligence text generation engine, and the emotion engine.

[0191] Program processing overview

[0192] 1. The user inputs their concerns on their device

[0193] The user enters their worries in text into the input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0194] 2. The device converts the concerns into JSON format

[0195] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0196] 3. The device sends JSON data to the server

[0197] The device sends the generated JSON data to the server using an HTTP POST request.

[0198] 4. The server receives the JSON data

[0199] The server receives an HTTP POST request from the device, parses the JSON data containing the worries, and extracts the parsed worry text.

[0200] 5. The server uses the emotion engine

[0201] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the user's emotion (e.g., stress, anxiety, joy, etc.).

[0202] 6. The server uses a natural language processing engine

[0203] The server takes into account the recognized emotional state and sends the text of the concern to a natural language processing engine to generate a prompt, for example, "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0204] 7. The server sends a request to the AI ​​text generation engine

[0205] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[0206] 8. AI text generation engine generates advice

[0207] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0208] 9. The server receives the generated advice

[0209] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0210] 10. The server sends JSON data to the device

[0211] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0212] 11. The device receives the advice

[0213] The device receives the HTTP response from the server and parses the JSON data containing advice, such as {"advice": "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."}

[0214] 12. The device displays advice to the user

[0215] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0216] In this way, advice is provided that takes into account the user's emotional state, allowing for more relevant and effective advice than conventional systems, helping users solve their problems more quickly and accurately.

[0217] The processing flow will be explained below.

[0218] Step 1:

[0219] Users input their concerns from their device

[0220] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0221] Step 2:

[0222] The device converts the concerns into JSON format.

[0223] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0224] Step 3:

[0225] The device sends JSON data to the server

[0226] The device sends the generated JSON data to the server using an HTTP POST request.

[0227] Step 4:

[0228] The server receives the JSON data

[0229] The server receives an HTTP POST request from the device and parses the JSON data containing the worries. By parsing the data, the worry text is extracted.

[0230] Step 5:

[0231] The server uses the emotion engine

[0232] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the emotional state, such as "stress" or "anxiety."

[0233] Step 6:

[0234] The server uses a natural language processing engine

[0235] The server sends the recognized emotional state and the text of the concern to a natural language processing engine to generate a prompt, such as "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0236] Step 7:

[0237] The server sends a request to the artificial intelligence text generation engine

[0238] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[0239] Step 8:

[0240] Artificial intelligence text generation engine generates advice

[0241] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you are feeling very stressed right now, but first, take a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0242] Step 9:

[0243] The server receives the generated advice

[0244] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0245] Step 10:

[0246] The server sends JSON data to the terminal.

[0247] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0248] Step 11:

[0249] The device receives the advice

[0250] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I understand you're under a lot of stress right now, but first take a short break. Then, re-prioritize your work and reaffirm what's most important to you. It's also important not to keep it to yourself, but to talk to a colleague or your boss about it."}

[0251] Step 12:

[0252] The device displays advice to the user

[0253] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0254] The above specific processing steps realize a system that allows a user to quickly obtain appropriate advice that takes into account the user's emotional state.

[0255] Example 2

[0256] 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."

[0257] Conventional systems have had difficulty providing appropriate and effective advice for the concerns entered by users. In particular, methods that generate general advice without considering the user's emotional state make it difficult to provide effective advice tailored to the user's individual situation. For this reason, there has been a demand for effective problem-solving and psychological support for users.

[0258] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving a concern input by a user; means for converting the received concern into JSON-formatted data; means for transmitting the JSON-formatted data to the server; means for analyzing the JSON data received by the server; means for transmitting the text of the concern to an emotion engine to recognize the user's emotional state; means for transmitting the text of the concern to a natural language processing engine to generate a prompt sentence based on the recognized emotional state; means for requesting an AI text generation engine to generate advice based on the generated prompt sentence; means for receiving the generated advice and converting it back into JSON format; means for transmitting the JSON data of the advice from the server to the terminal; and means for analyzing the JSON data of the advice received by the terminal and displaying it to the user. This enables more appropriate and effective advice that takes the user's emotional state into consideration.

[0259] "User" refers to the person who uses this system and inputs their concerns via a terminal.

[0260] A "terminal" is a device that allows a user to input their concerns and send them to the system, and includes smartphones, tablets, PCs, etc.

[0261] A "problem" is text data that a user enters into the system, expressing a particular problem or difficulty.

[0262] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight text format widely used as a data exchange format.

[0263] The "server" is a central system that receives data sent from the terminals, analyzes and processes it, and generates final advice.

[0264] A "natural language processing engine" is software or a system that analyzes text data entered by a user, understands its meaning, and generates prompt sentences.

[0265] An "emotion engine" is a system that functions as part of natural language processing and analyzes and recognizes emotional states from text entered by the user.

[0266] A "prompt sentence" is text data input to an artificial intelligence text generation engine, and is the sentence that serves as the basis for the generated advice.

[0267] An "artificial intelligence text generation engine" is an engine that generates advice to be provided to a user based on a prompt sentence.

[0268] An "HTTP POST request" is a type of network protocol for sending data to a server, and is used in this system to send data from a terminal to a server.

[0269] A "response" is a response message sent from the server to the terminal, and includes JSON data of the advice.

[0270] "Analysis" is the process of breaking down and analyzing data to understand its meaning and structure.

[0271] "Display" refers to the act of showing information to the user on the screen of a terminal.

[0272] This invention is a system that analyzes worries entered by a user and provides appropriate advice based on the analysis. This system is characterized by the use of an emotion engine to recognize the user's emotional state and generate advice that takes this into consideration. Below, we will explain how to specifically implement this system.

[0273] First, the devices used by users include smartphones, tablets, and PCs. Users enter their concerns in text form on these devices. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0274] The device then converts the user's input into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}. The converted data is then sent to the server using an HTTP POST request over the internet.

[0275] The server receives the HTTP POST request sent from the device. After receiving it, the server analyzes the JSON data included in the request body and extracts the text portion of the trouble. For example, the text "I'm stressed out at work and I don't know what to do" is extracted from {"trouble": "I'm stressed out at work and I don't know what to do"}.

[0276] The server then sends the extracted text to the emotion engine, which uses natural language processing technology to analyze the text and classify the user's emotions. For example, it can recognize a state of stress from the keyword "stressed."

[0277] Based on the recognized emotional state, the server sends the text to a natural language processing engine to generate a prompt, such as "Worry: I'm stressed at work and don't know what to do.\nAdvice from my future self:"

[0278] Based on the generated prompt, the server requests an AI text generation engine to generate advice. The prompt also includes the user's emotional state, allowing for more personalized advice. For example, advice may be provided in the form of "advice from your future self."

[0279] The AI ​​text generation engine generates advice based on the prompt, such as, "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."

[0280] The generated advice is returned to the server and converted back into JSON format. The server then sends the generated JSON format advice to the device as an HTTP response. For example, it could be formatted as {"advice": "I understand you're under a lot of stress right now, but first consider taking a short break..."}.

[0281] The device receives the HTTP response from the server and parses the JSON data containing advice, for example, {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."}

[0282] Finally, the device displays the analyzed advice on the user's screen. For example, a message such as "I understand you are feeling very stressed right now, but first consider taking a short break" can be displayed. The user can confirm the advice and use it to take action.

[0283] In this way, the present invention can provide appropriate and effective advice that takes into account the user's emotional state.

[0284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0285] Step 1:

[0286] The user inputs their worries from the terminal. Specifically, the user inputs their worries as text into the input form on the terminal. After inputting, the user presses the send button. An example of input is "I'm stressed out at work and I don't know what to do." The input in this step is the text of the user's worries, and the output is stored as text data on the terminal.

[0287] Step 2:

[0288] The device converts the worries into JSON format. The input text is analyzed by a program and converted into JSON format. For example, the input text "I'm stressed out at work and I don't know what to do" is formatted as {"trouble": "I'm stressed out at work and I don't know what to do"}. The input for this step is the user's text data, and the output is JSON format data.

[0289] Step 3:

[0290] The device sends JSON data to the server. The device sends the generated JSON data to the server using an HTTP POST request. The device issues a request to the specified endpoint on the server. The input for this step is JSON-formatted data, and the output is an HTTP request to the server.

[0291] Step 4:

[0292] The server receives the JSON data. The server receives the HTTP POST request and analyzes the received content. The JSON data included in the request body is analyzed and the text portion of the problem is extracted from it. For example, from {"trouble": "I'm stressed out at work and I don't know what to do"}, "I'm stressed out at work and I don't know what to do" is extracted. The input of this step is the HTTP request, and the output is the raw text data of the problem.

[0293] Step 5:

[0294] The server sends the extracted worry text to the emotion engine. The text data is sent to the emotion engine's API, which analyzes emotions. For example, information such as "stressful words are used" is obtained. The input for this step is the worry text data, and the output is the emotion analysis results.

[0295] Step 6:

[0296] The server sends the results from the emotion engine to the natural language processing engine to generate a prompt sentence. Taking into account the input data and the emotional state, it generates a prompt sentence such as "Worry: I'm stressed at work and I don't know what to do.\nAdvice from my future self:" The input of this step is text data and the emotional state, and the output is a prompt sentence.

[0297] Step 7:

[0298] The server sends the generated prompt sentence to the AI ​​text generation engine. It requests the generation of advice based on the prompt sentence. The AI ​​text generation engine generates advice based on the prompt sentence. For example, the advice generated might be, "I'm sure you're feeling very stressed right now, but first consider taking a short break." The input of this step is the prompt sentence, and the output is the generated advice.

[0299] Step 8:

[0300] The server receives the generated advice and converts it back into JSON format. For example, the generated advice "I understand that you are under a lot of stress right now, but first consider taking a short break." is formatted as {"advice": "I understand that you are under a lot of stress right now, but first consider taking a short break."}. The input of this step is the generated advice, and the output is advice data in JSON format.

[0301] Step 9:

[0302] The server sends the advice JSON data to the terminal. The server sends the generated JSON-formatted advice to the terminal as an HTTP response. The input of this step is JSON-formatted advice data, and the output is an HTTP response to the terminal.

[0303] Step 10:

[0304] The device receives the advice and parses the JSON data. The device receives the HTTP response and parses the JSON data containing the advice. For example, the data {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."} is parsed and converted into a display format. The input of this step is the HTTP response, and the output is the parsed advice data.

[0305] Step 11:

[0306] The terminal displays the analyzed advice to the user. The analysis results are displayed on the screen so that the user can confirm the advice. For example, a message such as "I understand that you are under a lot of stress right now, but first consider taking a short break" is displayed on the screen. The input of this step is the analyzed advice data, and the output is the screen display.

[0307] (Application example 2)

[0308] 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."

[0309] Modern users have a wide range of concerns, and these concerns often depend on the user's emotional state. Conventional systems can analyze a user's concerns and provide general advice, but it is difficult to provide advice that fully takes into account the user's emotional state. Furthermore, when customers have questions or concerns about product selection, particularly in physical stores, responding appropriately and providing advice in real time has been a challenge. There is a need for a system that can solve these problems and provide more appropriate advice based on the user's emotional state.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0311] In this invention, the server includes means for receiving a concern input by a user, emotion analysis means for recognizing the emotional state of the user, means for generating a prompt taking the recognized emotional state into consideration, means for causing an AI text generation engine to generate advice based on the generated prompt, and means for displaying the received advice to the user. This makes it possible to provide more appropriate advice based on the user's emotional state and support the shopping experience, particularly in physical stores.

[0312] The "means for receiving the worries entered by the user" refers to a means for acquiring the worry information entered by the user in text or voice and transmitting it to the system.

[0313] A "natural language processing engine" is a technical engine that analyzes input text data and generates appropriate text based on the analysis results.

[0314] The "means for receiving advice" is a means for receiving the generated advice from the server and providing it to the user.

[0315] "Emotion analysis means" is a technology that identifies emotions from text data entered by a user and specifies that emotional state.

[0316] The "means for generating a prompt" is a technology that creates a prompt sentence to be sent to an artificial intelligence text generation engine based on the results of sentiment analysis.

[0317] An "artificial intelligence text generation engine" is an engine that generates appropriate advice or text for input information based on specified prompts.

[0318] The "means for displaying to the user" refers to a technology for displaying the received advice on the user's device, and has the function of providing information visually or audibly.

[0319] The present invention is a system that analyzes the worries entered by a user and provides appropriate advice based on the user's emotional state. This system is designed to support the shopping experience in brick-and-mortar stores. Detailed embodiments of the present invention are described below.

[0320] composition

[0321] Hardware:

[0322] Smart glasses: A device that allows users to input questions or concerns about products or services using voice and text. This device is equipped with an input interface, a communication module, and a display module.

[0323] Server: Includes a sentiment analyzer, a natural language processing engine, and an artificial intelligence text generation engine. The server receives input from the user, analyzes the data, and generates advice.

[0324] software:

[0325] Sentiment analysis means (e.g., AWS Comprehend, IBM Watson (registered trademark)): Analyzes text data entered by the user and identifies emotional states (stress, anxiety, joy, etc.).

[0326] Natural language processing engine (e.g., Google (registered trademark) NLP, spaCy): Generates prompt sentences based on the results of sentiment analysis.

[0327] Artificial intelligence text generation engine (e.g., OpenAI GPT-4 (registered trademark)): Creates appropriate advice based on the generated prompt text.

[0328] Implementation flow

[0329] 1. Users input their concerns through smart glasses

[0330] Example: A user speaks or texts, "I'm not sure if this dress looks good on me."

[0331] 2. The smart glasses convert the inputted concerns into JSON format.

[0332] Example: {"trouble": "I don't know if this dress looks good on me"}

[0333] 3. The smart glasses send the JSON data to the server

[0334] Send data using an HTTP POST request.

[0335] 4. The server receives and analyzes the data

[0336] The server parses the received JSON data and extracts the text of the user's concerns.

[0337] 5. The server uses emotion analysis methods

[0338] A sentiment analysis means is used to recognize the emotional state of the user.

[0339] 6. The server generates a prompt using a natural language processing engine

[0340] Example: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[0341] 7. The server sends a request to the AI ​​text generation engine

[0342] An artificial intelligence text generation engine generates advice based on prompts.

[0343] 8. The server receives the generated advice and converts it to JSON format.

[0344] The server converts the advice from the AI ​​text generation engine into JSON format.

[0345] 9. The server sends JSON data to the smart glasses

[0346] Send the data as an HTTP response.

[0347] 10. Smart glasses receive and display advice

[0348] The advice is displayed in the user's field of view.

[0349] Specific examples

[0350] When a user uses smart glasses to choose a dress in a store, if they input, "I don't know if this dress will suit me," the system can analyze the user's anxious emotional state and generate and display advice such as:

[0351] Example prompt sentence:

[0352] Problem: I don't know if this dress will suit me.

[0353] Emotion: Anxiety

[0354] advice:

[0355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0356] Step 1:

[0357] The user inputs their concerns through smart glasses.

[0358] Input: The user inputs their concerns by voice or text.

[0359] How it works: Capture worries using the input interface of smart glasses.

[0360] Output: The captured text data.

[0361] Step 2:

[0362] The device converts the input concerns into JSON format.

[0363] Input: The captured text data.

[0364] How it works: The smart glasses software parses the text data and converts it into JSON formatted data.

[0365] Output: For example, JSON data like {"trouble": "I don't know if this dress looks good on me"}.

[0366] Step 3:

[0367] The device sends JSON data to the server.

[0368] Input: JSON formatted worries data.

[0369] What it does: Sends data to the server using an HTTP POST request.

[0370] Output: The JSON data sent to the server.

[0371] Step 4:

[0372] The server parses the received JSON data.

[0373] Input: JSON data sent from the terminal.

[0374] How it works: The server receives the HTTP POST request and parses the data to extract the problem text.

[0375] Output: For example, the text "I don't know if this dress looks good on me."

[0376] Step 5:

[0377] The server utilizes emotion analysis means to recognize the user's emotional state.

[0378] Input: Parsed problem text.

[0379] How it works: The server uses a sentiment analysis tool (e.g., AWS Comprehend) to identify the emotional state from the text, e.g., "anxiety."

[0380] Output: Emotional state recognition results.

[0381] Step 6:

[0382] The server generates the prompt using a natural language processing engine.

[0383] Input: Emotional state recognition results and distress text.

[0384] How it works: The server uses a natural language processing engine (e.g., Google NLP) to generate a prompt: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[0385] Output: The prompt statement.

[0386] Step 7:

[0387] The server requests the artificial intelligence text generation engine to generate advice.

[0388] Input: The generated prompt statement.

[0389] How it works: The server sends a prompt to an artificial intelligence text generation engine (e.g., OpenAI GPT-4) to generate appropriate advice.

[0390] Output: Generated advice (e.g., "First, check if the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape.")

[0391] Step 8:

[0392] The server receives the generated advice and converts it to JSON format.

[0393] Input: The generated advice.

[0394] How it works: The server converts the advice text into JSON format.

[0395] Output: For example, {"advice": "First, check that the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape."}

[0396] Step 9:

[0397] The server sends the JSON data to the smart glasses.

[0398] Input: Advice data in JSON format.

[0399] What it does: Sends data as an HTTP response.

[0400] Output: JSON data sent to the smart glasses.

[0401] Step 10:

[0402] The terminal receives the advice and displays it to the user.

[0403] Input: JSON data received from the server.

[0404] How it works: The display module in the smart glasses analyzes the advice and displays it in the field of view.

[0405] Output: Advice visually displayed to the user.

[0406] 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.

[0407] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] 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.

[0409] [Second embodiment]

[0410] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0421] 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."

[0422] To implement this invention, a system is required in which a user inputs their concerns through a terminal, the server receives the input, performs a series of processes, and then displays advice to the user. The main components of this system include a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[0423] Program processing overview

[0424] 1. The user inputs their concerns on their device

[0425] The user enters their concerns into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0426] 2. The device sends the worries to the server

[0427] The device converts the worries entered into the input form into JSON format data and sends it to the server via an HTTP POST request. For example, the data sent to the server is {"trouble": "I'm stressed out at work and I don't know what to do"}.

[0428] 3. The server receives the message

[0429] The server receives the JSON data sent from the device, parses it, and extracts the parsed worry text.

[0430] 4. The server uses a natural language processing engine

[0431] The server sends the received text of the problem to a natural language processing engine, which understands the problem and generates prompts that provide appropriate advice.

[0432] 5. The server sends a request to the AI ​​text generation engine

[0433] The server uses the generated prompt to request an AI text generation engine to generate advice. For example, it sends a prompt such as "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0434] 6. AI text generation engine generates advice

[0435] The AI ​​text generation engine generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "I understand you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what's most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0436] 7. The server receives the advice and returns it to the device.

[0437] The server receives the advice returned by the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."}

[0438] 8. The device displays advice to the user

[0439] The device parses the JSON data received from the server and displays the advice to the user, who can then interpret the advice and use it to take action.

[0440] In this way, the system according to the present invention can analyze the worries entered by the user and quickly provide advice based on the analysis. This process allows the user to receive effective advice even without specialized knowledge or experience, thereby enabling rapid problem resolution.

[0441] The processing flow will be explained below.

[0442] Step 1:

[0443] Users input their concerns from their device

[0444] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0445] Step 2:

[0446] The device converts the concerns into JSON format.

[0447] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0448] Step 3:

[0449] The device sends JSON data to the server

[0450] The device sends the generated JSON data to the server using an HTTP POST request.

[0451] Step 4:

[0452] The server receives the JSON data

[0453] The server receives an HTTP POST request from the device and parses the JSON data containing the problem.

[0454] Step 5:

[0455] The server uses a natural language processing engine

[0456] The server sends the analyzed problem text to a natural language processing engine, which analyzes the text and generates a prompt.

[0457] Step 6:

[0458] The server sends a request to the artificial intelligence text generation engine

[0459] The server then requests the AI ​​text generation engine to generate advice based on the generated prompt. For example, it sends the prompt "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0460] Step 7:

[0461] Artificial intelligence text generation engine generates advice

[0462] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0463] Step 8:

[0464] The server receives the generated advice

[0465] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0466] Step 9:

[0467] The server sends JSON data to the terminal.

[0468] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0469] Step 10:

[0470] The device receives the advice

[0471] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."}

[0472] Step 11:

[0473] The device displays advice to the user

[0474] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0475] The above steps realize a system that allows users to receive prompt and appropriate advice on their concerns.

[0476] Example 1

[0477] 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."

[0478] In modern society, individual users have a wide range of problems, and there is a need for methods to quickly and effectively address them. However, users often need time and effort to seek professional advice, making it difficult to quickly resolve their problems. Furthermore, existing systems often lack the accuracy and efficiency to generate and provide appropriate advice for the problems entered by users.

[0479] 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.

[0480] In this invention, the server includes a means for receiving a problem input by a user, a means for transmitting the received problem to a natural language processing engine, and a means for receiving advice generated by the natural language processing engine. This makes it possible to analyze the problem input by the user and provide quick and effective advice using a generative AI model. Furthermore, by adding a means for converting the generated advice into JSON format and transmitting it to the terminal, the user can receive the advice in a format that is easy to understand and implement. This enables quick and effective problem solving for each user's problem.

[0481] A "user" is an individual or group that uses the system to input their concerns and receive advice.

[0482] A "terminal" is a hardware device or software application that a user uses to input concerns and receive advice.

[0483] A "server" is a central processing unit or system that receives input of concerns from users and performs processing for analysis and advice generation.

[0484] A "natural language processing engine" is a processing engine that includes algorithms and technologies for analyzing text data and understanding its meaning.

[0485] A "generative AI model" is an artificial intelligence algorithm and model that generates appropriate advice based on input text data.

[0486] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data description language widely used as a data exchange format.

[0487] A "prompt" refers to pre-defined text used to prompt a generative AI model to generate advice.

[0488] "Means of analysis" refers to the processes and techniques used to interpret the text of the user's concerns and extract the necessary information.

[0489] "Advice" refers to useful information or suggestions that the generative AI model provides to address the user's concerns.

[0490] A "transmission means" is a method or protocol for moving data from one device or system to another.

[0491] This invention is a system that analyzes user-entered concerns and generates and provides advice based on the analysis. This system includes a user terminal, a server, a natural language processing engine, and a generative AI model.

[0492] Users input their concerns using a device. User devices can be smartphones, tablets, personal computers, etc. By typing their concerns into the input form and submitting it, the concerns are imported into the system. For example, a user might input, "I'm stressed out at work and I don't know what to do."

[0493] The terminal receives input from the user and converts it into JSON format, generating the following data: {"trouble": "I'm stressed at work and I don't know what to do"}. The generated JSON data is then sent to the server using an HTTP POST request.

[0494] The server receives and analyzes the JSON data sent from the device. A natural language processing engine is used for the analysis to understand the meaning of the text data. At this time, the server sends the text data of the worries to the natural language processing engine and requests it to analyze it to understand the content of the worries.

[0495] The server generates a prompt based on the analysis results and sends it to the generative AI model. The generative AI model uses the prompt to generate advice. For example, a prompt could be "Worry: I'm stressed out at work and I don't know what to do.\nAdvice from my future self:"

[0496] Based on this prompt, the generative AI model generates appropriate advice and sends it back to the server. An example of generated advice is, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. It is also important not to keep it to yourself, but to talk to your colleagues or superiors."

[0497] The server converts the received advice into JSON format and sends it to the device. For example, the following data is generated: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."

[0498] Finally, the device analyzes the received JSON data and displays advice to the user. The user can then confirm the displayed advice and use it to take practical action. In this way, the system according to the present invention can analyze the concerns entered by the user and quickly provide advice based on the analysis. This allows users to receive effective advice and quickly solve problems, even if they do not have specialized knowledge or experience.

[0499] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0500] Step 1:

[0501] The user enters their worries into an input form on the device. The entered worries are saved as text data on the device. As an example of input, the user might type, "I'm stressed out at work and I don't know what to do." The specific user interaction is the action of clicking the "Send" button on the device screen. The input data is the worries in text format.

[0502] Step 2:

[0503] The device converts the received text data into JSON format. For example, it generates JSON data such as {"trouble": "I'm stressed at work and I don't know what to do"}. The device then sends this JSON data to the server via an HTTP POST request. The input data is the text-formatted problem, and the output is JSON format data.

[0504] Step 3:

[0505] The server receives the JSON data sent from the device. The server parses this data and extracts the relevant text data. Specifically, it uses the JSON.parse function to parse the data {"trouble": "I'm stressed at work and I don't know what to do"}. The input data is JSON format data, and the output is the parsed text data.

[0506] Step 4:

[0507] The server sends the extracted text data to a natural language processing engine. The natural language processing engine analyzes the received text data and understands its meaning. In this process, for example, a POST request is sent to the API endpoint of the natural language processing engine. The input data is the text-formatted problem data, and the output is the analysis results.

[0508] Step 5:

[0509] The server generates a prompt based on the analysis results returned by the natural language processing engine. The generated prompt is then sent to a generative AI model, requesting it to generate advice. Specifically, it generates a prompt in the format "Problem: I'm stressed at work and don't know what to do.\nAdvice from my future self:" and sends it to the generative AI model. The input data is the text of the analysis results, and the output is the generated prompt.

[0510] Step 6:

[0511] The generative AI model generates advice based on the received prompt. For example, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors." The generated advice is then sent back to the server. The input data is the prompt, and the output is the generated advice text.

[0512] Step 7:

[0513] The server receives the advice returned from the generative AI model. It converts the received advice back into JSON format and sends it to the device. For example, it converts it into the following format: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."} and sends it. The input data is the advice text, and the output is the advice data in JSON format.

[0514] Step 8:

[0515] The device parses the JSON data received from the server. The parsed data is displayed to the user through the user interface. The user reads the displayed advice and puts it into action. The input data is advice data in JSON format, and the output is text advice that is displayed to the user. Specifically, the device application displays the advice on the screen.

[0516] (Application example 1)

[0517] 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."

[0518] The purpose of this invention is to provide a support system that enables users with home security concerns to solve their problems effectively and quickly. Specifically, the objective is to establish a technical means for providing appropriate and professional advice immediately to users in response to their anxieties and concerns.

[0519] 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.

[0520] In this invention, the server includes means for receiving a concern input by a user, means for transmitting the received concern to a natural language processing engine, means for receiving advice generated by the natural language processing engine, means for displaying the received advice to the user, and means for providing a solution to the home security concern. This enables the security concern input by the user to be analyzed and appropriate advice provided by AI to be promptly received.

[0521] A "user" is someone who uses the system to input their concerns and receive advice.

[0522] A "problem" is a problem or anxiety that a user has, or an issue that the user wants to solve.

[0523] "Means for receiving" refers to the method or process by which the server obtains the worries entered by the user and the advice generated by the AI ​​engine.

[0524] A "natural language processing engine" is a collection of information processing technologies that analyzes text information entered by a user and understands its meaning and intent.

[0525] An "artificial intelligence text generation engine" is a technology that generates appropriate advice for users based on the analysis results of a natural language processing engine.

[0526] "Advice" refers to advice or solutions provided to users for their concerns.

[0527] "Home security" refers to the efforts and measures taken to ensure the safety of the home environment.

[0528] "Solution provision means" refers to a method or process that uses a natural language processing engine or an artificial intelligence text generation engine to provide specific advice or instructions based on the problem entered by the user.

[0529] To implement this invention, a system is required to provide appropriate advice to users regarding their home security concerns. This system is composed of a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[0530] 1. Program Generation

[0531] In this invention, the server includes the following means:

[0532] A means of receiving user-entered concerns

[0533] A means of sending received concerns to a natural language processing engine

[0534] A means of generating advice based on prompts generated by a natural language processing engine

[0535] A means of displaying received advice to the user

[0536] A means of providing solutions to home security concerns

[0537] 2. Hardware and Software Used

[0538] Hardware:

[0539] User devices: smartphones, tablets, computers, etc.

[0540] Server: Linux-based server or cloud server (e.g. AWS EC2 instance)

[0541] software:

[0542] Natural language processing engine: an engine for analyzing user input text (e.g., SpaCy, NLTK)

[0543] Artificial intelligence text generation engine: An engine for generating advice based on analysis results (e.g., OpenAI GPT-3)

[0544] Data processing and calculation:

[0545] Converts user input into JSON format and sends it to the server

[0546] The server uses a natural language processing engine to analyze the input text and generate an appropriate prompt.

[0547] Use prompts to let an artificial intelligence text generation engine generate advice

[0548] The generated advice is converted to JSON format and sent back to the user device.

[0549] The user device analyzes the received advice and displays it on the screen.

[0550] 3. Specific Examples

[0551] When a user inputs "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried," the system operates as follows:

[0552] The server sends the user's input to a natural language processing engine, which generates a prompt such as, "I feel like there's a suspicious person walking around my house at night. I'm worried. What home security advice do I need?"

[0553] The AI ​​text generation engine generates advice based on the prompts, such as, "First, install an outdoor camera and set it up to send notifications when movement is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[0554] Example prompt sentence:

[0555] Problem: I feel like there is a suspicious person walking around my house in the middle of the night. It makes me uneasy.

[0556] Any advice please:

[0557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0558] Step 1:

[0559] The user inputs their concerns. There is an input form on the user's device, and they can enter specific concerns, such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried." This input is sent to the server as JSON format data.

[0560] Step 2:

[0561] The device sends the worries to the server. The user device converts the worries into JSON format data and sends it to the server via an HTTP POST request. For example, the following data is sent to the server: {"trouble": "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried."}

[0562] Step 3:

[0563] The server receives the concerns. The server receives the JSON data sent from the device, analyzes it, and extracts the concerns. At this time, it classifies the concerns entered by the user into security-related categories.

[0564] Step 4:

[0565] The server uses a natural language processing engine. The server sends the received worry text to the natural language processing engine. This process analyzes the worry text and generates an appropriate prompt. For example, it generates a prompt such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried. Please tell me what home security advice I need."

[0566] Step 5:

[0567] The server sends a request to the artificial intelligence text generation engine. The server uses the generated prompt to request the artificial intelligence text generation engine to generate advice. The server sends a request with the prompt to the generative AI model and receives the generated advice text.

[0568] Step 6:

[0569] An artificial intelligence text generation engine generates advice. The generative AI model generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "First, install an outdoor camera and set it up to send notifications when motion is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[0570] Step 7:

[0571] The server receives the advice and sends it back to the device. The server receives the advice returned from the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends the following data: {"advice": "First, install an outdoor camera and set it up so that it sends notifications when it detects movement. It is also important to strengthen cooperation with neighboring residents and share information with each other."}

[0572] Step 8:

[0573] The device displays the advice to the user. The user's device parses the JSON data received from the server and displays the advice on the screen. The user can read the presented advice and use it to take action.

[0574] 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.

[0575] This invention is a system that analyzes user inputted worries and provides appropriate advice based on them. This system is characterized by its ability to recognize the user's emotional state and generate advice that takes this into consideration by combining it with an emotion engine. The main components of the system include a user terminal, a server, a natural language processing engine, an artificial intelligence text generation engine, and the emotion engine.

[0576] Program processing overview

[0577] 1. The user inputs their concerns on their device

[0578] The user enters their worries in text into the input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0579] 2. The device converts the concerns into JSON format

[0580] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0581] 3. The device sends JSON data to the server

[0582] The device sends the generated JSON data to the server using an HTTP POST request.

[0583] 4. The server receives the JSON data

[0584] The server receives an HTTP POST request from the device, parses the JSON data containing the worries, and extracts the parsed worry text.

[0585] 5. The server uses the emotion engine

[0586] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the user's emotion (e.g., stress, anxiety, joy, etc.).

[0587] 6. The server uses a natural language processing engine

[0588] The server takes into account the recognized emotional state and sends the text of the concern to a natural language processing engine to generate a prompt, for example, "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0589] 7. The server sends a request to the AI ​​text generation engine

[0590] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[0591] 8. AI text generation engine generates advice

[0592] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0593] 9. The server receives the generated advice

[0594] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0595] 10. The server sends JSON data to the device

[0596] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0597] 11. The device receives the advice

[0598] The device receives the HTTP response from the server and parses the JSON data containing advice, such as {"advice": "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."}

[0599] 12. The device displays advice to the user

[0600] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0601] In this way, advice is provided that takes into account the user's emotional state, allowing for more relevant and effective advice than conventional systems, helping users solve their problems more quickly and accurately.

[0602] The processing flow will be explained below.

[0603] Step 1:

[0604] Users input their concerns from their device

[0605] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0606] Step 2:

[0607] The device converts the concerns into JSON format.

[0608] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0609] Step 3:

[0610] The device sends JSON data to the server

[0611] The device sends the generated JSON data to the server using an HTTP POST request.

[0612] Step 4:

[0613] The server receives the JSON data

[0614] The server receives an HTTP POST request from the device and parses the JSON data containing the worries. By parsing the data, the worry text is extracted.

[0615] Step 5:

[0616] The server uses the emotion engine

[0617] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the emotional state, such as "stress" or "anxiety."

[0618] Step 6:

[0619] The server uses a natural language processing engine

[0620] The server sends the recognized emotional state and the text of the concern to a natural language processing engine to generate a prompt, such as "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0621] Step 7:

[0622] The server sends a request to the artificial intelligence text generation engine

[0623] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[0624] Step 8:

[0625] Artificial intelligence text generation engine generates advice

[0626] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you are feeling very stressed right now, but first, take a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0627] Step 9:

[0628] The server receives the generated advice

[0629] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0630] Step 10:

[0631] The server sends JSON data to the terminal.

[0632] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0633] Step 11:

[0634] The device receives the advice

[0635] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I understand you're under a lot of stress right now, but first take a short break. Then, re-prioritize your work and reaffirm what's most important to you. It's also important not to keep it to yourself, but to talk to a colleague or your boss about it."}

[0636] Step 12:

[0637] The device displays advice to the user

[0638] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0639] The above specific processing steps realize a system that allows a user to quickly obtain appropriate advice that takes into account the user's emotional state.

[0640] Example 2

[0641] 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."

[0642] Conventional systems have had difficulty providing appropriate and effective advice for the concerns entered by users. In particular, methods that generate general advice without considering the user's emotional state make it difficult to provide effective advice tailored to the user's individual situation. For this reason, there has been a demand for effective problem-solving and psychological support for users.

[0643] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving a concern input by a user; means for converting the received concern into JSON-formatted data; means for transmitting the JSON-formatted data to the server; means for analyzing the JSON data received by the server; means for transmitting the text of the concern to an emotion engine to recognize the user's emotional state; means for transmitting the text of the concern to a natural language processing engine to generate a prompt sentence based on the recognized emotional state; means for requesting an AI text generation engine to generate advice based on the generated prompt sentence; means for receiving the generated advice and converting it back into JSON format; means for transmitting the JSON data of the advice from the server to the terminal; and means for analyzing the JSON data of the advice received by the terminal and displaying it to the user. This enables more appropriate and effective advice that takes the user's emotional state into consideration.

[0644] "User" refers to the person who uses this system and inputs their concerns via a terminal.

[0645] A "terminal" is a device that allows a user to input their concerns and send them to the system, and includes smartphones, tablets, PCs, etc.

[0646] A "problem" is text data that a user enters into the system, expressing a particular problem or difficulty.

[0647] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight text format widely used as a data exchange format.

[0648] The "server" is a central system that receives data sent from the terminals, analyzes and processes it, and generates final advice.

[0649] A "natural language processing engine" is software or a system that analyzes text data entered by a user, understands its meaning, and generates prompt sentences.

[0650] An "emotion engine" is a system that functions as part of natural language processing and analyzes and recognizes emotional states from text entered by the user.

[0651] A "prompt sentence" is text data input to an artificial intelligence text generation engine, and is the sentence that serves as the basis for the generated advice.

[0652] An "artificial intelligence text generation engine" is an engine that generates advice to be provided to a user based on a prompt sentence.

[0653] An "HTTP POST request" is a type of network protocol for sending data to a server, and is used in this system to send data from a terminal to a server.

[0654] A "response" is a response message sent from the server to the terminal, and includes JSON data of the advice.

[0655] "Analysis" is the process of breaking down and analyzing data to understand its meaning and structure.

[0656] "Display" refers to the act of showing information to the user on the screen of a terminal.

[0657] This invention is a system that analyzes worries entered by a user and provides appropriate advice based on the analysis. This system is characterized by the use of an emotion engine to recognize the user's emotional state and generate advice that takes this into consideration. Below, we will explain how to specifically implement this system.

[0658] First, the devices used by users include smartphones, tablets, and PCs. Users enter their concerns in text form on these devices. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0659] The device then converts the user's input into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}. The converted data is then sent to the server using an HTTP POST request over the internet.

[0660] The server receives the HTTP POST request sent from the device. After receiving it, the server analyzes the JSON data included in the request body and extracts the text portion of the trouble. For example, the text "I'm stressed out at work and I don't know what to do" is extracted from {"trouble": "I'm stressed out at work and I don't know what to do"}.

[0661] The server then sends the extracted text to the emotion engine, which uses natural language processing technology to analyze the text and classify the user's emotions. For example, it can recognize a state of stress from the keyword "stressed."

[0662] Based on the recognized emotional state, the server sends the text to a natural language processing engine to generate a prompt, such as "Worry: I'm stressed at work and don't know what to do.\nAdvice from my future self:"

[0663] Based on the generated prompt, the server requests an AI text generation engine to generate advice. The prompt also includes the user's emotional state, allowing for more personalized advice. For example, advice may be provided in the form of "advice from your future self."

[0664] The AI ​​text generation engine generates advice based on the prompt, such as, "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."

[0665] The generated advice is returned to the server and converted back into JSON format. The server then sends the generated JSON format advice to the device as an HTTP response. For example, it could be formatted as {"advice": "I understand you're under a lot of stress right now, but first consider taking a short break..."}.

[0666] The device receives the HTTP response from the server and parses the JSON data containing advice, for example, {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."}

[0667] Finally, the device displays the analyzed advice on the user's screen. For example, a message such as "I understand you are feeling very stressed right now, but first consider taking a short break" can be displayed. The user can confirm the advice and use it to take action.

[0668] In this way, the present invention can provide appropriate and effective advice that takes into account the user's emotional state.

[0669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0670] Step 1:

[0671] The user inputs their worries from the terminal. Specifically, the user inputs their worries as text into the input form on the terminal. After inputting, the user presses the send button. An example of input is "I'm stressed out at work and I don't know what to do." The input in this step is the text of the user's worries, and the output is stored as text data on the terminal.

[0672] Step 2:

[0673] The device converts the worries into JSON format. The input text is analyzed by a program and converted into JSON format. For example, the input text "I'm stressed out at work and I don't know what to do" is formatted as {"trouble": "I'm stressed out at work and I don't know what to do"}. The input for this step is the user's text data, and the output is JSON format data.

[0674] Step 3:

[0675] The device sends JSON data to the server. The device sends the generated JSON data to the server using an HTTP POST request. The device issues a request to the specified endpoint on the server. The input for this step is JSON-formatted data, and the output is an HTTP request to the server.

[0676] Step 4:

[0677] The server receives the JSON data. The server receives the HTTP POST request and analyzes the received content. The JSON data included in the request body is analyzed and the text portion of the problem is extracted from it. For example, from {"trouble": "I'm stressed out at work and I don't know what to do"}, "I'm stressed out at work and I don't know what to do" is extracted. The input of this step is the HTTP request, and the output is the raw text data of the problem.

[0678] Step 5:

[0679] The server sends the extracted worry text to the emotion engine. The text data is sent to the emotion engine's API, which analyzes emotions. For example, information such as "stressful words are used" is obtained. The input for this step is the worry text data, and the output is the emotion analysis results.

[0680] Step 6:

[0681] The server sends the results from the emotion engine to the natural language processing engine to generate a prompt sentence. Taking into account the input data and the emotional state, it generates a prompt sentence such as "Worry: I'm stressed at work and I don't know what to do.\nAdvice from my future self:" The input of this step is text data and the emotional state, and the output is a prompt sentence.

[0682] Step 7:

[0683] The server sends the generated prompt sentence to the AI ​​text generation engine. It requests the generation of advice based on the prompt sentence. The AI ​​text generation engine generates advice based on the prompt sentence. For example, the advice generated might be, "I'm sure you're feeling very stressed right now, but first consider taking a short break." The input of this step is the prompt sentence, and the output is the generated advice.

[0684] Step 8:

[0685] The server receives the generated advice and converts it back into JSON format. For example, the generated advice "I understand that you are under a lot of stress right now, but first consider taking a short break." is formatted as {"advice": "I understand that you are under a lot of stress right now, but first consider taking a short break."}. The input of this step is the generated advice, and the output is advice data in JSON format.

[0686] Step 9:

[0687] The server sends the advice JSON data to the terminal. The server sends the generated JSON-formatted advice to the terminal as an HTTP response. The input of this step is JSON-formatted advice data, and the output is an HTTP response to the terminal.

[0688] Step 10:

[0689] The device receives the advice and parses the JSON data. The device receives the HTTP response and parses the JSON data containing the advice. For example, the data {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."} is parsed and converted into a display format. The input of this step is the HTTP response, and the output is the parsed advice data.

[0690] Step 11:

[0691] The terminal displays the analyzed advice to the user. The analysis results are displayed on the screen so that the user can confirm the advice. For example, a message such as "I understand that you are under a lot of stress right now, but first consider taking a short break" is displayed on the screen. The input of this step is the analyzed advice data, and the output is the screen display.

[0692] (Application example 2)

[0693] 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."

[0694] Modern users have a wide range of concerns, and these concerns often depend on the user's emotional state. Conventional systems can analyze a user's concerns and provide general advice, but it is difficult to provide advice that fully takes into account the user's emotional state. Furthermore, when customers have questions or concerns about product selection, particularly in physical stores, responding appropriately and providing advice in real time has been a challenge. There is a need for a system that can solve these problems and provide more appropriate advice based on the user's emotional state.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0696] In this invention, the server includes means for receiving a concern input by a user, emotion analysis means for recognizing the emotional state of the user, means for generating a prompt taking the recognized emotional state into consideration, means for causing an AI text generation engine to generate advice based on the generated prompt, and means for displaying the received advice to the user. This makes it possible to provide more appropriate advice based on the user's emotional state and support the shopping experience, particularly in physical stores.

[0697] The "means for receiving the worries entered by the user" refers to a means for acquiring the worry information entered by the user in text or voice and transmitting it to the system.

[0698] A "natural language processing engine" is a technical engine that analyzes input text data and generates appropriate text based on the analysis results.

[0699] The "means for receiving advice" is a means for receiving the generated advice from the server and providing it to the user.

[0700] "Emotion analysis means" is a technology that identifies emotions from text data entered by a user and specifies that emotional state.

[0701] The "means for generating a prompt" is a technology that creates a prompt sentence to be sent to an artificial intelligence text generation engine based on the results of sentiment analysis.

[0702] An "artificial intelligence text generation engine" is an engine that generates appropriate advice or text for input information based on specified prompts.

[0703] The "means for displaying to the user" refers to a technology for displaying the received advice on the user's device, and has the function of providing information visually or audibly.

[0704] The present invention is a system that analyzes the worries entered by a user and provides appropriate advice based on the user's emotional state. This system is designed to support the shopping experience in brick-and-mortar stores. Detailed embodiments of the present invention are described below.

[0705] composition

[0706] Hardware:

[0707] Smart glasses: A device that allows users to input questions or concerns about products or services using voice and text. This device is equipped with an input interface, a communication module, and a display module.

[0708] Server: Includes a sentiment analyzer, a natural language processing engine, and an artificial intelligence text generation engine. The server receives input from the user, analyzes the data, and generates advice.

[0709] software:

[0710] Sentiment analysis tools (e.g., AWS Comprehend, IBM Watson): Analyzes text data entered by the user and identifies their emotional state (e.g., stress, anxiety, joy).

[0711] Natural language processing engine (e.g., Google NLP, spaCy): Generates prompt sentences based on the results of sentiment analysis.

[0712] Artificial intelligence text generation engine (e.g., OpenAI GPT-4): Creates appropriate advice based on the generated prompt.

[0713] Implementation flow

[0714] 1. Users input their concerns through smart glasses

[0715] Example: A user speaks or texts, "I'm not sure if this dress looks good on me."

[0716] 2. The smart glasses convert the inputted concerns into JSON format.

[0717] Example: {"trouble": "I don't know if this dress looks good on me"}

[0718] 3. The smart glasses send the JSON data to the server

[0719] Send data using an HTTP POST request.

[0720] 4. The server receives and analyzes the data

[0721] The server parses the received JSON data and extracts the text of the user's concerns.

[0722] 5. The server uses emotion analysis methods

[0723] A sentiment analysis means is used to recognize the emotional state of the user.

[0724] 6. The server generates a prompt using a natural language processing engine

[0725] Example: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[0726] 7. The server sends a request to the AI ​​text generation engine

[0727] An artificial intelligence text generation engine generates advice based on prompts.

[0728] 8. The server receives the generated advice and converts it to JSON format.

[0729] The server converts the advice from the AI ​​text generation engine into JSON format.

[0730] 9. The server sends JSON data to the smart glasses

[0731] Send the data as an HTTP response.

[0732] 10. Smart glasses receive and display advice

[0733] The advice is displayed in the user's field of view.

[0734] Specific examples

[0735] When a user uses smart glasses to choose a dress in a store, if they input, "I don't know if this dress will suit me," the system can analyze the user's anxious emotional state and generate and display advice such as:

[0736] Example prompt sentence:

[0737] Problem: I don't know if this dress will suit me.

[0738] Emotion: Anxiety

[0739] advice:

[0740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0741] Step 1:

[0742] The user inputs their concerns through smart glasses.

[0743] Input: The user inputs their concerns by voice or text.

[0744] How it works: Capture worries using the input interface of smart glasses.

[0745] Output: The captured text data.

[0746] Step 2:

[0747] The device converts the input concerns into JSON format.

[0748] Input: The captured text data.

[0749] How it works: The smart glasses software parses the text data and converts it into JSON formatted data.

[0750] Output: For example, JSON data like {"trouble": "I don't know if this dress looks good on me"}.

[0751] Step 3:

[0752] The device sends JSON data to the server.

[0753] Input: JSON formatted worries data.

[0754] What it does: Sends data to the server using an HTTP POST request.

[0755] Output: The JSON data sent to the server.

[0756] Step 4:

[0757] The server parses the received JSON data.

[0758] Input: JSON data sent from the terminal.

[0759] How it works: The server receives the HTTP POST request and parses the data to extract the problem text.

[0760] Output: For example, the text "I don't know if this dress looks good on me."

[0761] Step 5:

[0762] The server utilizes emotion analysis means to recognize the user's emotional state.

[0763] Input: Parsed problem text.

[0764] How it works: The server uses a sentiment analysis tool (e.g., AWS Comprehend) to identify the emotional state from the text, e.g., "anxiety."

[0765] Output: Emotional state recognition results.

[0766] Step 6:

[0767] The server generates the prompt using a natural language processing engine.

[0768] Input: Emotional state recognition results and distress text.

[0769] How it works: The server uses a natural language processing engine (e.g., Google NLP) to generate a prompt: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[0770] Output: The prompt statement.

[0771] Step 7:

[0772] The server requests the artificial intelligence text generation engine to generate advice.

[0773] Input: The generated prompt statement.

[0774] How it works: The server sends a prompt to an artificial intelligence text generation engine (e.g., OpenAI GPT-4) to generate appropriate advice.

[0775] Output: Generated advice (e.g., "First, check if the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape.")

[0776] Step 8:

[0777] The server receives the generated advice and converts it to JSON format.

[0778] Input: The generated advice.

[0779] How it works: The server converts the advice text into JSON format.

[0780] Output: For example, {"advice": "First, check that the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape."}

[0781] Step 9:

[0782] The server sends the JSON data to the smart glasses.

[0783] Input: Advice data in JSON format.

[0784] What it does: Sends data as an HTTP response.

[0785] Output: JSON data sent to the smart glasses.

[0786] Step 10:

[0787] The terminal receives the advice and displays it to the user.

[0788] Input: JSON data received from the server.

[0789] How it works: The display module in the smart glasses analyzes the advice and displays it in the field of view.

[0790] Output: Advice visually displayed to the user.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] [Third embodiment]

[0795] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0796] 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.

[0797] 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).

[0798] 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.

[0799] 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.

[0800] 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).

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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."

[0807] To implement this invention, a system is required in which a user inputs their concerns through a terminal, the server receives the input, performs a series of processes, and then displays advice to the user. The main components of this system include a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[0808] Program processing overview

[0809] 1. The user inputs their concerns on the device

[0810] The user enters their concerns into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0811] 2. The device sends the worries to the server

[0812] The device converts the worries entered into the input form into JSON format data and sends it to the server via an HTTP POST request. For example, the data sent to the server is {"trouble": "I'm stressed out at work and I don't know what to do"}.

[0813] 3. The server receives the message

[0814] The server receives the JSON data sent from the device, parses it, and extracts the parsed worry text.

[0815] 4. The server uses a natural language processing engine

[0816] The server sends the received text of the problem to a natural language processing engine, which understands the problem and generates prompts that provide appropriate advice.

[0817] 5. The server sends a request to the AI ​​text generation engine

[0818] The server uses the generated prompt to request an AI text generation engine to generate advice. For example, it sends a prompt such as "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0819] 6. AI text generation engine generates advice

[0820] The AI ​​text generation engine generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "I understand you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what's most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0821] 7. The server receives the advice and returns it to the device.

[0822] The server receives the advice returned by the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."}

[0823] 8. The device displays advice to the user

[0824] The device parses the JSON data received from the server and displays the advice to the user, who can then interpret the advice and use it to take action.

[0825] In this way, the system according to the present invention can analyze the worries entered by the user and quickly provide advice based on the analysis. This process allows the user to receive effective advice even without specialized knowledge or experience, thereby enabling rapid problem resolution.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] Users input their concerns from their device

[0829] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0830] Step 2:

[0831] The device converts the concerns into JSON format.

[0832] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0833] Step 3:

[0834] The device sends JSON data to the server

[0835] The device sends the generated JSON data to the server using an HTTP POST request.

[0836] Step 4:

[0837] The server receives the JSON data

[0838] The server receives an HTTP POST request from the device and parses the JSON data containing the problem.

[0839] Step 5:

[0840] The server uses a natural language processing engine

[0841] The server sends the analyzed problem text to a natural language processing engine, which analyzes the text and generates a prompt.

[0842] Step 6:

[0843] The server sends a request to the artificial intelligence text generation engine

[0844] The server then requests the AI ​​text generation engine to generate advice based on the generated prompt. For example, it sends the prompt "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0845] Step 7:

[0846] Artificial intelligence text generation engine generates advice

[0847] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0848] Step 8:

[0849] The server receives the generated advice

[0850] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0851] Step 9:

[0852] The server sends JSON data to the terminal.

[0853] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0854] Step 10:

[0855] The device receives the advice

[0856] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."}

[0857] Step 11:

[0858] The device displays advice to the user

[0859] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0860] The above steps realize a system that allows users to receive prompt and appropriate advice on their concerns.

[0861] Example 1

[0862] 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."

[0863] In modern society, individual users have a wide range of problems, and there is a need for methods to quickly and effectively address them. However, users often need time and effort to seek professional advice, making it difficult to quickly resolve their problems. Furthermore, existing systems often lack the accuracy and efficiency to generate and provide appropriate advice for the problems entered by users.

[0864] 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.

[0865] In this invention, the server includes a means for receiving a problem input by a user, a means for transmitting the received problem to a natural language processing engine, and a means for receiving advice generated by the natural language processing engine. This makes it possible to analyze the problem input by the user and provide quick and effective advice using a generative AI model. Furthermore, by adding a means for converting the generated advice into JSON format and transmitting it to the terminal, the user can receive the advice in a format that is easy to understand and implement. This enables quick and effective problem solving for each user's problem.

[0866] A "user" is an individual or group that uses the system to input their concerns and receive advice.

[0867] A "terminal" is a hardware device or software application that a user uses to input concerns and receive advice.

[0868] A "server" is a central processing unit or system that receives input of concerns from users and performs processing for analysis and advice generation.

[0869] A "natural language processing engine" is a processing engine that includes algorithms and technologies for analyzing text data and understanding its meaning.

[0870] A "generative AI model" is an artificial intelligence algorithm and model that generates appropriate advice based on input text data.

[0871] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data description language widely used as a data exchange format.

[0872] A "prompt" refers to pre-defined text used to prompt a generative AI model to generate advice.

[0873] "Means of analysis" refers to the processes and techniques used to interpret the text of the user's concerns and extract the necessary information.

[0874] "Advice" refers to useful information or suggestions that the generative AI model provides to address the user's concerns.

[0875] A "transmission means" is a method or protocol for moving data from one device or system to another.

[0876] This invention is a system that analyzes user-entered concerns and generates and provides advice based on the analysis. This system includes a user terminal, a server, a natural language processing engine, and a generative AI model.

[0877] Users input their concerns using a device. User devices can be smartphones, tablets, personal computers, etc. By typing their concerns into the input form and submitting it, the concerns are imported into the system. For example, a user might input, "I'm stressed out at work and I don't know what to do."

[0878] The terminal receives input from the user and converts it into JSON format, generating the following data: {"trouble": "I'm stressed at work and I don't know what to do"}. The generated JSON data is then sent to the server using an HTTP POST request.

[0879] The server receives and analyzes the JSON data sent from the device. A natural language processing engine is used for the analysis to understand the meaning of the text data. At this time, the server sends the text data of the worries to the natural language processing engine and requests it to analyze it to understand the content of the worries.

[0880] The server generates a prompt based on the analysis results and sends it to the generative AI model. The generative AI model uses the prompt to generate advice. For example, a prompt could be "Worry: I'm stressed out at work and I don't know what to do.\nAdvice from my future self:"

[0881] Based on this prompt, the generative AI model generates appropriate advice and sends it back to the server. An example of generated advice is, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. It is also important not to keep it to yourself, but to talk to your colleagues or superiors."

[0882] The server converts the received advice into JSON format and sends it to the device. For example, the following data is generated: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."

[0883] Finally, the device analyzes the received JSON data and displays advice to the user. The user can then confirm the displayed advice and use it to take practical action. In this way, the system according to the present invention can analyze the concerns entered by the user and quickly provide advice based on the analysis. This allows users to receive effective advice and quickly solve problems, even if they do not have specialized knowledge or experience.

[0884] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0885] Step 1:

[0886] The user enters their worries into an input form on the device. The entered worries are saved as text data on the device. As an example of input, the user might type, "I'm stressed out at work and I don't know what to do." The specific user interaction is the action of clicking the "Send" button on the device screen. The input data is the worries in text format.

[0887] Step 2:

[0888] The device converts the received text data into JSON format. For example, it generates JSON data such as {"trouble": "I'm stressed at work and I don't know what to do"}. The device then sends this JSON data to the server via an HTTP POST request. The input data is the text-formatted problem, and the output is JSON format data.

[0889] Step 3:

[0890] The server receives the JSON data sent from the device. The server parses this data and extracts the relevant text data. Specifically, it uses the JSON.parse function to parse the data {"trouble": "I'm stressed at work and I don't know what to do"}. The input data is JSON format data, and the output is the parsed text data.

[0891] Step 4:

[0892] The server sends the extracted text data to a natural language processing engine. The natural language processing engine analyzes the received text data and understands its meaning. In this process, for example, a POST request is sent to the API endpoint of the natural language processing engine. The input data is the text-formatted problem data, and the output is the analysis results.

[0893] Step 5:

[0894] The server generates a prompt based on the analysis results returned by the natural language processing engine. The generated prompt is then sent to a generative AI model, requesting it to generate advice. Specifically, it generates a prompt in the format "Problem: I'm stressed at work and don't know what to do.\nAdvice from my future self:" and sends it to the generative AI model. The input data is the text of the analysis results, and the output is the generated prompt.

[0895] Step 6:

[0896] The generative AI model generates advice based on the received prompt. For example, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors." The generated advice is then sent back to the server. The input data is the prompt, and the output is the generated advice text.

[0897] Step 7:

[0898] The server receives the advice returned from the generative AI model. It converts the received advice back into JSON format and sends it to the device. For example, it converts it into the following format: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."} and sends it. The input data is the advice text, and the output is the advice data in JSON format.

[0899] Step 8:

[0900] The device parses the JSON data received from the server. The parsed data is displayed to the user through the user interface. The user reads the displayed advice and puts it into action. The input data is advice data in JSON format, and the output is text advice that is displayed to the user. Specifically, the device application displays the advice on the screen.

[0901] (Application example 1)

[0902] 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."

[0903] The purpose of this invention is to provide a support system that enables users with home security concerns to solve their problems effectively and quickly. Specifically, the objective is to establish a technical means for providing appropriate and professional advice immediately to users in response to their anxieties and concerns.

[0904] 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.

[0905] In this invention, the server includes means for receiving a concern input by a user, means for transmitting the received concern to a natural language processing engine, means for receiving advice generated by the natural language processing engine, means for displaying the received advice to the user, and means for providing a solution to the home security concern. This enables the security concern input by the user to be analyzed and appropriate advice provided by AI to be promptly received.

[0906] A "user" is someone who uses the system to input their concerns and receive advice.

[0907] A "problem" is a problem or anxiety that a user has, or an issue that the user wants to solve.

[0908] "Means for receiving" refers to the method or process by which the server obtains the worries entered by the user and the advice generated by the AI ​​engine.

[0909] A "natural language processing engine" is a collection of information processing technologies that analyzes text information entered by a user and understands its meaning and intent.

[0910] An "artificial intelligence text generation engine" is a technology that generates appropriate advice for users based on the analysis results of a natural language processing engine.

[0911] "Advice" refers to advice or solutions provided to users for their concerns.

[0912] "Home security" refers to the efforts and measures taken to ensure the safety of the home environment.

[0913] "Solution provision means" refers to a method or process that uses a natural language processing engine or an artificial intelligence text generation engine to provide specific advice or instructions based on the problem entered by the user.

[0914] To implement this invention, a system is required to provide appropriate advice to users regarding their home security concerns. This system is composed of a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[0915] 1. Program Generation

[0916] In this invention, the server includes the following means:

[0917] A means of receiving user-entered concerns

[0918] A means of sending received concerns to a natural language processing engine

[0919] A means of generating advice based on prompts generated by a natural language processing engine

[0920] A means of displaying received advice to the user

[0921] A means of providing solutions to home security concerns

[0922] 2. Hardware and Software Used

[0923] Hardware:

[0924] User devices: smartphones, tablets, computers, etc.

[0925] Server: Linux-based server or cloud server (e.g. AWS EC2 instance)

[0926] software:

[0927] Natural language processing engine: an engine for analyzing user input text (e.g., SpaCy, NLTK)

[0928] Artificial intelligence text generation engine: An engine for generating advice based on analysis results (e.g., OpenAI GPT-3)

[0929] Data processing and calculation:

[0930] Converts user input into JSON format and sends it to the server

[0931] The server uses a natural language processing engine to analyze the input text and generate an appropriate prompt.

[0932] Use prompts to let an artificial intelligence text generation engine generate advice

[0933] The generated advice is converted to JSON format and sent back to the user device.

[0934] The user device analyzes the received advice and displays it on the screen.

[0935] 3. Specific Examples

[0936] When a user inputs "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried," the system operates as follows:

[0937] The server sends the user's input to a natural language processing engine, which generates a prompt such as, "I feel like there's a suspicious person walking around my house at night. I'm worried. What home security advice do I need?"

[0938] The AI ​​text generation engine generates advice based on the prompts, such as, "First, install an outdoor camera and set it up to send notifications when movement is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[0939] Example prompt sentence:

[0940] Problem: I feel like there is a suspicious person walking around my house in the middle of the night. It makes me uneasy.

[0941] Any advice please:

[0942] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0943] Step 1:

[0944] The user inputs their concerns. There is an input form on the user's device, and they can enter specific concerns, such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried." This input is sent to the server as JSON format data.

[0945] Step 2:

[0946] The device sends the worries to the server. The user device converts the worries into JSON format data and sends it to the server via an HTTP POST request. For example, the following data is sent to the server: {"trouble": "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried."}

[0947] Step 3:

[0948] The server receives the concerns. The server receives the JSON data sent from the device, analyzes it, and extracts the concerns. At this time, it classifies the concerns entered by the user into security-related categories.

[0949] Step 4:

[0950] The server uses a natural language processing engine. The server sends the received worry text to the natural language processing engine. This process analyzes the worry text and generates an appropriate prompt. For example, it generates a prompt such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried. Please tell me what home security advice I need."

[0951] Step 5:

[0952] The server sends a request to the artificial intelligence text generation engine. The server uses the generated prompt to request the artificial intelligence text generation engine to generate advice. The server sends a request with the prompt to the generative AI model and receives the generated advice text.

[0953] Step 6:

[0954] An artificial intelligence text generation engine generates advice. The generative AI model generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "First, install an outdoor camera and set it up to send notifications when motion is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[0955] Step 7:

[0956] The server receives the advice and sends it back to the device. The server receives the advice returned from the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends the following data: {"advice": "First, install an outdoor camera and set it up so that it sends notifications when it detects movement. It is also important to strengthen cooperation with neighboring residents and share information with each other."}

[0957] Step 8:

[0958] The device displays the advice to the user. The user's device parses the JSON data received from the server and displays the advice on the screen. The user can read the presented advice and use it to take action.

[0959] 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.

[0960] This invention is a system that analyzes user inputted worries and provides appropriate advice based on them. This system is characterized by its ability to recognize the user's emotional state and generate advice that takes this into consideration by combining it with an emotion engine. The main components of the system include a user terminal, a server, a natural language processing engine, an artificial intelligence text generation engine, and the emotion engine.

[0961] Program processing overview

[0962] 1. The user inputs their concerns on their device

[0963] The user enters their worries in text into the input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0964] 2. The device converts the concerns into JSON format

[0965] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0966] 3. The device sends JSON data to the server

[0967] The device sends the generated JSON data to the server using an HTTP POST request.

[0968] 4. The server receives the JSON data

[0969] The server receives an HTTP POST request from the device, parses the JSON data containing the worries, and extracts the parsed worry text.

[0970] 5. The server uses the emotion engine

[0971] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the user's emotion (e.g., stress, anxiety, joy, etc.).

[0972] 6. The server uses a natural language processing engine

[0973] The server takes into account the recognized emotional state and sends the text of the concern to a natural language processing engine to generate a prompt, for example, "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[0974] 7. The server sends a request to the AI ​​text generation engine

[0975] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[0976] 8. AI text generation engine generates advice

[0977] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[0978] 9. The server receives the generated advice

[0979] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[0980] 10. The server sends JSON data to the device

[0981] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[0982] 11. The device receives the advice

[0983] The device receives the HTTP response from the server and parses the JSON data containing advice, such as {"advice": "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."}

[0984] 12. The device displays advice to the user

[0985] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[0986] In this way, advice is provided that takes into account the user's emotional state, allowing for more relevant and effective advice than conventional systems, helping users solve their problems more quickly and accurately.

[0987] The processing flow will be explained below.

[0988] Step 1:

[0989] Users input their concerns from their device

[0990] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[0991] Step 2:

[0992] The device converts the concerns into JSON format.

[0993] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[0994] Step 3:

[0995] The device sends JSON data to the server

[0996] The device sends the generated JSON data to the server using an HTTP POST request.

[0997] Step 4:

[0998] The server receives the JSON data

[0999] The server receives an HTTP POST request from the device and parses the JSON data containing the worries. By parsing the data, the worry text is extracted.

[1000] Step 5:

[1001] The server uses the emotion engine

[1002] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the emotional state, such as "stress" or "anxiety."

[1003] Step 6:

[1004] The server uses a natural language processing engine

[1005] The server sends the recognized emotional state and the text of the concern to a natural language processing engine to generate a prompt, such as "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[1006] Step 7:

[1007] The server sends a request to the artificial intelligence text generation engine

[1008] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[1009] Step 8:

[1010] Artificial intelligence text generation engine generates advice

[1011] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you are feeling very stressed right now, but first, take a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[1012] Step 9:

[1013] The server receives the generated advice

[1014] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[1015] Step 10:

[1016] The server sends JSON data to the terminal.

[1017] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[1018] Step 11:

[1019] The device receives the advice

[1020] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I understand you're under a lot of stress right now, but first take a short break. Then, re-prioritize your work and reaffirm what's most important to you. It's also important not to keep it to yourself, but to talk to a colleague or your boss about it."}

[1021] Step 12:

[1022] The device displays advice to the user

[1023] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[1024] The above specific processing steps realize a system that allows a user to quickly obtain appropriate advice that takes into account the user's emotional state.

[1025] Example 2

[1026] 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."

[1027] Conventional systems have had difficulty providing appropriate and effective advice for the concerns entered by users. In particular, methods that generate general advice without considering the user's emotional state make it difficult to provide effective advice tailored to the user's individual situation. For this reason, there has been a demand for effective problem-solving and psychological support for users.

[1028] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving a concern input by a user; means for converting the received concern into JSON-formatted data; means for transmitting the JSON-formatted data to the server; means for analyzing the JSON data received by the server; means for transmitting the text of the concern to an emotion engine to recognize the user's emotional state; means for transmitting the text of the concern to a natural language processing engine to generate a prompt sentence based on the recognized emotional state; means for requesting an AI text generation engine to generate advice based on the generated prompt sentence; means for receiving the generated advice and converting it back into JSON format; means for transmitting the JSON data of the advice from the server to the terminal; and means for analyzing the JSON data of the advice received by the terminal and displaying it to the user. This enables more appropriate and effective advice that takes the user's emotional state into consideration.

[1029] "User" refers to the person who uses this system and inputs their concerns via a terminal.

[1030] A "terminal" is a device that allows a user to input their concerns and send them to the system, and includes smartphones, tablets, PCs, etc.

[1031] A "problem" is text data that a user enters into the system, expressing a particular problem or difficulty.

[1032] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight text format widely used as a data exchange format.

[1033] The "server" is a central system that receives data sent from the terminals, analyzes and processes it, and generates final advice.

[1034] A "natural language processing engine" is software or a system that analyzes text data entered by a user, understands its meaning, and generates prompt sentences.

[1035] An "emotion engine" is a system that functions as part of natural language processing and analyzes and recognizes emotional states from text entered by the user.

[1036] A "prompt sentence" is text data input to an artificial intelligence text generation engine, and is the sentence that serves as the basis for the generated advice.

[1037] An "artificial intelligence text generation engine" is an engine that generates advice to be provided to a user based on a prompt sentence.

[1038] An "HTTP POST request" is a type of network protocol for sending data to a server, and is used in this system to send data from a terminal to a server.

[1039] A "response" is a response message sent from the server to the terminal, and includes JSON data of the advice.

[1040] "Analysis" is the process of breaking down and analyzing data to understand its meaning and structure.

[1041] "Display" refers to the act of showing information to the user on the screen of a terminal.

[1042] This invention is a system that analyzes worries entered by a user and provides appropriate advice based on the analysis. This system is characterized by the use of an emotion engine to recognize the user's emotional state and generate advice that takes this into consideration. Below, we will explain how to specifically implement this system.

[1043] First, the devices used by users include smartphones, tablets, and PCs. Users enter their concerns in text form on these devices. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[1044] The device then converts the user's input into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}. The converted data is then sent to the server using an HTTP POST request over the internet.

[1045] The server receives the HTTP POST request sent from the device. After receiving it, the server analyzes the JSON data included in the request body and extracts the text portion of the trouble. For example, the text "I'm stressed out at work and I don't know what to do" is extracted from {"trouble": "I'm stressed out at work and I don't know what to do"}.

[1046] The server then sends the extracted text to the emotion engine, which uses natural language processing technology to analyze the text and classify the user's emotions. For example, it can recognize a state of stress from the keyword "stressed."

[1047] Based on the recognized emotional state, the server sends the text to a natural language processing engine to generate a prompt, such as "Worry: I'm stressed at work and don't know what to do.\nAdvice from my future self:"

[1048] Based on the generated prompt, the server requests an AI text generation engine to generate advice. The prompt also includes the user's emotional state, allowing for more personalized advice. For example, advice may be provided in the form of "advice from your future self."

[1049] The AI ​​text generation engine generates advice based on the prompt, such as, "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."

[1050] The generated advice is returned to the server and converted back into JSON format. The server then sends the generated JSON format advice to the device as an HTTP response. For example, it could be formatted as {"advice": "I understand you're under a lot of stress right now, but first consider taking a short break..."}.

[1051] The device receives the HTTP response from the server and parses the JSON data containing advice, for example, {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."}

[1052] Finally, the device displays the analyzed advice on the user's screen. For example, a message such as "I understand you are feeling very stressed right now, but first consider taking a short break" can be displayed. The user can confirm the advice and use it to take action.

[1053] In this way, the present invention can provide appropriate and effective advice that takes into account the user's emotional state.

[1054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1055] Step 1:

[1056] The user inputs their worries from the terminal. Specifically, the user inputs their worries as text into the input form on the terminal. After inputting, the user presses the send button. An example of input is "I'm stressed out at work and I don't know what to do." The input in this step is the text of the user's worries, and the output is stored as text data on the terminal.

[1057] Step 2:

[1058] The device converts the worries into JSON format. The input text is analyzed by a program and converted into JSON format. For example, the input text "I'm stressed out at work and I don't know what to do" is formatted as {"trouble": "I'm stressed out at work and I don't know what to do"}. The input for this step is the user's text data, and the output is JSON format data.

[1059] Step 3:

[1060] The device sends JSON data to the server. The device sends the generated JSON data to the server using an HTTP POST request. The device issues a request to the specified endpoint on the server. The input for this step is JSON-formatted data, and the output is an HTTP request to the server.

[1061] Step 4:

[1062] The server receives the JSON data. The server receives the HTTP POST request and analyzes the received content. The JSON data included in the request body is analyzed and the text portion of the problem is extracted from it. For example, from {"trouble": "I'm stressed out at work and I don't know what to do"}, "I'm stressed out at work and I don't know what to do" is extracted. The input of this step is the HTTP request, and the output is the raw text data of the problem.

[1063] Step 5:

[1064] The server sends the extracted worry text to the emotion engine. The text data is sent to the emotion engine's API, which analyzes emotions. For example, information such as "stressful words are used" is obtained. The input for this step is the worry text data, and the output is the emotion analysis results.

[1065] Step 6:

[1066] The server sends the results from the emotion engine to the natural language processing engine to generate a prompt sentence. Taking into account the input data and the emotional state, it generates a prompt sentence such as "Worry: I'm stressed at work and I don't know what to do.\nAdvice from my future self:" The input of this step is text data and the emotional state, and the output is a prompt sentence.

[1067] Step 7:

[1068] The server sends the generated prompt sentence to the AI ​​text generation engine. It requests the generation of advice based on the prompt sentence. The AI ​​text generation engine generates advice based on the prompt sentence. For example, the advice generated might be, "I'm sure you're feeling very stressed right now, but first consider taking a short break." The input of this step is the prompt sentence, and the output is the generated advice.

[1069] Step 8:

[1070] The server receives the generated advice and converts it back into JSON format. For example, the generated advice "I understand that you are under a lot of stress right now, but first consider taking a short break." is formatted as {"advice": "I understand that you are under a lot of stress right now, but first consider taking a short break."}. The input of this step is the generated advice, and the output is advice data in JSON format.

[1071] Step 9:

[1072] The server sends the advice JSON data to the terminal. The server sends the generated JSON-formatted advice to the terminal as an HTTP response. The input of this step is JSON-formatted advice data, and the output is an HTTP response to the terminal.

[1073] Step 10:

[1074] The device receives the advice and parses the JSON data. The device receives the HTTP response and parses the JSON data containing the advice. For example, the data {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."} is parsed and converted into a display format. The input of this step is the HTTP response, and the output is the parsed advice data.

[1075] Step 11:

[1076] The terminal displays the analyzed advice to the user. The analysis results are displayed on the screen so that the user can confirm the advice. For example, a message such as "I understand that you are under a lot of stress right now, but first consider taking a short break" is displayed on the screen. The input of this step is the analyzed advice data, and the output is the screen display.

[1077] (Application example 2)

[1078] 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."

[1079] Modern users have a wide range of concerns, and these concerns often depend on the user's emotional state. Conventional systems can analyze a user's concerns and provide general advice, but it is difficult to provide advice that fully takes into account the user's emotional state. Furthermore, when customers have questions or concerns about product selection, particularly in physical stores, responding appropriately and providing advice in real time has been a challenge. There is a need for a system that can solve these problems and provide more appropriate advice based on the user's emotional state.

[1080] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1081] In this invention, the server includes means for receiving a concern input by a user, emotion analysis means for recognizing the emotional state of the user, means for generating a prompt taking the recognized emotional state into consideration, means for causing an AI text generation engine to generate advice based on the generated prompt, and means for displaying the received advice to the user. This makes it possible to provide more appropriate advice based on the user's emotional state and support the shopping experience, particularly in physical stores.

[1082] The "means for receiving the worries entered by the user" refers to a means for acquiring the worry information entered by the user in text or voice and transmitting it to the system.

[1083] A "natural language processing engine" is a technical engine that analyzes input text data and generates appropriate text based on the analysis results.

[1084] The "means for receiving advice" is a means for receiving the generated advice from the server and providing it to the user.

[1085] "Emotion analysis means" is a technology that identifies emotions from text data entered by a user and specifies that emotional state.

[1086] The "means for generating a prompt" is a technology that creates a prompt sentence to be sent to an artificial intelligence text generation engine based on the results of sentiment analysis.

[1087] An "artificial intelligence text generation engine" is an engine that generates appropriate advice or text for input information based on specified prompts.

[1088] The "means for displaying to the user" refers to a technology for displaying the received advice on the user's device, and has the function of providing information visually or audibly.

[1089] The present invention is a system that analyzes the worries entered by a user and provides appropriate advice based on the user's emotional state. This system is designed to support the shopping experience in brick-and-mortar stores. Detailed embodiments of the present invention are described below.

[1090] composition

[1091] Hardware:

[1092] Smart glasses: A device that allows users to input questions or concerns about products or services using voice and text. This device is equipped with an input interface, a communication module, and a display module.

[1093] Server: Includes a sentiment analyzer, a natural language processing engine, and an artificial intelligence text generation engine. The server receives input from the user, analyzes the data, and generates advice.

[1094] software:

[1095] Sentiment analysis tools (e.g., AWS Comprehend, IBM Watson): Analyzes text data entered by the user and identifies their emotional state (e.g., stress, anxiety, joy).

[1096] Natural language processing engine (e.g., Google NLP, spaCy): Generates prompt sentences based on the results of sentiment analysis.

[1097] Artificial intelligence text generation engine (e.g., OpenAI GPT-4): Creates appropriate advice based on the generated prompt.

[1098] Implementation flow

[1099] 1. Users input their concerns through smart glasses

[1100] Example: A user speaks or texts, "I'm not sure if this dress looks good on me."

[1101] 2. The smart glasses convert the inputted concerns into JSON format.

[1102] Example: {"trouble": "I don't know if this dress looks good on me"}

[1103] 3. The smart glasses send the JSON data to the server

[1104] Send data using an HTTP POST request.

[1105] 4. The server receives and analyzes the data

[1106] The server parses the received JSON data and extracts the text of the user's concerns.

[1107] 5. The server uses emotion analysis methods

[1108] A sentiment analysis means is used to recognize the emotional state of the user.

[1109] 6. The server generates a prompt using a natural language processing engine

[1110] Example: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[1111] 7. The server sends a request to the AI ​​text generation engine

[1112] An artificial intelligence text generation engine generates advice based on prompts.

[1113] 8. The server receives the generated advice and converts it to JSON format.

[1114] The server converts the advice from the AI ​​text generation engine into JSON format.

[1115] 9. The server sends JSON data to the smart glasses

[1116] Send the data as an HTTP response.

[1117] 10. Smart glasses receive and display advice

[1118] The advice is displayed in the user's field of view.

[1119] Specific examples

[1120] When a user uses smart glasses to choose a dress in a store, if they input, "I don't know if this dress will suit me," the system can analyze the user's anxious emotional state and generate and display advice such as:

[1121] Example prompt sentence:

[1122] Problem: I don't know if this dress will suit me.

[1123] Emotion: Anxiety

[1124] advice:

[1125] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1126] Step 1:

[1127] The user inputs their concerns through smart glasses.

[1128] Input: The user inputs their concerns by voice or text.

[1129] How it works: Capture worries using the input interface of smart glasses.

[1130] Output: The captured text data.

[1131] Step 2:

[1132] The device converts the input concerns into JSON format.

[1133] Input: The captured text data.

[1134] How it works: The smart glasses software parses the text data and converts it into JSON formatted data.

[1135] Output: For example, JSON data like {"trouble": "I don't know if this dress looks good on me"}.

[1136] Step 3:

[1137] The device sends JSON data to the server.

[1138] Input: JSON formatted worries data.

[1139] What it does: Sends data to the server using an HTTP POST request.

[1140] Output: The JSON data sent to the server.

[1141] Step 4:

[1142] The server parses the received JSON data.

[1143] Input: JSON data sent from the terminal.

[1144] How it works: The server receives the HTTP POST request and parses the data to extract the problem text.

[1145] Output: For example, the text "I don't know if this dress looks good on me."

[1146] Step 5:

[1147] The server utilizes emotion analysis means to recognize the user's emotional state.

[1148] Input: Parsed problem text.

[1149] How it works: The server uses a sentiment analysis tool (e.g., AWS Comprehend) to identify the emotional state from the text, e.g., "anxiety."

[1150] Output: Emotional state recognition results.

[1151] Step 6:

[1152] The server generates the prompt using a natural language processing engine.

[1153] Input: Emotional state recognition results and distress text.

[1154] How it works: The server uses a natural language processing engine (e.g., Google NLP) to generate a prompt: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[1155] Output: The prompt statement.

[1156] Step 7:

[1157] The server requests the artificial intelligence text generation engine to generate advice.

[1158] Input: The generated prompt statement.

[1159] How it works: The server sends a prompt to an artificial intelligence text generation engine (e.g., OpenAI GPT-4) to generate appropriate advice.

[1160] Output: Generated advice (e.g., "First, check if the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape.")

[1161] Step 8:

[1162] The server receives the generated advice and converts it to JSON format.

[1163] Input: The generated advice.

[1164] How it works: The server converts the advice text into JSON format.

[1165] Output: For example, {"advice": "First, check that the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape."}

[1166] Step 9:

[1167] The server sends the JSON data to the smart glasses.

[1168] Input: Advice data in JSON format.

[1169] What it does: Sends data as an HTTP response.

[1170] Output: JSON data sent to the smart glasses.

[1171] Step 10:

[1172] The terminal receives the advice and displays it to the user.

[1173] Input: JSON data received from the server.

[1174] How it works: The display module in the smart glasses analyzes the advice and displays it in the field of view.

[1175] Output: Advice visually displayed to the user.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] [Fourth embodiment]

[1180] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1181] 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.

[1182] 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).

[1183] 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.

[1184] 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.

[1185] 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).

[1186] 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.

[1187] 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.

[1188] 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.

[1189] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1191] In the 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.

[1192] 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."

[1193] To implement this invention, a system is required in which a user inputs their concerns through a terminal, the server receives the input, performs a series of processes, and then displays advice to the user. The main components of this system include a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[1194] Program processing overview

[1195] 1. The user inputs their concerns on their device

[1196] The user enters their concerns into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[1197] 2. The device sends the worries to the server

[1198] The device converts the worries entered into the input form into JSON format data and sends it to the server via an HTTP POST request. For example, the data sent to the server is {"trouble": "I'm stressed out at work and I don't know what to do"}.

[1199] 3. The server receives the message

[1200] The server receives the JSON data sent from the device, parses it, and extracts the parsed worry text.

[1201] 4. The server uses a natural language processing engine

[1202] The server sends the received text of the problem to a natural language processing engine, which understands the problem and generates prompts that provide appropriate advice.

[1203] 5. The server sends a request to the AI ​​text generation engine

[1204] The server uses the generated prompt to request an AI text generation engine to generate advice. For example, it sends a prompt such as "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[1205] 6. AI text generation engine generates advice

[1206] The AI ​​text generation engine generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "I understand you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what's most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[1207] 7. The server receives the advice and returns it to the device.

[1208] The server receives the advice returned by the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."}

[1209] 8. The device displays advice to the user

[1210] The device parses the JSON data received from the server and displays the advice to the user, who can then interpret the advice and use it to take action.

[1211] In this way, the system according to the present invention can analyze the worries entered by the user and quickly provide advice based on the analysis. This process allows the user to receive effective advice even without specialized knowledge or experience, thereby enabling rapid problem resolution.

[1212] The processing flow will be explained below.

[1213] Step 1:

[1214] Users input their concerns from their device

[1215] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[1216] Step 2:

[1217] The device converts the concerns into JSON format.

[1218] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[1219] Step 3:

[1220] The device sends JSON data to the server

[1221] The device sends the generated JSON data to the server using an HTTP POST request.

[1222] Step 4:

[1223] The server receives the JSON data

[1224] The server receives an HTTP POST request from the device and parses the JSON data containing the problem.

[1225] Step 5:

[1226] The server uses a natural language processing engine

[1227] The server sends the analyzed problem text to a natural language processing engine, which analyzes the text and generates a prompt.

[1228] Step 6:

[1229] The server sends a request to the artificial intelligence text generation engine

[1230] The server then requests the AI ​​text generation engine to generate advice based on the generated prompt. For example, it sends the prompt "Problem: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[1231] Step 7:

[1232] Artificial intelligence text generation engine generates advice

[1233] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[1234] Step 8:

[1235] The server receives the generated advice

[1236] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[1237] Step 9:

[1238] The server sends JSON data to the terminal.

[1239] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[1240] Step 10:

[1241] The device receives the advice

[1242] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."}

[1243] Step 11:

[1244] The device displays advice to the user

[1245] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[1246] The above steps realize a system that allows users to receive prompt and appropriate advice on their concerns.

[1247] Example 1

[1248] 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."

[1249] In modern society, individual users have a wide range of problems, and there is a need for methods to quickly and effectively address them. However, users often need time and effort to seek professional advice, making it difficult to quickly resolve their problems. Furthermore, existing systems often lack the accuracy and efficiency to generate and provide appropriate advice for the problems entered by users.

[1250] 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.

[1251] In this invention, the server includes a means for receiving a problem input by a user, a means for transmitting the received problem to a natural language processing engine, and a means for receiving advice generated by the natural language processing engine. This makes it possible to analyze the problem input by the user and provide quick and effective advice using a generative AI model. Furthermore, by adding a means for converting the generated advice into JSON format and transmitting it to the terminal, the user can receive the advice in a format that is easy to understand and implement. This enables quick and effective problem solving for each user's problem.

[1252] A "user" is an individual or group that uses the system to input their concerns and receive advice.

[1253] A "terminal" is a hardware device or software application that a user uses to input concerns and receive advice.

[1254] A "server" is a central processing unit or system that receives input of concerns from users and performs processing for analysis and advice generation.

[1255] A "natural language processing engine" is a processing engine that includes algorithms and technologies for analyzing text data and understanding its meaning.

[1256] A "generative AI model" is an artificial intelligence algorithm and model that generates appropriate advice based on input text data.

[1257] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data description language widely used as a data exchange format.

[1258] A "prompt" refers to pre-defined text used to prompt a generative AI model to generate advice.

[1259] "Means of analysis" refers to the processes and techniques used to interpret the text of the user's concerns and extract the necessary information.

[1260] "Advice" refers to useful information or suggestions that the generative AI model provides to address the user's concerns.

[1261] A "transmission means" is a method or protocol for moving data from one device or system to another.

[1262] This invention is a system that analyzes user-entered concerns and generates and provides advice based on the analysis. This system includes a user terminal, a server, a natural language processing engine, and a generative AI model.

[1263] Users input their concerns using a device. User devices can be smartphones, tablets, personal computers, etc. By typing their concerns into the input form and submitting it, the concerns are imported into the system. For example, a user might input, "I'm stressed out at work and I don't know what to do."

[1264] The terminal receives input from the user and converts it into JSON format, generating the following data: {"trouble": "I'm stressed at work and I don't know what to do"}. The generated JSON data is then sent to the server using an HTTP POST request.

[1265] The server receives and analyzes the JSON data sent from the device. A natural language processing engine is used for the analysis to understand the meaning of the text data. At this time, the server sends the text data of the worries to the natural language processing engine and requests it to analyze it to understand the content of the worries.

[1266] The server generates a prompt based on the analysis results and sends it to the generative AI model. The generative AI model uses the prompt to generate advice. For example, a prompt could be "Worry: I'm stressed out at work and I don't know what to do.\nAdvice from my future self:"

[1267] Based on this prompt, the generative AI model generates appropriate advice and sends it back to the server. An example of generated advice is, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. It is also important not to keep it to yourself, but to talk to your colleagues or superiors."

[1268] The server converts the received advice into JSON format and sends it to the device. For example, the following data is generated: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or boss."

[1269] Finally, the device analyzes the received JSON data and displays advice to the user. The user can then confirm the displayed advice and use it to take practical action. In this way, the system according to the present invention can analyze the concerns entered by the user and quickly provide advice based on the analysis. This allows users to receive effective advice and quickly solve problems, even if they do not have specialized knowledge or experience.

[1270] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1271] Step 1:

[1272] The user enters their worries into an input form on the device. The entered worries are saved as text data on the device. As an example of input, the user might type, "I'm stressed out at work and I don't know what to do." The specific user interaction is the action of clicking the "Send" button on the device screen. The input data is the worries in text format.

[1273] Step 2:

[1274] The device converts the received text data into JSON format. For example, it generates JSON data such as {"trouble": "I'm stressed at work and I don't know what to do"}. The device then sends this JSON data to the server via an HTTP POST request. The input data is the text-formatted problem, and the output is JSON format data.

[1275] Step 3:

[1276] The server receives the JSON data sent from the device. The server parses this data and extracts the relevant text data. Specifically, it uses the JSON.parse function to parse the data {"trouble": "I'm stressed at work and I don't know what to do"}. The input data is JSON format data, and the output is the parsed text data.

[1277] Step 4:

[1278] The server sends the extracted text data to a natural language processing engine. The natural language processing engine analyzes the received text data and understands its meaning. In this process, for example, a POST request is sent to the API endpoint of the natural language processing engine. The input data is the text-formatted problem data, and the output is the analysis results.

[1279] Step 5:

[1280] The server generates a prompt based on the analysis results returned by the natural language processing engine. The generated prompt is then sent to a generative AI model, requesting it to generate advice. Specifically, it generates a prompt in the format "Problem: I'm stressed at work and don't know what to do.\nAdvice from my future self:" and sends it to the generative AI model. The input data is the text of the analysis results, and the output is the generated prompt.

[1281] Step 6:

[1282] The generative AI model generates advice based on the received prompt. For example, "I understand that you are feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors." The generated advice is then sent back to the server. The input data is the prompt, and the output is the generated advice text.

[1283] Step 7:

[1284] The server receives the advice returned from the generative AI model. It converts the received advice back into JSON format and sends it to the device. For example, it converts it into the following format: {"advice": "I'm sure you're feeling very stressed right now, but first consider taking a short break. Then, review your work priorities and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."} and sends it. The input data is the advice text, and the output is the advice data in JSON format.

[1285] Step 8:

[1286] The device parses the JSON data received from the server. The parsed data is displayed to the user through the user interface. The user reads the displayed advice and puts it into action. The input data is advice data in JSON format, and the output is text advice that is displayed to the user. Specifically, the device application displays the advice on the screen.

[1287] (Application example 1)

[1288] 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."

[1289] The purpose of this invention is to provide a support system that enables users with home security concerns to solve their problems effectively and quickly. Specifically, the objective is to establish a technical means for providing appropriate and professional advice immediately to users in response to their anxieties and concerns.

[1290] 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.

[1291] In this invention, the server includes means for receiving a concern input by a user, means for transmitting the received concern to a natural language processing engine, means for receiving advice generated by the natural language processing engine, means for displaying the received advice to the user, and means for providing a solution to the home security concern. This enables the security concern input by the user to be analyzed and appropriate advice provided by AI to be promptly received.

[1292] A "user" is someone who uses the system to input their concerns and receive advice.

[1293] A "problem" is a problem or anxiety that a user has, or an issue that the user wants to solve.

[1294] "Means for receiving" refers to the method or process by which the server obtains the worries entered by the user and the advice generated by the AI ​​engine.

[1295] A "natural language processing engine" is a collection of information processing technologies that analyzes text information entered by a user and understands its meaning and intent.

[1296] An "artificial intelligence text generation engine" is a technology that generates appropriate advice for users based on the analysis results of a natural language processing engine.

[1297] "Advice" refers to advice or solutions provided to users for their concerns.

[1298] "Home security" refers to the efforts and measures taken to ensure the safety of the home environment.

[1299] "Solution provision means" refers to a method or process that uses a natural language processing engine or an artificial intelligence text generation engine to provide specific advice or instructions based on the problem entered by the user.

[1300] To implement this invention, a system is required to provide appropriate advice to users regarding their home security concerns. This system is composed of a user terminal, a server, a natural language processing engine, and an artificial intelligence text generation engine.

[1301] 1. Program Generation

[1302] In this invention, the server includes the following means:

[1303] A means of receiving user-entered concerns

[1304] A means of sending received concerns to a natural language processing engine

[1305] A means of generating advice based on prompts generated by a natural language processing engine

[1306] A means of displaying received advice to the user

[1307] A means of providing solutions to home security concerns

[1308] 2. Hardware and Software Used

[1309] Hardware:

[1310] User devices: smartphones, tablets, computers, etc.

[1311] Server: Linux-based server or cloud server (e.g. AWS EC2 instance)

[1312] software:

[1313] Natural language processing engine: an engine for analyzing user input text (e.g., SpaCy, NLTK)

[1314] Artificial intelligence text generation engine: An engine for generating advice based on analysis results (e.g., OpenAI GPT-3)

[1315] Data processing and calculation:

[1316] Converts user input into JSON format and sends it to the server

[1317] The server uses a natural language processing engine to analyze the input text and generate an appropriate prompt.

[1318] Use prompts to let an artificial intelligence text generation engine generate advice

[1319] The generated advice is converted to JSON format and sent back to the user device.

[1320] The user device analyzes the received advice and displays it on the screen.

[1321] 3. Specific Examples

[1322] When a user inputs "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried," the system operates as follows:

[1323] The server sends the user's input to a natural language processing engine, which generates a prompt such as, "I feel like there's a suspicious person walking around my house at night. I'm worried. What home security advice do I need?"

[1324] The AI ​​text generation engine generates advice based on the prompts, such as, "First, install an outdoor camera and set it up to send notifications when movement is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[1325] Example prompt sentence:

[1326] Problem: I feel like there is a suspicious person walking around my house in the middle of the night. It makes me uneasy.

[1327] Any advice please:

[1328] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1329] Step 1:

[1330] The user inputs their concerns. There is an input form on the user's device, and they can enter specific concerns, such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried." This input is sent to the server as JSON format data.

[1331] Step 2:

[1332] The device sends the worries to the server. The user device converts the worries into JSON format data and sends it to the server via an HTTP POST request. For example, the following data is sent to the server: {"trouble": "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried."}

[1333] Step 3:

[1334] The server receives the concerns. The server receives the JSON data sent from the device, analyzes it, and extracts the concerns. At this time, it classifies the concerns entered by the user into security-related categories.

[1335] Step 4:

[1336] The server uses a natural language processing engine. The server sends the received worry text to the natural language processing engine. This process analyzes the worry text and generates an appropriate prompt. For example, it generates a prompt such as, "I feel like there's a suspicious person walking around my house in the middle of the night. I'm worried. Please tell me what home security advice I need."

[1337] Step 5:

[1338] The server sends a request to the artificial intelligence text generation engine. The server uses the generated prompt to request the artificial intelligence text generation engine to generate advice. The server sends a request with the prompt to the generative AI model and receives the generated advice text.

[1339] Step 6:

[1340] An artificial intelligence text generation engine generates advice. The generative AI model generates advice based on the prompt and sends it back to the server. For example, it might generate advice like, "First, install an outdoor camera and set it up to send notifications when motion is detected. It's also important to strengthen cooperation with neighbors and share information with each other."

[1341] Step 7:

[1342] The server receives the advice and sends it back to the device. The server receives the advice returned from the AI ​​text generation engine, converts it into JSON format, and sends it back to the device. For example, it sends the following data: {"advice": "First, install an outdoor camera and set it up so that it sends notifications when it detects movement. It is also important to strengthen cooperation with neighboring residents and share information with each other."}

[1343] Step 8:

[1344] The device displays the advice to the user. The user's device parses the JSON data received from the server and displays the advice on the screen. The user can read the presented advice and use it to take action.

[1345] 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.

[1346] This invention is a system that analyzes user inputted worries and provides appropriate advice based on them. This system is characterized by its ability to recognize the user's emotional state and generate advice that takes this into consideration by combining it with an emotion engine. The main components of the system include a user terminal, a server, a natural language processing engine, an artificial intelligence text generation engine, and the emotion engine.

[1347] Program processing overview

[1348] 1. The user inputs their concerns on their device

[1349] The user enters their worries in text into the input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[1350] 2. The device converts the concerns into JSON format

[1351] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[1352] 3. The device sends JSON data to the server

[1353] The device sends the generated JSON data to the server using an HTTP POST request.

[1354] 4. The server receives the JSON data

[1355] The server receives an HTTP POST request from the device, parses the JSON data containing the worries, and extracts the parsed worry text.

[1356] 5. The server uses the emotion engine

[1357] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the user's emotion (e.g., stress, anxiety, joy, etc.).

[1358] 6. The server uses a natural language processing engine

[1359] The server takes into account the recognized emotional state and sends the text of the concern to a natural language processing engine to generate a prompt, for example, "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[1360] 7. The server sends a request to the AI ​​text generation engine

[1361] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[1362] 8. AI text generation engine generates advice

[1363] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[1364] 9. The server receives the generated advice

[1365] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[1366] 10. The server sends JSON data to the device

[1367] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[1368] 11. The device receives the advice

[1369] The device receives the HTTP response from the server and parses the JSON data containing advice, such as {"advice": "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."}

[1370] 12. The device displays advice to the user

[1371] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[1372] In this way, advice is provided that takes into account the user's emotional state, allowing for more relevant and effective advice than conventional systems, helping users solve their problems more quickly and accurately.

[1373] The processing flow will be explained below.

[1374] Step 1:

[1375] Users input their concerns from their device

[1376] The user enters their concerns in text into an input form on the device. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[1377] Step 2:

[1378] The device converts the concerns into JSON format.

[1379] The device converts the user's inputted worries into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}.

[1380] Step 3:

[1381] The device sends JSON data to the server

[1382] The device sends the generated JSON data to the server using an HTTP POST request.

[1383] Step 4:

[1384] The server receives the JSON data

[1385] The server receives an HTTP POST request from the device and parses the JSON data containing the worries. By parsing the data, the worry text is extracted.

[1386] Step 5:

[1387] The server uses the emotion engine

[1388] The server sends the extracted worry text to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the text and classifies the emotional state, such as "stress" or "anxiety."

[1389] Step 6:

[1390] The server uses a natural language processing engine

[1391] The server sends the recognized emotional state and the text of the concern to a natural language processing engine to generate a prompt, such as "Concern: I'm stressed at work and I don't know what to do.\nAdvice from my future self:"

[1392] Step 7:

[1393] The server sends a request to the artificial intelligence text generation engine

[1394] The server requests an AI text generation engine to generate advice based on the generated prompt, which also includes the user's emotional state.

[1395] Step 8:

[1396] Artificial intelligence text generation engine generates advice

[1397] The AI ​​text generation engine generates advice based on prompts, such as, "I understand that you are feeling very stressed right now, but first, take a short break. Then, re-prioritize your work and reaffirm what is most important to you. Don't keep it to yourself; it's also important to talk to your colleagues or superiors."

[1398] Step 9:

[1399] The server receives the generated advice

[1400] The server receives advice from the artificial intelligence text generation engine and converts it back into JSON format.

[1401] Step 10:

[1402] The server sends JSON data to the terminal.

[1403] The server sends the generated advice in JSON format to the terminal as an HTTP response.

[1404] Step 11:

[1405] The device receives the advice

[1406] The device receives the HTTP response from the server and parses the JSON data containing advice. For example, it receives data like this: {"advice": "I understand you're under a lot of stress right now, but first take a short break. Then, re-prioritize your work and reaffirm what's most important to you. It's also important not to keep it to yourself, but to talk to a colleague or your boss about it."}

[1407] Step 12:

[1408] The device displays advice to the user

[1409] The device then displays the analyzed advice on the user's screen, allowing the user to check the advice and use it to take action.

[1410] The above specific processing steps realize a system that allows a user to quickly obtain appropriate advice that takes into account the user's emotional state.

[1411] Example 2

[1412] 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."

[1413] Conventional systems have had difficulty providing appropriate and effective advice for the concerns entered by users. In particular, methods that generate general advice without considering the user's emotional state make it difficult to provide effective advice tailored to the user's individual situation. For this reason, there has been a demand for effective problem-solving and psychological support for users.

[1414] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving a concern input by a user; means for converting the received concern into JSON-formatted data; means for transmitting the JSON-formatted data to the server; means for analyzing the JSON data received by the server; means for transmitting the text of the concern to an emotion engine to recognize the user's emotional state; means for transmitting the text of the concern to a natural language processing engine to generate a prompt sentence based on the recognized emotional state; means for requesting an AI text generation engine to generate advice based on the generated prompt sentence; means for receiving the generated advice and converting it back into JSON format; means for transmitting the JSON data of the advice from the server to the terminal; and means for analyzing the JSON data of the advice received by the terminal and displaying it to the user. This enables more appropriate and effective advice that takes the user's emotional state into consideration.

[1415] "User" refers to the person who uses this system and inputs their concerns via a terminal.

[1416] A "terminal" is a device that allows a user to input their concerns and send them to the system, and includes smartphones, tablets, PCs, etc.

[1417] A "problem" is text data that a user enters into the system, expressing a particular problem or difficulty.

[1418] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight text format widely used as a data exchange format.

[1419] The "server" is a central system that receives data sent from the terminals, analyzes and processes it, and generates final advice.

[1420] A "natural language processing engine" is software or a system that analyzes text data entered by a user, understands its meaning, and generates prompt sentences.

[1421] An "emotion engine" is a system that functions as part of natural language processing and analyzes and recognizes emotional states from text entered by the user.

[1422] A "prompt sentence" is text data input to an artificial intelligence text generation engine, and is the sentence that serves as the basis for the generated advice.

[1423] An "artificial intelligence text generation engine" is an engine that generates advice to be provided to a user based on a prompt sentence.

[1424] An "HTTP POST request" is a type of network protocol for sending data to a server, and is used in this system to send data from a terminal to a server.

[1425] A "response" is a response message sent from the server to the terminal, and includes JSON data of the advice.

[1426] "Analysis" is the process of breaking down and analyzing data to understand its meaning and structure.

[1427] "Display" refers to the act of showing information to the user on the screen of a terminal.

[1428] This invention is a system that analyzes worries entered by a user and provides appropriate advice based on the analysis. This system is characterized by the use of an emotion engine to recognize the user's emotional state and generate advice that takes this into consideration. Below, we will explain how to specifically implement this system.

[1429] First, the devices used by users include smartphones, tablets, and PCs. Users enter their concerns in text form on these devices. For example, they might enter, "I'm stressed out at work and I don't know what to do."

[1430] The device then converts the user's input into JSON format data, for example, {"trouble": "I'm stressed at work and I don't know what to do"}. The converted data is then sent to the server using an HTTP POST request over the internet.

[1431] The server receives the HTTP POST request sent from the device. After receiving it, the server analyzes the JSON data included in the request body and extracts the text portion of the trouble. For example, the text "I'm stressed out at work and I don't know what to do" is extracted from {"trouble": "I'm stressed out at work and I don't know what to do"}.

[1432] The server then sends the extracted text to the emotion engine, which uses natural language processing technology to analyze the text and classify the user's emotions. For example, it can recognize a state of stress from the keyword "stressed."

[1433] Based on the recognized emotional state, the server sends the text to a natural language processing engine to generate a prompt, such as "Worry: I'm stressed at work and don't know what to do.\nAdvice from my future self:"

[1434] Based on the generated prompt, the server requests an AI text generation engine to generate advice. The prompt also includes the user's emotional state, allowing for more personalized advice. For example, advice may be provided in the form of "advice from your future self."

[1435] The AI ​​text generation engine generates advice based on the prompt, such as, "I know you're feeling very stressed right now, but first consider taking a short break. Then, reprioritize your work and reassess what's most important to you."

[1436] The generated advice is returned to the server and converted back into JSON format. The server then sends the generated JSON format advice to the device as an HTTP response. For example, it could be formatted as {"advice": "I understand you're under a lot of stress right now, but first consider taking a short break..."}.

[1437] The device receives the HTTP response from the server and parses the JSON data containing advice, for example, {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."}

[1438] Finally, the device displays the analyzed advice on the user's screen. For example, a message such as "I understand you are feeling very stressed right now, but first consider taking a short break" can be displayed. The user can confirm the advice and use it to take action.

[1439] In this way, the present invention can provide appropriate and effective advice that takes into account the user's emotional state.

[1440] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1441] Step 1:

[1442] The user inputs their worries from the terminal. Specifically, the user inputs their worries as text into the input form on the terminal. After inputting, the user presses the send button. An example of input is "I'm stressed out at work and I don't know what to do." The input in this step is the text of the user's worries, and the output is stored as text data on the terminal.

[1443] Step 2:

[1444] The device converts the worries into JSON format. The input text is analyzed by a program and converted into JSON format. For example, the input text "I'm stressed out at work and I don't know what to do" is formatted as {"trouble": "I'm stressed out at work and I don't know what to do"}. The input for this step is the user's text data, and the output is JSON format data.

[1445] Step 3:

[1446] The device sends JSON data to the server. The device sends the generated JSON data to the server using an HTTP POST request. The device issues a request to the specified endpoint on the server. The input for this step is JSON-formatted data, and the output is an HTTP request to the server.

[1447] Step 4:

[1448] The server receives the JSON data. The server receives the HTTP POST request and analyzes the received content. The JSON data included in the request body is analyzed and the text portion of the problem is extracted from it. For example, from {"trouble": "I'm stressed out at work and I don't know what to do"}, "I'm stressed out at work and I don't know what to do" is extracted. The input of this step is the HTTP request, and the output is the raw text data of the problem.

[1449] Step 5:

[1450] The server sends the extracted worry text to the emotion engine. The text data is sent to the emotion engine's API, which analyzes emotions. For example, information such as "stressful words are used" is obtained. The input for this step is the worry text data, and the output is the emotion analysis results.

[1451] Step 6:

[1452] The server sends the results from the emotion engine to the natural language processing engine to generate a prompt sentence. Taking into account the input data and the emotional state, it generates a prompt sentence such as "Worry: I'm stressed at work and I don't know what to do.\nAdvice from my future self:" The input of this step is text data and the emotional state, and the output is a prompt sentence.

[1453] Step 7:

[1454] The server sends the generated prompt sentence to the AI ​​text generation engine. It requests the generation of advice based on the prompt sentence. The AI ​​text generation engine generates advice based on the prompt sentence. For example, the advice generated might be, "I'm sure you're feeling very stressed right now, but first consider taking a short break." The input of this step is the prompt sentence, and the output is the generated advice.

[1455] Step 8:

[1456] The server receives the generated advice and converts it back into JSON format. For example, the generated advice "I understand that you are under a lot of stress right now, but first consider taking a short break." is formatted as {"advice": "I understand that you are under a lot of stress right now, but first consider taking a short break."}. The input of this step is the generated advice, and the output is advice data in JSON format.

[1457] Step 9:

[1458] The server sends the advice JSON data to the terminal. The server sends the generated JSON-formatted advice to the terminal as an HTTP response. The input of this step is JSON-formatted advice data, and the output is an HTTP response to the terminal.

[1459] Step 10:

[1460] The device receives the advice and parses the JSON data. The device receives the HTTP response and parses the JSON data containing the advice. For example, the data {"advice": "I know you're feeling very stressed right now, but first consider taking a short break."} is parsed and converted into a display format. The input of this step is the HTTP response, and the output is the parsed advice data.

[1461] Step 11:

[1462] The terminal displays the analyzed advice to the user. The analysis results are displayed on the screen so that the user can confirm the advice. For example, a message such as "I understand that you are under a lot of stress right now, but first consider taking a short break" is displayed on the screen. The input of this step is the analyzed advice data, and the output is the screen display.

[1463] (Application example 2)

[1464] 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."

[1465] Modern users have a wide range of concerns, and these concerns often depend on the user's emotional state. Conventional systems can analyze a user's concerns and provide general advice, but it is difficult to provide advice that fully takes into account the user's emotional state. Furthermore, when customers have questions or concerns about product selection, particularly in physical stores, responding appropriately and providing advice in real time has been a challenge. There is a need for a system that can solve these problems and provide more appropriate advice based on the user's emotional state.

[1466] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1467] In this invention, the server includes means for receiving a concern input by a user, emotion analysis means for recognizing the emotional state of the user, means for generating a prompt taking the recognized emotional state into consideration, means for causing an AI text generation engine to generate advice based on the generated prompt, and means for displaying the received advice to the user. This makes it possible to provide more appropriate advice based on the user's emotional state and support the shopping experience, particularly in physical stores.

[1468] The "means for receiving the worries entered by the user" refers to a means for acquiring the worry information entered by the user in text or voice and transmitting it to the system.

[1469] A "natural language processing engine" is a technical engine that analyzes input text data and generates appropriate text based on the analysis results.

[1470] The "means for receiving advice" is a means for receiving the generated advice from the server and providing it to the user.

[1471] "Emotion analysis means" is a technology that identifies emotions from text data entered by a user and specifies that emotional state.

[1472] The "means for generating a prompt" is a technology that creates a prompt sentence to be sent to an artificial intelligence text generation engine based on the results of sentiment analysis.

[1473] An "artificial intelligence text generation engine" is an engine that generates appropriate advice or text for input information based on specified prompts.

[1474] The "means for displaying to the user" refers to a technology for displaying the received advice on the user's device, and has the function of providing information visually or audibly.

[1475] The present invention is a system that analyzes the worries entered by a user and provides appropriate advice based on the user's emotional state. This system is designed to support the shopping experience in brick-and-mortar stores. Detailed embodiments of the present invention are described below.

[1476] composition

[1477] Hardware:

[1478] Smart glasses: A device that allows users to input questions or concerns about products or services using voice and text. This device is equipped with an input interface, a communication module, and a display module.

[1479] Server: Includes a sentiment analyzer, a natural language processing engine, and an artificial intelligence text generation engine. The server receives input from the user, analyzes the data, and generates advice.

[1480] software:

[1481] Sentiment analysis tools (e.g., AWS Comprehend, IBM Watson): Analyzes text data entered by the user and identifies their emotional state (e.g., stress, anxiety, joy).

[1482] Natural language processing engine (e.g., Google NLP, spaCy): Generates prompt sentences based on the results of sentiment analysis.

[1483] Artificial intelligence text generation engine (e.g., OpenAI GPT-4): Creates appropriate advice based on the generated prompt.

[1484] Implementation flow

[1485] 1. Users input their concerns through smart glasses

[1486] Example: A user speaks or texts, "I'm not sure if this dress looks good on me."

[1487] 2. The smart glasses convert the inputted concerns into JSON format.

[1488] Example: {"trouble": "I don't know if this dress looks good on me"}

[1489] 3. The smart glasses send the JSON data to the server

[1490] Send data using an HTTP POST request.

[1491] 4. The server receives and analyzes the data

[1492] The server parses the received JSON data and extracts the text of the user's concerns.

[1493] 5. The server uses emotion analysis methods

[1494] A sentiment analysis means is used to recognize the emotional state of the user.

[1495] 6. The server generates a prompt using a natural language processing engine

[1496] For example: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[1497] 7. The server sends a request to the AI ​​text generation engine

[1498] An artificial intelligence text generation engine generates advice based on prompts.

[1499] 8. The server receives the generated advice and converts it to JSON format.

[1500] The server converts the advice from the AI ​​text generation engine into JSON format.

[1501] 9. The server sends JSON data to the smart glasses

[1502] Send the data as an HTTP response.

[1503] 10. Smart glasses receive and display advice

[1504] The advice is displayed in the user's field of view.

[1505] Specific examples

[1506] When a user uses smart glasses to choose a dress in a store, if they input, "I don't know if this dress will suit me," the system can analyze the user's anxious emotional state and generate and display advice such as:

[1507] Example prompt sentence:

[1508] Problem: I don't know if this dress will suit me.

[1509] Emotion: Anxiety

[1510] advice:

[1511] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1512] Step 1:

[1513] The user inputs their concerns through smart glasses.

[1514] Input: The user inputs their concerns by voice or text.

[1515] How it works: Capture worries using the input interface of smart glasses.

[1516] Output: The captured text data.

[1517] Step 2:

[1518] The device converts the input concerns into JSON format.

[1519] Input: The captured text data.

[1520] How it works: The smart glasses software parses the text data and converts it into JSON formatted data.

[1521] Output: For example, JSON data like {"trouble": "I don't know if this dress looks good on me"}.

[1522] Step 3:

[1523] The device sends JSON data to the server.

[1524] Input: JSON formatted worries data.

[1525] What it does: Sends data to the server using an HTTP POST request.

[1526] Output: The JSON data sent to the server.

[1527] Step 4:

[1528] The server parses the received JSON data.

[1529] Input: JSON data sent from the terminal.

[1530] How it works: The server receives the HTTP POST request and parses the data to extract the problem text.

[1531] Output: For example, the text "I don't know if this dress looks good on me."

[1532] Step 5:

[1533] The server utilizes emotion analysis means to recognize the user's emotional state.

[1534] Input: Parsed problem text.

[1535] How it works: The server uses a sentiment analysis tool (e.g., AWS Comprehend) to identify the emotional state from the text, e.g., "anxiety."

[1536] Output: Emotional state recognition results.

[1537] Step 6:

[1538] The server generates the prompt using a natural language processing engine.

[1539] Input: Emotional state recognition results and distress text.

[1540] How it works: The server uses a natural language processing engine (e.g., Google NLP) to generate a prompt: "Concern: I don't know if this dress will look good on me.\nEmotion: Anxiety.\nAdvice:"

[1541] Output: The prompt statement.

[1542] Step 7:

[1543] The server requests the artificial intelligence text generation engine to generate advice.

[1544] Input: The generated prompt statement.

[1545] How it works: The server sends a prompt to an artificial intelligence text generation engine (e.g., OpenAI GPT-4) to generate appropriate advice.

[1546] Output: Generated advice (e.g., "First, check if the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape.")

[1547] Step 8:

[1548] The server receives the generated advice and converts it to JSON format.

[1549] Input: The generated advice.

[1550] How it works: The server converts the advice text into JSON format.

[1551] Output: For example, {"advice": "First, check that the color of the dress matches your skin tone. Also, consider whether the silhouette of the dress flatters your body shape."}

[1552] Step 9:

[1553] The server sends the JSON data to the smart glasses.

[1554] Input: Advice data in JSON format.

[1555] What it does: Sends data as an HTTP response.

[1556] Output: JSON data sent to the smart glasses.

[1557] Step 10:

[1558] The terminal receives the advice and displays it to the user.

[1559] Input: JSON data received from the server.

[1560] How it works: The display module in the smart glasses analyzes the advice and displays it in the field of view.

[1561] Output: Advice visually displayed to the user.

[1562] 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.

[1563] 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.

[1564] 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 robot 414.

[1565] 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.

[1566] FIG. 9 illustrates 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 behaviors 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.

[1567] 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.

[1568] 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).

[1569] 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.

[1570] 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."

[1571] 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.

[1572] 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).

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] The following is further disclosed regarding the above embodiment.

[1584] (Claim 1)

[1585] A means for receiving a concern input by a user;

[1586] A means for transmitting the received concerns to a natural language processing engine;

[1587] means for receiving advice generated by the natural language processing engine;

[1588] means for displaying the received advice to the user;

[1589] A system including:

[1590] (Claim 2)

[1591] 2. The system of claim 1, wherein the natural language processing engine is an artificial intelligence text generation engine.

[1592] (Claim 3)

[1593] The system according to claim 1, further comprising means for analyzing the concerns input by the user and converting them into JSON format data.

[1594] "Example 1"

[1595] (Claim 1)

[1596] A means for receiving a concern input by a user;

[1597] A means for transmitting the received concerns to a natural language processing engine;

[1598] means for receiving advice generated by the natural language processing engine;

[1599] means for displaying the received advice to the user;

[1600] A method to analyze the concerns entered by the user and convert them into JSON format data,

[1601] A means to convert the generated advice into JSON format and send it to the terminal;

[1602] A system including:

[1603] (Claim 2)

[1604] 10. The system of claim 1, wherein the natural language processing engine is a generative AI model.

[1605] (Claim 3)

[1606] 10. The system of claim 1, further comprising means for generating prompts for the advice generated by the generative AI model.

[1607] "Application Example 1"

[1608] (Claim 1)

[1609] A means for receiving a concern input by a user;

[1610] A means for transmitting the received concerns to a natural language processing engine;

[1611] means for receiving advice generated by the natural language processing engine;

[1612] means for displaying the received advice to the user;

[1613] A means of providing solutions to home security concerns;

[1614] A system including:

[1615] (Claim 2)

[1616] 2. The system of claim 1, wherein the natural language processing engine is an artificial intelligence text generation engine.

[1617] (Claim 3)

[1618] The system according to claim 1, further comprising means for analyzing the concerns input by the user and converting them into JSON format data.

[1619] "Example 2: Combining Emotion Engines"

[1620] (Claim 1)

[1621] A means for receiving a concern input by a user;

[1622] A way to convert received concerns into JSON format data,

[1623] A means of sending JSON formatted data to the server;

[1624] A means for the server to parse the received JSON data;

[1625] means for transmitting the distress text to an emotion engine to recognize the user's emotional state;

[1626] means for transmitting the recognized emotional state to a natural language processing engine for generating a prompt sentence;

[1627] a means for requesting an artificial intelligence text generation engine to generate advice based on the generated prompt sentence;

[1628] A means to receive the generated advice and convert it back to JSON format;

[1629] A means to send advice JSON data from the server to the device;

[1630] A means for parsing the JSON data of the advice received by the device and displaying it to the user;

[1631] A system including:

[1632] (Claim 2)

[1633] 2. The system according to claim 1, wherein the natural language processing engine has a function of generating a prompt sentence, and an artificial intelligence text generation engine generates advice based on the prompt sentence.

[1634] (Claim 3)

[1635] 2. The system of claim 1, wherein the server recognizes the user's emotional state through an emotion engine and takes this into account in the advice generation process.

[1636] "Application example 2 when combining emotion engines"

[1637] (Claim 1)

[1638] A means for receiving a concern input by a user;

[1639] A means for transmitting the received concerns to a natural language processing engine;

[1640] means for receiving advice generated by the natural language processing engine;

[1641] emotion analysis means for recognizing the emotional state of a user;

[1642] a means for generating prompts taking into account a perceived emotional state;

[1643] means for causing an artificial intelligence text generation engine to generate advice based on the generated prompts;

[1644] means for displaying the received advice to the user;

[1645] A system including:

[1646] (Claim 2)

[1647] 2. The system of claim 1, wherein the natural language processing engine is an artificial intelligence text generation engine.

[1648] (Claim 3)

[1649] The system according to claim 1, further comprising means for analyzing the concerns input by the user and converting them into JSON format data. [Explanation of symbols]

[1650] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving a concern input by a user; A means for transmitting the received concerns to a natural language processing engine; means for receiving advice generated by the natural language processing engine; means for displaying the received advice to the user; A system including:

2. 10. The system of claim 1, wherein the natural language processing engine is an artificial intelligence text generation engine.

3. The system according to claim 1, further comprising means for analyzing the concerns input by the user and converting them into JSON format data.

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