System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a negative sentiment input from a user; means for analyzing the received input and generating a positive perspective or encouragement message; and means for transmitting the generated message back to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, people are exposed to various stressors daily, such as work and interpersonal relationships, and tend to keep worries and negative emotions to themselves, especially those that are difficult to discuss with others. Under these circumstances, mental health is likely to be impaired, resulting in mental fatigue and depression, which has become a problem. The problem that this invention aims to solve is to provide a method and system that allows users to easily express negative emotions and convert them into a positive perspective, thereby achieving mental stability and improving happiness. [Means for solving the problem]

[0005] The present invention relates to a system including a means for receiving input regarding negative emotions from a user, a means for analyzing the received input and generating a positive perspective or an encouraging message, and a means for returning the generated message to the user. Specifically, the system includes a means for analyzing the user's input using natural language processing technology and providing personalized feedback for each user. The present invention makes it easier for users to express negative emotions, allowing them to adopt a positive perspective and reducing mental stress.

[0006] "User" refers to an individual who provides input regarding negative emotions to the system.

[0007] "Input" refers to text data or speech provided by a user to the system.

[0008] "Analysis" refers to the technical operations performed to process received user input as data and understand, classify, or transform its content.

[0009] A "positive perspective" refers to an opinion or feedback that reframes negative feelings or situations in a positive way.

[0010] An "encouraging message" refers to a message containing positive content that supports and cheers up the user.

[0011] "Generation" refers to the process by which the system creates new messages and feedback based on the analysis results.

[0012] "Reply" refers to the act of sending the generated message back to the user.

[0013] "System" in this patent refers to a set of devices or programs that receive and analyze user input and generate and return positive perspectives and messages.

[0014] "Natural language processing technology" refers to a technical system that enables computers to understand, analyze, and generate human language (natural language).

[0015] "Personalized feedback" refers to advice and messages that are optimized for a user's individual characteristics and circumstances. [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] The present invention relates to a system that receives input from a user regarding negative emotions, analyzes the input, and generates and returns messages of positive perspective and encouragement. An embodiment of this system, i.e., specific operations, will be described in detail below.

[0038] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[0039] The device then receives the user's tweets and sends them to a server. The server then uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[0040] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[0041] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[0042] As a specific example, the following flow can be considered.

[0043] 1. User tweet: The user types into the device, "I made a big mistake at work."

[0044] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[0045] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[0046] 4. Message generation: The server generates a positive message based on the analysis results.

[0047] 5. Returning the message: The server returns the generated message to the terminal.

[0048] 6. Display to user: The terminal displays the received message to the user.

[0049] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[0053] Step 2:

[0054] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[0055] Step 3:

[0056] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[0057] Step 4:

[0058] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[0059] Step 5:

[0060] The server uses natural language processing techniques to analyze the received user input, specifically calling the OpenAI API to generate positive prompt-based messages.

[0061] Step 6:

[0062] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[0063] Step 7:

[0064] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[0065] Step 8:

[0066] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[0067] Step 9:

[0068] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[0069] This series of steps helps users shift their negative emotions into a positive perspective, reducing mental stress.

[0070] Example 1

[0071] 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."

[0072] In modern society, users often experience many negative emotions and stress in their daily lives and at work. Ignoring these emotions can have a negative impact on mental health, so appropriate measures are needed. Conventional systems have difficulty providing prompt and appropriate feedback on individual users' emotions and lack the means to convert negative emotions into positive ones.

[0073] 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.

[0074] In this invention, the server includes means for receiving text related to negative emotions from the user, means for transmitting the received text data to the server, means for analyzing the data received by the server based on natural language processing technology, means for generating a positive perspective or an encouraging message based on the analysis results, means for returning the generated message from the server to the terminal, and means for displaying the returned message to the user. This makes it possible to quickly and effectively convert the user's negative emotions into a positive perspective and reduce mental burden.

[0075] "User" refers to a person who uses the system to input text about negative emotions.

[0076] "Text" refers to character string data input by the user.

[0077] "Terminal" refers to an electronic device used by a user, such as a smartphone, PC, or tablet.

[0078] A "server" refers to a computer system that receives data sent from a terminal and performs processing such as analysis and message generation.

[0079] "Natural language processing technology" is a technology for analyzing text data, and refers to a method for understanding and processing linguistic content.

[0080] "Generative AI model" refers to artificial intelligence technology that generates positive messages based on user input.

[0081] "Positive perspectives and encouraging messages" refer to sentences that provide a positive outlook and comfort to users who have negative feelings.

[0082] "Personalized feedback" refers to responses or messages that are individually tailored to each user's characteristics and situation.

[0083] "Analysis" refers to the process of understanding the meaning and sentiment of text data entered by a user.

[0084] "Return" refers to the act of sending the generated message back from the server to the terminal.

[0085] "Display" refers to the act of visually presenting a message to a user on a terminal.

[0086] This invention relates to a system that receives input about negative emotions from a user, analyzes the input, and generates and returns a message of positive perspective or encouragement. The system includes a terminal through which the user inputs negative emotions, a server that analyzes the received data and generates a positive message, and a means for displaying the generated message to the user.

[0087] First, a user inputs text related to negative emotions using a device such as a smartphone, PC, or tablet. For example, if a user inputs "I made a big mistake at work," the device receives this text and sends the text data to the server's API endpoint using an HTTP POST request.

[0088] The server analyzes the received text data using natural language processing technology. For analysis, it uses a generative AI model such as OpenAI's GPT-3. The server analyzes the sentiment of the text and identifies negative elements. For example, it can extract negative sentiment from the sentence, "I made a big mistake at work."

[0089] The server then generates a message of positive perspective and encouragement based on the analysis results. Using a generative AI model, it generates personalized messages based on the user's characteristics and circumstances. For example, it generates a message like, "Failure is an opportunity for growth. You can do better next time."

[0090] The generated positive message is returned from the server to the device. The server uses an HTTP response to send the message to the device. The device receives the returned message and displays it in the user interface. For example, a pop-up message might say, "Failure is an opportunity to grow. You'll do better next time."

[0091] Some examples of specific prompts include:

[0092] How can you generate a positive message when a user says, "I made a big mistake at work"?

[0093] Can you give me an example of API communication for transferring user tweets to the server?

[0094] How can we analyze negative sentiment data and generate positive messages?

[0095] Create a positive response to the negative, "I made a big mistake at work."

[0096] What is an example of an API call to send the generated positive message back to the device?

[0097] Can you give me an example of a UI design that displays positive messages received to the user?

[0098] This allows users to quickly and effectively transform negative emotions into positive ones, reducing mental strain.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] User Input

[0102] Users use devices such as smartphones, computers, and tablets to input text related to negative emotions.

[0103] Input: Negative text (e.g., "I made a big mistake at work.")

[0104] Specific behavior: The device's user interface displays a text input field and a submit button, the user enters text, and presses the submit button.

[0105] Step 2:

[0106] Sending input data

[0107] The terminal receives the text data entered by the user and sends it to the server using an HTTP POST request.

[0108] Input: Negative text data entered by the user

[0109] Output: Text data is sent to the server's API endpoint

[0110] Specific operation: The device constructs an HTTP request and sends data to the server over the Internet.

[0111] Step 3:

[0112] Receiving data

[0113] The server receives the text data sent from the terminal.

[0114] Input: Negative text data sent from the device

[0115] Output: Text data stored in the server's memory or storage

[0116] Specific operation: The server receives the request at the API endpoint and stores the text data in memory or in a database.

[0117] Step 4:

[0118] Data analysis

[0119] The server analyzes the received text data using natural language processing (NLP) technology and uses a generative AI model to identify negative elements.

[0120] Input: Saved negative text data

[0121] Output: Analysis results (identification of negative elements and classification of emotions)

[0122] Specific operation: The server calls an NLP library or generative AI model (e.g., GPT-3) to perform sentiment analysis of text data and extract negative elements.

[0123] Step 5:

[0124] Message Generation

[0125] The server generates positive perspectives and encouraging messages based on the analysis results.

[0126] Input: Analysis results (information on negative elements)

[0127] Output: Generated positive message (e.g., "Failure is an opportunity to grow. You can do better next time.")

[0128] Specific operation: The server uses the generative AI model to generate an appropriate positive message.

[0129] Step 6:

[0130] Message bounce

[0131] The server returns the generated positive message to the terminal in an HTTP response.

[0132] Input: Generated positive message

[0133] Output: HTTP response with a positive message

[0134] Specific operation: The server constructs an HTTP response and sends it back to the device over the Internet.

[0135] Step 7:

[0136] What the user sees

[0137] The terminal receives a positive message returned from the server.

[0138] Input: Positive message returned by the server

[0139] Output: A positive message displayed in the user interface.

[0140] Specific operation: The device receives the HTTP response and displays a message in the user interface (e.g., a popup or message field).

[0141] (Application example 1)

[0142] 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."

[0143] In modern society, users experience a wide range of negative emotions. For example, customers may feel stressed if they make an order error or encounter a service issue while shopping at a physical store. If such negative experiences are left unaddressed, it can lead to lower customer satisfaction and fewer repeat visits. However, there is currently no system that can instantly transform these negative emotions into positive ones.

[0144] 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.

[0145] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input and generating a message of positive perspective or encouragement, means for returning the generated message to the user, and means for displaying the generated message to the user. This allows immediate positive feedback to be provided to customers who have experienced negative emotions in a physical store, improving customer satisfaction and the rate of repeat visits.

[0146] A "user" is a person who uses the system and is the subject who inputs negative emotions.

[0147] "Negative emotions" are negative emotions such as dissatisfaction, discomfort, anxiety, and depression experienced by the user.

[0148] "Input" is information provided by a user to a system in the form of text, speech, or the like.

[0149] The "receiving means" is a function for acquiring user input as digital data.

[0150] "Means of analysis" refers to the function of deciphering received data using natural language processing technology, etc., and understanding its contents.

[0151] "Means for generating positive perspectives and encouraging messages" is a function that generates messages that make users feel positive based on the analysis results.

[0152] The "means for returning" is a function for sending the generated message to the user.

[0153] The "means for displaying" is a function for displaying the returned message in a form that can be visually confirmed by the user.

[0154] "Natural language processing technology" is an artificial intelligence technology that analyzes text data and understands its meaning and intent.

[0155] A "generative AI model" is a machine learning model used to generate appropriate responses to user input.

[0156] A "prompt sentence" is a text sentence that is given as specific input to a generative AI model.

[0157] "Personalized feedback" is a response that is customized according to the characteristics and circumstances of each individual user.

[0158] A "brick and mortar store" is a commercial establishment that exists in a physical location and can be visited in person by customers.

[0159] The present invention is a system that analyzes a user's input of negative emotions and generates, returns, and displays a positive perspective or encouraging message. The system includes a means for receiving an input regarding negative emotions from a user, a means for analyzing the received input and generating a positive message, a means for returning the generated message to the user, and a means for displaying the generated message to the user.

[0160] Hardware and software used

[0161] User devices: smartphones, tablets, computers, etc.

[0162] Server: performs data analysis and message generation

[0163] Natural Language Processing (NLP): SpaCy, BERT, etc.

[0164] Generative AI models: GPT-3, etc.

[0165] Data communication library: requests library

[0166] Data processing and calculation

[0167] 1. Receiving Input

[0168] The user terminal receives input about the user's negative emotions. For example, the user inputs "I made a mistake in my order and I'm a little frustrated" on a smartphone.

[0169] 2. Data transmission

[0170] The user terminal sends the received input data to the server using the requests library for data communication.

[0171] 3. Parsing the Input

[0172] The server analyzes the data received from the user using natural language processing techniques such as SpaCy and BERT, which tokenize the text data and understand its content.

[0173] 4. Message Creation

[0174] Based on the analysis results, the server uses a generative AI model (e.g., GPT-3) to generate a positive or encouraging message. It provides a prompt and asks the model to generate the message.

[0175] 5. Replying and Displaying Messages

[0176] The server returns the generated positive message to the user terminal, which displays the received message to the user.

[0177] Specific examples

[0178] User input: "I'm a little frustrated because I made a mistake with my order."

[0179] Analysis: Tokenizes user input to identify negative sentiment.

[0180] Generated message: "Everyone makes small mistakes. Please let our staff know and we'll take care of it right away."

[0181] Example prompt: User input: "I made a mistake with my order and I'm a little annoyed." ||| Positive response:

[0182] This allows the present invention to quickly address negative emotions experienced by customers in physical stores and increase customer satisfaction. In particular, by generating prompt sentences using a generative AI model, it is possible to provide positive messages customized for each individual user.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] The user uses a smartphone device to input negative emotions or experiences into a text box. An example input is "I made a mistake in my order and I'm a little frustrated." Once input is complete, the user presses the send button to send the input data to the server. Input: Negative text from the user. Output: Text data sent to the server.

[0186] Step 2:

[0187] The server receives text data from the user. The input at this stage is the user's negative input data, and the output is the received text data itself. The server receives data using the requests library.

[0188] Step 3:

[0189] The server analyzes the received text data using natural language processing technology. This analysis uses SpaCy and BERT to tokenize the text and understand its content. The input is the received text data, and the output is the analyzed negative sentiment type and content.

[0190] Step 4:

[0191] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a positive perspective or encouraging message. Specifically, the server inserts the user's negative input into a prompt and asks the AI ​​model to generate a message. The input is the analysis result and the prompt to be generated, and the output is a positive message. An example of a prompt: "I'm a little frustrated because I made a mistake with my order ||| Positive response:"

[0192] Step 5:

[0193] The server sends the generated positive message back to the user terminal. At this stage, the input is the generated positive message and the output is the data sent to the user terminal. The requests library is used for data communication.

[0194] Step 6:

[0195] The user terminal receives the positive message returned from the server and displays it to the user. The input is the positive message received from the server, and the output is the message to be displayed. Specifically, the message is displayed on the smartphone screen.

[0196] 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.

[0197] The present invention relates to a system that receives input about a user's negative emotions, analyzes the input, generates positive perspectives and encouraging messages, and combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[0198] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[0199] The device then receives the user's input and sends the data to a server, which uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[0200] A distinctive feature of the present invention is the use of an emotion engine. This emotion engine analyzes the user's emotional state from their input and generates more appropriate positive perspectives and encouraging messages based on that emotional state. The emotion engine reads emotional changes and tone from the user's input data and generates the most appropriate message accordingly.

[0201] Furthermore, the emotion engine can refer to the user's past input data to understand the user's emotional tendencies, providing more personalized feedback. This feature not only helps convert negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[0202] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[0203] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[0204] As a specific example, the following flow can be considered.

[0205] 1. User tweet: The user types into the device, "I made a big mistake at work."

[0206] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[0207] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[0208] 4. Use of emotion engine: The emotion engine analyzes the user's input and understands their emotional state.

[0209] 5. Message generation: The server generates a positive perspective or personalized message based on the results of the emotion engine.

[0210] 6. Returning the message: The server returns the generated message to the terminal.

[0211] 7. Display to user: The terminal displays the received message to the user.

[0212] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[0213] The processing flow will be explained below.

[0214] Step 1:

[0215] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[0216] Step 2:

[0217] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[0218] Step 3:

[0219] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[0220] Step 4:

[0221] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[0222] Step 5:

[0223] The server uses an emotion engine to analyze the received user input, which analyzes the user's emotional state from the input and is used to generate positive messages based on the emotional state.

[0224] Step 6:

[0225] The server uses natural language processing technology to generate positive prompt-based messages, which then incorporate the results of the emotion engine to generate more personalized messages.

[0226] Step 7:

[0227] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[0228] Step 8:

[0229] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[0230] Step 9:

[0231] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[0232] Step 10:

[0233] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[0234] Through this series of steps, the present invention provides a system that converts a user's negative emotions into positive ones and reduces mental burden. Furthermore, the emotion engine provides more appropriate feedback based on the user's emotional state, which is also effective for long-term emotion management.

[0235] Example 2

[0236] 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."

[0237] Conventional systems have had problems in responding adequately to users' negative emotions, making it difficult to contribute to improving users' mental stability and happiness. In particular, it was difficult to accurately grasp emotional changes and tone and provide appropriate messages accordingly, resulting in a lack of personalized feedback. Furthermore, analysis using natural language processing alone was unable to deeply understand the user's emotional state, making it impossible to generate effective positive messages for the user.

[0238] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0239] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input using natural language processing technology, means for analyzing the user's emotional state using an emotion engine, means for generating a positive perspective or an encouraging message using a generative AI model based on the analysis results, means for returning the generated message to the user, and means for understanding the user's emotional tendencies by referring to the user's past input data, thereby enabling a deep understanding of the user's emotions and providing personalized feedback.

[0240] "User" refers to the entity that uses this system to input negative emotions.

[0241] "Input" refers to text data that a user provides to a system through a terminal.

[0242] "Emotional input" refers to text data that allows a user to express their emotional state or feelings.

[0243] "Terminal" refers to an electronic device used by a user to provide input and communicate with the system, including, for example, a smartphone, a personal computer, or a tablet.

[0244] "Means for receiving" refers to the function of the terminal to receive user input and send it to the server.

[0245] "Means for transmitting" refers to a function for transmitting input data received by the terminal to the server.

[0246] "Server" refers to a computer system for analyzing received data, generating positive messages, and returning results to the terminal.

[0247] "Means for analyzing" refers to a function for analyzing received input data in a specific way (e.g., natural language processing technology).

[0248] "Natural language processing technology" refers to methods and technologies for analyzing text data and understanding its meaning. Specifically, it includes keyword extraction, sentiment analysis, etc.

[0249] An "emotion engine" refers to algorithms and technologies for analyzing a user's emotional state from input text data.

[0250] "Generative AI models" refer to artificial intelligence algorithms that generate text based on input data, such as OpenAI GPT-3.

[0251] "Means for generation" refers to the function for generating positive perspectives and encouraging messages based on the analysis results.

[0252] "Means for returning" refers to a function for returning a generated message from the server to the terminal.

[0253] The "means for displaying" refers to a function for displaying a message received by the terminal to the user.

[0254] "Past input data" refers to input data that a user has previously provided to the system.

[0255] "Personalized feedback" refers to appropriate response messages provided based on a user's individual characteristics and emotional tendencies.

[0256] The present invention relates to a system that receives input about a user's negative emotions, analyzes it, generates positive perspectives and encouraging messages, and further combines it with an emotion engine that recognizes the user's emotions.

[0257] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses may be a smartphone, PC, tablet, etc. The device receives this input and sends the data to a server via the Internet.

[0258] The server uses natural language processing (NLP) techniques to analyze the received data. Specifically, it uses the Python libraries NLTK and spaCy to analyze the specific negative content of the user's input. For example, from the input "I made a big mistake at work," it extracts "work" and "mistake" as important keywords and identifies the negative content.

[0259] The server then uses an emotion engine (e.g., Microsoft Azure's Text Analytics API) to analyze the user's emotional state. This engine reads the emotional inflection and tone of the user's input and generates an optimal positive message based on that emotional state.

[0260] Based on the analysis results of the emotion engine, the server uses a generative AI model (e.g., OpenAI GPT-3) to generate positive and encouraging messages. Examples of prompts for the generative AI model include the following:

[0261] User:

[0262] "I made a big mistake at work. What should I do?"

[0263] System (generative AI model):

[0264] "Failure is an opportunity to grow. You can do better next time. You're a hard worker, so use that experience to improve yourself even more."

[0265] The server then sends the generated message back to the device, which then displays it on its user interface. This allows the user to reframe negative emotions and situations in a positive way, reducing their mental burden.

[0266] Furthermore, the system can refer to the user's past input data to understand their emotional tendencies, providing more personalized feedback. This feature not only helps to turn negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[0267] In this way, the system achieves a series of steps from analyzing the user's emotions to generating personalized positive messages, contributing to improving the user's mental stability and sense of happiness.

[0268] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0269] Step 1: Getting User Input

[0270] The user uses the device to input a tweet about a negative emotion. For example, the user might type, "I made a big mistake at work," into the device's input field. This input is saved as text data on the device.

[0271] Step 2: Submitting input data

[0272] The device converts the received text data into JSON format and sends it to the server using an HTTP POST request. The input data (tweets) is sent to the server via the Internet, and the server receives the data.

[0273] Step 3: Data analysis with natural language processing (NLP)

[0274] The server analyzes the received JSON data. First, it uses Python libraries such as NLTK and spaCy to extract important keywords and sentiment from users' tweets. For example, negative sentiment can be identified from keywords such as "work" and "big mistake." This process makes it possible to determine the type of negative content contained in the input data.

[0275] Step 4: Emotional state analysis by the emotion engine

[0276] The server then calls an emotion engine based on the extracted keywords and context information. Here, the engine uses, for example, Microsoft Azure's Text Analytics API to perform a more detailed analysis of the user's emotional state. The engine identifies the intensity and type of emotion from the user's tweets and returns that information. Based on the input data and the analysis results, the emotional state (e.g., "sadness intensity 50%) is obtained.

[0277] Step 5: Generate a positive message

[0278] Based on the analysis results of the emotion engine, the server inputs a prompt into a generative AI model (e.g., OpenAI GPT-3) to generate a positive message. For example, the prompt could read, "The user said, 'I made a big mistake at work.' Please provide this user with an encouraging message," and the generative AI model would generate a message saying, "Failure is an opportunity for growth. You'll do better next time." This process generates an encouraging message that is appropriate for the user's emotional state.

[0279] Step 6: Returning the message

[0280] The server converts the generated positive message into JSON format and returns it to the terminal as an HTTP response, where the generated message is output and sent to the terminal.

[0281] Step 7: Display a message to the user

[0282] The device extracts positive messages from the received JSON data and displays them to the user. For example, a pop-up window on the device might say, "Failure is an opportunity for growth. You can do better next time." This process allows the user to receive encouraging messages and turn negative emotions into positive ones.

[0283] (Application example 2)

[0284] 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."

[0285] The goal is to improve the purchasing experience by reducing the negative emotions and stress felt by users when shopping in virtual stores. Also, a system is needed that can detect the user's emotional state in real time and provide appropriate feedback.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input related to negative emotions from the user, means for analyzing the received input and generating a positive perspective or an encouraging message, means for returning the generated message to the user, and means for displaying the message to the user using smart glasses. This makes it possible to convert the user's negative emotions into positive ones in real time and improve the shopping experience in the virtual store.

[0287] (definition statement)

[0288] The "means for receiving input relating to negative emotions from the user" is a function that allows the user to input information relating to negative emotions via the terminal and receive the information.

[0289] "Means for analyzing received input and generating positive perspectives and encouraging messages" is a function for processing received data on the user's negative emotions and generating positive replies and encouraging messages.

[0290] The "means for returning the generated message to the user" is a function for sending the generated positive message to the user's terminal.

[0291] The "means for displaying a message to a user using smart glasses" is a function for displaying the generated positive message on the display of the smart glasses.

[0292] "Natural language processing technology" refers to algorithms and methods for analyzing and generating natural language data, and is a technology used to understand and process user input text.

[0293] "Personalized feedback" is a method for providing optimized, individual feedback based on the characteristics and past data of each individual user.

[0294] MODE FOR CARRYING OUT THE INVENTION

[0295] The present invention is a system that converts negative emotions felt by users while shopping in a virtual store into positive ones. In this system, users input their emotions through smart glasses, and the data is analyzed by a server, which then generates and returns an appropriate message.

[0296] System construction

[0297] Smart Glasses:

[0298] Users wear smart glasses to shop in a virtual store, and the glasses allow users to input voice and text, and provide real-time feedback.

[0299] server:

[0300] The server has the following main roles:

[0301] 1. Receiving emotion input: Receive data about negative emotions input by the user through the smart glasses.

[0302] 2. Data analysis: The received data is analyzed on the server using natural language processing technology, specifically Google Cloud Natural Language API and IBM Watson Emotion Analysis.

[0303] 3. Positive Message Generation: The emotion engine analyzes the user's input and generates appropriate positive messages, taking into account the user's past data to provide personalized feedback.

[0304] Program processing

[0305] First, the user inputs text such as "I'm having trouble finding the item in stock" into the smart glasses. This input is sent to the server via the communication module built into the smart glasses.

[0306] Server-side steps:

[0307] 1. The server receives the input data.

[0308] 2. Analyze the received data using natural language processing technology to identify negative emotional content.

[0309] 3. Based on the results of this analysis, the emotion engine generates positive perspectives and encouraging messages.

[0310] 4. The generated message might be something like, "You may find some stock online. If not, look forward to our new models!"

[0311] Steps for smart glasses:

[0312] 1. Receive a positive message back from the server.

[0313] 2. Display the received message on the smart glasses display.

[0314] Specific examples

[0315] If a user searches for a discontinued product in stock in your virtual store but can't find it:

[0316] Input statement: The user types "I'm having trouble finding the item in stock" into the smart glasses.

[0317] Output message: The smart glasses display will display the message "Stock may also be found online. If not, look forward to our new model!"

[0318] Prompt Sentence Examples

[0319] User input: "I'm having trouble finding it in stock."

[0320] Sentiment analysis result: "user_feelings: frustration"

[0321] Appropriate positive message: "You may find stock online. If you still can't find it, look forward to our new models!"

[0322] This makes shopping in a virtual store a more positive and stress-free experience, increasing users' motivation to buy.

[0323] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0324] System program processing flow

[0325] Step 1:

[0326] The user uses the smart glasses to input a message related to a negative emotion. For example, the user may input, "I'm having trouble finding the item in stock." The smart glasses receive this input and store it in their internal memory.

[0327] Step 2:

[0328] The device (smart glasses) sends the received input data about negative emotions to the server, where the input data is packaged in JSON format and sent as an HTTP request.

[0329] Step 3:

[0330] The server receives this HTTP request and parses the JSON data. It extracts the user's input message from the received data and inputs it into a natural language processing model. The natural language processing model (e.g., Google Cloud Natural Language API) analyzes the input text for negative sentiment and identifies the emotional state.

[0331] Step 4:

[0332] The server uses an emotion engine to generate positive and encouraging messages based on the analysis results. The generative AI model creates appropriate messages by taking into account the user's context and emotional state. For example, it generates a message saying, "You may be able to find the item online. If not, look forward to our new model!"

[0333] Step 5:

[0334] The server then packages the generated message in JSON format again and sends it back to the device (smart glasses). The server then sends data containing a positive message as an HTTP response.

[0335] Step 6:

[0336] The device (smart glasses) receives this HTTP response and extracts a positive message from the JSON data. It displays the extracted message on the display and notifies the user. At this time, the display shows the following message: "Stock may also be found online. If not, look forward to our new model!"

[0337] summary

[0338] Step 1: The user inputs negative emotions into the smart glasses.

[0339] Step 2: The terminal sends the input data to the server.

[0340] Step 3: The server receives the data and analyzes it using a natural language processing model.

[0341] Step 4: The server generates a positive message using the emotion engine.

[0342] Step 5: The server sends the generated message back to the terminal.

[0343] Step 6: The device receives the message and displays it on the smart glasses display.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] [Second embodiment]

[0348] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0349] 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.

[0350] 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).

[0351] 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.

[0352] 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.

[0353] 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).

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0359] 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."

[0360] The present invention relates to a system that receives input from a user regarding negative emotions, analyzes the input, and generates and returns messages of positive perspective and encouragement. An embodiment of this system, i.e., specific operations, will be described in detail below.

[0361] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[0362] The device then receives the user's tweets and sends them to a server. The server then uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[0363] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[0364] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[0365] As a specific example, the following flow can be considered.

[0366] 1. User tweet: The user types into the device, "I made a big mistake at work."

[0367] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[0368] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[0369] 4. Message generation: The server generates a positive message based on the analysis results.

[0370] 5. Returning the message: The server returns the generated message to the terminal.

[0371] 6. Display to user: The terminal displays the received message to the user.

[0372] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[0373] The processing flow will be explained below.

[0374] Step 1:

[0375] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[0376] Step 2:

[0377] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[0378] Step 3:

[0379] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[0380] Step 4:

[0381] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[0382] Step 5:

[0383] The server uses natural language processing techniques to analyze the received user input, specifically calling the OpenAI API to generate positive prompt-based messages.

[0384] Step 6:

[0385] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[0386] Step 7:

[0387] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[0388] Step 8:

[0389] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[0390] Step 9:

[0391] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[0392] This series of steps helps users shift their negative emotions into a positive perspective, reducing mental stress.

[0393] Example 1

[0394] 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."

[0395] In modern society, users often experience many negative emotions and stress in their daily lives and at work. Ignoring these emotions can have a negative impact on mental health, so appropriate measures are needed. Conventional systems have difficulty providing prompt and appropriate feedback on individual users' emotions and lack the means to convert negative emotions into positive ones.

[0396] 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.

[0397] In this invention, the server includes means for receiving text related to negative emotions from the user, means for transmitting the received text data to the server, means for analyzing the data received by the server based on natural language processing technology, means for generating a positive perspective or an encouraging message based on the analysis results, means for returning the generated message from the server to the terminal, and means for displaying the returned message to the user. This makes it possible to quickly and effectively convert the user's negative emotions into a positive perspective and reduce mental burden.

[0398] "User" refers to a person who uses the system to input text about negative emotions.

[0399] "Text" refers to character string data input by the user.

[0400] "Terminal" refers to an electronic device used by a user, such as a smartphone, PC, or tablet.

[0401] A "server" refers to a computer system that receives data sent from a terminal and performs processing such as analysis and message generation.

[0402] "Natural language processing technology" is a technology for analyzing text data, and refers to a method for understanding and processing linguistic content.

[0403] "Generative AI model" refers to artificial intelligence technology that generates positive messages based on user input.

[0404] "Positive perspectives and encouraging messages" refer to sentences that provide a positive outlook and comfort to users who have negative feelings.

[0405] "Personalized feedback" refers to responses or messages that are individually tailored to each user's characteristics and situation.

[0406] "Analysis" refers to the process of understanding the meaning and sentiment of text data entered by a user.

[0407] "Return" refers to the act of sending the generated message back from the server to the terminal.

[0408] "Display" refers to the act of visually presenting a message to a user on a terminal.

[0409] This invention relates to a system that receives input about negative emotions from a user, analyzes the input, and generates and returns a message of positive perspective or encouragement. The system includes a terminal through which the user inputs negative emotions, a server that analyzes the received data and generates a positive message, and a means for displaying the generated message to the user.

[0410] First, a user inputs text related to negative emotions using a device such as a smartphone, PC, or tablet. For example, if a user inputs "I made a big mistake at work," the device receives this text and sends the text data to the server's API endpoint using an HTTP POST request.

[0411] The server analyzes the received text data using natural language processing technology. For analysis, it uses a generative AI model such as OpenAI's GPT-3. The server analyzes the sentiment of the text and identifies negative elements. For example, it can extract negative sentiment from the sentence, "I made a big mistake at work."

[0412] The server then generates a message of positive perspective and encouragement based on the analysis results. Using a generative AI model, it generates personalized messages based on the user's characteristics and circumstances. For example, it generates a message like, "Failure is an opportunity for growth. You can do better next time."

[0413] The generated positive message is returned from the server to the device. The server uses an HTTP response to send the message to the device. The device receives the returned message and displays it in the user interface. For example, a pop-up message might say, "Failure is an opportunity to grow. You'll do better next time."

[0414] Some examples of specific prompts include:

[0415] How can you generate a positive message when a user says, "I made a big mistake at work"?

[0416] Can you give me an example of API communication for transferring user tweets to the server?

[0417] How can we analyze negative sentiment data and generate positive messages?

[0418] Create a positive response to the negative, "I made a big mistake at work."

[0419] What is an example of an API call to send the generated positive message back to the device?

[0420] Can you give me an example of a UI design that displays positive messages received to the user?

[0421] This allows users to quickly and effectively transform negative emotions into positive ones, reducing mental strain.

[0422] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0423] Step 1:

[0424] User Input

[0425] Users use devices such as smartphones, computers, and tablets to input text related to negative emotions.

[0426] Input: Negative text (e.g., "I made a big mistake at work.")

[0427] Specific behavior: The device's user interface displays a text input field and a submit button, the user enters text, and presses the submit button.

[0428] Step 2:

[0429] Sending input data

[0430] The terminal receives the text data entered by the user and sends it to the server using an HTTP POST request.

[0431] Input: Negative text data entered by the user

[0432] Output: Text data is sent to the server's API endpoint

[0433] Specific operation: The device constructs an HTTP request and sends data to the server over the Internet.

[0434] Step 3:

[0435] Receiving data

[0436] The server receives the text data sent from the terminal.

[0437] Input: Negative text data sent from the device

[0438] Output: Text data stored in the server's memory or storage

[0439] Specific operation: The server receives the request at the API endpoint and stores the text data in memory or in a database.

[0440] Step 4:

[0441] Data analysis

[0442] The server analyzes the received text data using natural language processing (NLP) technology and uses a generative AI model to identify negative elements.

[0443] Input: Saved negative text data

[0444] Output: Analysis results (identification of negative elements and classification of emotions)

[0445] Specific operation: The server calls an NLP library or generative AI model (e.g., GPT-3) to perform sentiment analysis of text data and extract negative elements.

[0446] Step 5:

[0447] Message Generation

[0448] The server generates positive perspectives and encouraging messages based on the analysis results.

[0449] Input: Analysis results (information on negative elements)

[0450] Output: Generated positive message (e.g., "Failure is an opportunity to grow. You can do better next time.")

[0451] Specific operation: The server uses the generative AI model to generate an appropriate positive message.

[0452] Step 6:

[0453] Message bounce

[0454] The server returns the generated positive message to the terminal in an HTTP response.

[0455] Input: Generated positive message

[0456] Output: HTTP response with a positive message

[0457] Specific operation: The server constructs an HTTP response and sends it back to the device over the Internet.

[0458] Step 7:

[0459] What the user sees

[0460] The terminal receives a positive message returned from the server.

[0461] Input: Positive message returned by the server

[0462] Output: A positive message displayed in the user interface.

[0463] Specific operation: The device receives the HTTP response and displays a message in the user interface (e.g., a popup or message field).

[0464] (Application example 1)

[0465] 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."

[0466] In modern society, users experience a wide range of negative emotions. For example, customers may feel stressed if they make an order error or encounter a service issue while shopping at a physical store. If such negative experiences are left unaddressed, it can lead to lower customer satisfaction and fewer repeat visits. However, there is currently no system that can instantly transform these negative emotions into positive ones.

[0467] 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.

[0468] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input and generating a message of positive perspective or encouragement, means for returning the generated message to the user, and means for displaying the generated message to the user. This allows immediate positive feedback to be provided to customers who have experienced negative emotions in a physical store, improving customer satisfaction and the rate of repeat visits.

[0469] A "user" is a person who uses the system and is the subject who inputs negative emotions.

[0470] "Negative emotions" are negative emotions such as dissatisfaction, discomfort, anxiety, and depression experienced by the user.

[0471] "Input" is information provided by a user to a system in the form of text, speech, or the like.

[0472] The "receiving means" is a function for acquiring user input as digital data.

[0473] "Means of analysis" refers to the function of deciphering received data using natural language processing technology, etc., and understanding its contents.

[0474] "Means for generating positive perspectives and encouraging messages" is a function that generates messages that make users feel positive based on the analysis results.

[0475] The "means for returning" is a function for sending the generated message to the user.

[0476] The "means for displaying" is a function for displaying the returned message in a form that can be visually confirmed by the user.

[0477] "Natural language processing technology" is an artificial intelligence technology that analyzes text data and understands its meaning and intent.

[0478] A "generative AI model" is a machine learning model used to generate appropriate responses to user input.

[0479] A "prompt sentence" is a text sentence that is given as specific input to a generative AI model.

[0480] "Personalized feedback" is a response that is customized according to the characteristics and circumstances of each individual user.

[0481] A "brick and mortar store" is a commercial establishment that exists in a physical location and can be visited in person by customers.

[0482] The present invention is a system that analyzes a user's input of negative emotions and generates, returns, and displays a positive perspective or encouraging message. The system includes a means for receiving an input regarding negative emotions from a user, a means for analyzing the received input and generating a positive message, a means for returning the generated message to the user, and a means for displaying the generated message to the user.

[0483] Hardware and software used

[0484] User devices: smartphones, tablets, computers, etc.

[0485] Server: performs data analysis and message generation

[0486] Natural Language Processing (NLP): SpaCy, BERT, etc.

[0487] Generative AI models: GPT-3, etc.

[0488] Data communication library: requests library

[0489] Data processing and calculation

[0490] 1. Receiving Input

[0491] The user terminal receives input about the user's negative emotions. For example, the user inputs "I made a mistake in my order and I'm a little frustrated" on a smartphone.

[0492] 2. Data transmission

[0493] The user terminal sends the received input data to the server using the requests library for data communication.

[0494] 3. Parsing the Input

[0495] The server analyzes the data received from the user using natural language processing techniques such as SpaCy and BERT, which tokenize the text data and understand its content.

[0496] 4. Message Creation

[0497] Based on the analysis results, the server uses a generative AI model (e.g., GPT-3) to generate a positive or encouraging message. It provides a prompt and asks the model to generate the message.

[0498] 5. Replying and Displaying Messages

[0499] The server returns the generated positive message to the user terminal, which displays the received message to the user.

[0500] Specific examples

[0501] User input: "I'm a little frustrated because I made a mistake with my order."

[0502] Analysis: Tokenizes user input to identify negative sentiment.

[0503] Generated message: "Everyone makes small mistakes. Please let our staff know and we'll take care of it right away."

[0504] Example prompt: User input: "I made a mistake with my order and I'm a little annoyed." ||| Positive response:

[0505] This allows the present invention to quickly address negative emotions experienced by customers in physical stores and increase customer satisfaction. In particular, by generating prompt sentences using a generative AI model, it is possible to provide positive messages customized for each individual user.

[0506] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0507] Step 1:

[0508] The user uses a smartphone device to input negative emotions or experiences into a text box. An example input is "I made a mistake in my order and I'm a little frustrated." Once input is complete, the user presses the send button to send the input data to the server. Input: Negative text from the user. Output: Text data sent to the server.

[0509] Step 2:

[0510] The server receives text data from the user. The input at this stage is the user's negative input data, and the output is the received text data itself. The server receives data using the requests library.

[0511] Step 3:

[0512] The server analyzes the received text data using natural language processing technology. This analysis uses SpaCy and BERT to tokenize the text and understand its content. The input is the received text data, and the output is the analyzed negative sentiment type and content.

[0513] Step 4:

[0514] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a positive perspective or encouraging message. Specifically, the server inserts the user's negative input into a prompt and asks the AI ​​model to generate a message. The input is the analysis result and the prompt to be generated, and the output is a positive message. An example of a prompt: "I'm a little frustrated because I made a mistake with my order ||| Positive response:"

[0515] Step 5:

[0516] The server sends the generated positive message back to the user terminal. At this stage, the input is the generated positive message and the output is the data sent to the user terminal. The requests library is used for data communication.

[0517] Step 6:

[0518] The user terminal receives the positive message returned from the server and displays it to the user. The input is the positive message received from the server, and the output is the message to be displayed. Specifically, the message is displayed on the smartphone screen.

[0519] 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.

[0520] The present invention relates to a system that receives input about a user's negative emotions, analyzes the input, generates positive perspectives and encouraging messages, and combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[0521] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[0522] The device then receives the user's input and sends the data to a server, which uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[0523] A distinctive feature of the present invention is the use of an emotion engine. This emotion engine analyzes the user's emotional state from their input and generates more appropriate positive perspectives and encouraging messages based on that emotional state. The emotion engine reads emotional changes and tone from the user's input data and generates the most appropriate message accordingly.

[0524] Furthermore, the emotion engine can refer to the user's past input data to understand the user's emotional tendencies, providing more personalized feedback. This feature not only helps convert negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[0525] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[0526] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[0527] As a specific example, the following flow can be considered.

[0528] 1. User tweet: The user types into the device, "I made a big mistake at work."

[0529] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[0530] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[0531] 4. Use of emotion engine: The emotion engine analyzes the user's input and understands their emotional state.

[0532] 5. Message generation: The server generates a positive perspective or personalized message based on the results of the emotion engine.

[0533] 6. Returning the message: The server returns the generated message to the terminal.

[0534] 7. Display to user: The terminal displays the received message to the user.

[0535] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[0536] The processing flow will be explained below.

[0537] Step 1:

[0538] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[0539] Step 2:

[0540] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[0541] Step 3:

[0542] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[0543] Step 4:

[0544] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[0545] Step 5:

[0546] The server uses an emotion engine to analyze the received user input, which analyzes the user's emotional state from the input and is used to generate positive messages based on the emotional state.

[0547] Step 6:

[0548] The server uses natural language processing technology to generate positive prompt-based messages, which then incorporate the results of the emotion engine to generate more personalized messages.

[0549] Step 7:

[0550] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[0551] Step 8:

[0552] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[0553] Step 9:

[0554] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[0555] Step 10:

[0556] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[0557] Through this series of steps, the present invention provides a system that converts a user's negative emotions into positive ones and reduces mental burden. Furthermore, the emotion engine provides more appropriate feedback based on the user's emotional state, which is also effective for long-term emotion management.

[0558] Example 2

[0559] 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."

[0560] Conventional systems have had problems in responding adequately to users' negative emotions, making it difficult to contribute to improving users' mental stability and happiness. In particular, it was difficult to accurately grasp emotional changes and tone and provide appropriate messages accordingly, resulting in a lack of personalized feedback. Furthermore, analysis using natural language processing alone was unable to deeply understand the user's emotional state, making it impossible to generate effective positive messages for the user.

[0561] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0562] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input using natural language processing technology, means for analyzing the user's emotional state using an emotion engine, means for generating a positive perspective or an encouraging message using a generative AI model based on the analysis results, means for returning the generated message to the user, and means for understanding the user's emotional tendencies by referring to the user's past input data, thereby enabling a deep understanding of the user's emotions and providing personalized feedback.

[0563] "User" refers to the entity that uses this system to input negative emotions.

[0564] "Input" refers to text data that a user provides to a system through a terminal.

[0565] "Emotional input" refers to text data that allows a user to express their emotional state or feelings.

[0566] "Terminal" refers to an electronic device used by a user to provide input and communicate with the system, including, for example, a smartphone, a personal computer, or a tablet.

[0567] "Means for receiving" refers to the function of the terminal to receive user input and send it to the server.

[0568] "Means for transmitting" refers to a function for transmitting input data received by the terminal to the server.

[0569] "Server" refers to a computer system for analyzing received data, generating positive messages, and returning results to the terminal.

[0570] "Means for analyzing" refers to a function for analyzing received input data in a specific way (e.g., natural language processing technology).

[0571] "Natural language processing technology" refers to methods and technologies for analyzing text data and understanding its meaning. Specifically, it includes keyword extraction, sentiment analysis, etc.

[0572] An "emotion engine" refers to algorithms and technologies for analyzing a user's emotional state from input text data.

[0573] "Generative AI models" refer to artificial intelligence algorithms that generate text based on input data, such as OpenAI GPT-3.

[0574] "Means for generation" refers to the function for generating positive perspectives and encouraging messages based on the analysis results.

[0575] "Means for returning" refers to a function for returning a generated message from the server to the terminal.

[0576] The "means for displaying" refers to a function for displaying a message received by the terminal to the user.

[0577] "Past input data" refers to input data that a user has previously provided to the system.

[0578] "Personalized feedback" refers to appropriate response messages provided based on a user's individual characteristics and emotional tendencies.

[0579] The present invention relates to a system that receives input about a user's negative emotions, analyzes it, generates positive perspectives and encouraging messages, and further combines it with an emotion engine that recognizes the user's emotions.

[0580] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses may be a smartphone, PC, tablet, etc. The device receives this input and sends the data to a server via the Internet.

[0581] The server uses natural language processing (NLP) techniques to analyze the received data. Specifically, it uses the Python libraries NLTK and spaCy to analyze the specific negative content of the user's input. For example, from the input "I made a big mistake at work," it extracts "work" and "mistake" as important keywords and identifies the negative content.

[0582] The server then uses an emotion engine (e.g., Microsoft Azure's Text Analytics API) to analyze the user's emotional state. This engine reads the emotional inflection and tone of the user's input and generates an optimal positive message based on that emotional state.

[0583] Based on the analysis results of the emotion engine, the server uses a generative AI model (e.g., OpenAI GPT-3) to generate positive and encouraging messages. Examples of prompts for the generative AI model include the following:

[0584] User:

[0585] "I made a big mistake at work. What should I do?"

[0586] System (generative AI model):

[0587] "Failure is an opportunity to grow. You can do better next time. You're a hard worker, so use that experience to improve yourself even more."

[0588] The server then sends the generated message back to the device, which then displays it on its user interface. This allows the user to reframe negative emotions and situations in a positive way, reducing their mental burden.

[0589] Furthermore, the system can refer to the user's past input data to understand their emotional tendencies, providing more personalized feedback. This feature not only helps to turn negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[0590] In this way, the system achieves a series of steps from analyzing the user's emotions to generating personalized positive messages, contributing to improving the user's mental stability and sense of happiness.

[0591] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0592] Step 1: Getting User Input

[0593] The user uses the device to input a tweet about a negative emotion. For example, the user might type, "I made a big mistake at work," into the device's input field. This input is saved as text data on the device.

[0594] Step 2: Submitting input data

[0595] The device converts the received text data into JSON format and sends it to the server using an HTTP POST request. The input data (tweets) is sent to the server via the Internet, and the server receives the data.

[0596] Step 3: Data analysis with natural language processing (NLP)

[0597] The server analyzes the received JSON data. First, it uses Python libraries such as NLTK and spaCy to extract important keywords and sentiment from users' tweets. For example, negative sentiment can be identified from keywords such as "work" and "big mistake." This process makes it possible to determine the type of negative content contained in the input data.

[0598] Step 4: Emotional state analysis by the emotion engine

[0599] The server then calls an emotion engine based on the extracted keywords and context information. Here, the engine uses, for example, Microsoft Azure's Text Analytics API to perform a more detailed analysis of the user's emotional state. The engine identifies the intensity and type of emotion from the user's tweets and returns that information. Based on the input data and the analysis results, the emotional state (e.g., "sadness intensity 50%) is obtained.

[0600] Step 5: Generate a positive message

[0601] Based on the analysis results of the emotion engine, the server inputs a prompt into a generative AI model (e.g., OpenAI GPT-3) to generate a positive message. For example, the prompt could read, "The user said, 'I made a big mistake at work.' Please provide this user with an encouraging message," and the generative AI model would generate a message saying, "Failure is an opportunity for growth. You'll do better next time." This process generates an encouraging message that is appropriate for the user's emotional state.

[0602] Step 6: Returning the message

[0603] The server converts the generated positive message into JSON format and returns it to the terminal as an HTTP response, where the generated message is output and sent to the terminal.

[0604] Step 7: Display a message to the user

[0605] The device extracts positive messages from the received JSON data and displays them to the user. For example, a pop-up window on the device might say, "Failure is an opportunity for growth. You can do better next time." This process allows the user to receive encouraging messages and turn negative emotions into positive ones.

[0606] (Application example 2)

[0607] 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."

[0608] The goal is to improve the purchasing experience by reducing the negative emotions and stress felt by users when shopping in virtual stores. Also, a system is needed that can detect the user's emotional state in real time and provide appropriate feedback.

[0609] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input related to negative emotions from the user, means for analyzing the received input and generating a positive perspective or an encouraging message, means for returning the generated message to the user, and means for displaying the message to the user using smart glasses. This makes it possible to convert the user's negative emotions into positive ones in real time and improve the shopping experience in the virtual store.

[0610] (definition statement)

[0611] The "means for receiving input relating to negative emotions from the user" is a function that allows the user to input information relating to negative emotions via the terminal and receive the information.

[0612] "Means for analyzing received input and generating positive perspectives and encouraging messages" is a function for processing received data on the user's negative emotions and generating positive replies and encouraging messages.

[0613] The "means for returning the generated message to the user" is a function for sending the generated positive message to the user's terminal.

[0614] The "means for displaying a message to a user using smart glasses" is a function for displaying the generated positive message on the display of the smart glasses.

[0615] "Natural language processing technology" refers to algorithms and methods for analyzing and generating natural language data, and is a technology used to understand and process user input text.

[0616] "Personalized feedback" is a method for providing optimized, individual feedback based on the characteristics and past data of each individual user.

[0617] MODE FOR CARRYING OUT THE INVENTION

[0618] The present invention is a system that converts negative emotions felt by users while shopping in a virtual store into positive ones. In this system, users input their emotions through smart glasses, and the data is analyzed by a server, which then generates and returns an appropriate message.

[0619] System construction

[0620] Smart Glasses:

[0621] Users wear smart glasses to shop in a virtual store, and the glasses allow users to input voice and text, and provide real-time feedback.

[0622] server:

[0623] The server has the following main roles:

[0624] 1. Receiving emotion input: Receive data about negative emotions input by the user through the smart glasses.

[0625] 2. Data analysis: The received data is analyzed on the server using natural language processing technology, specifically Google Cloud Natural Language API and IBM Watson Emotion Analysis.

[0626] 3. Positive Message Generation: The emotion engine analyzes the user's input and generates appropriate positive messages, taking into account the user's past data to provide personalized feedback.

[0627] Program processing

[0628] First, the user inputs text such as "I'm having trouble finding the item in stock" into the smart glasses. This input is sent to the server via the communication module built into the smart glasses.

[0629] Server-side steps:

[0630] 1. The server receives the input data.

[0631] 2. Analyze the received data using natural language processing technology to identify negative emotional content.

[0632] 3. Based on the results of this analysis, the emotion engine generates positive perspectives and encouraging messages.

[0633] 4. The generated message might be something like, "You may find some stock online. If not, look forward to our new models!"

[0634] Steps for smart glasses:

[0635] 1. Receive a positive message back from the server.

[0636] 2. Display the received message on the smart glasses display.

[0637] Specific examples

[0638] If a user searches for a discontinued product in stock in your virtual store but can't find it:

[0639] Input statement: The user types "I'm having trouble finding the item in stock" into the smart glasses.

[0640] Output message: The smart glasses display will display the message "Stock may also be found online. If not, look forward to our new model!"

[0641] Prompt Sentence Examples

[0642] User input: "I'm having trouble finding it in stock."

[0643] Sentiment analysis result: "user_feelings: frustration"

[0644] Appropriate positive message: "You may find stock online. If you still can't find it, look forward to our new models!"

[0645] This makes shopping in a virtual store a more positive and stress-free experience, increasing users' motivation to buy.

[0646] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0647] System program processing flow

[0648] Step 1:

[0649] The user uses the smart glasses to input a message related to a negative emotion. For example, the user may input, "I'm having trouble finding the item in stock." The smart glasses receive this input and store it in their internal memory.

[0650] Step 2:

[0651] The device (smart glasses) sends the received input data about negative emotions to the server, where the input data is packaged in JSON format and sent as an HTTP request.

[0652] Step 3:

[0653] The server receives this HTTP request and parses the JSON data. It extracts the user's input message from the received data and inputs it into a natural language processing model. The natural language processing model (e.g., Google Cloud Natural Language API) analyzes the input text for negative sentiment and identifies the emotional state.

[0654] Step 4:

[0655] The server uses an emotion engine to generate positive and encouraging messages based on the analysis results. The generative AI model creates appropriate messages by taking into account the user's context and emotional state. For example, it generates a message saying, "You may be able to find the item online. If not, look forward to our new model!"

[0656] Step 5:

[0657] The server then packages the generated message in JSON format again and sends it back to the device (smart glasses). The server then sends data containing a positive message as an HTTP response.

[0658] Step 6:

[0659] The device (smart glasses) receives this HTTP response and extracts a positive message from the JSON data. It displays the extracted message on the display and notifies the user. At this time, the display shows the following message: "Stock may also be found online. If not, look forward to our new model!"

[0660] summary

[0661] Step 1: The user inputs negative emotions into the smart glasses.

[0662] Step 2: The terminal sends the input data to the server.

[0663] Step 3: The server receives the data and analyzes it using a natural language processing model.

[0664] Step 4: The server generates a positive message using the emotion engine.

[0665] Step 5: The server sends the generated message back to the terminal.

[0666] Step 6: The device receives the message and displays it on the smart glasses display.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] [Third embodiment]

[0671] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0672] 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.

[0673] 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).

[0674] 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.

[0675] 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.

[0676] 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).

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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."

[0683] The present invention relates to a system that receives input from a user regarding negative emotions, analyzes the input, and generates and returns messages of positive perspective and encouragement. An embodiment of this system, i.e., specific operations, will be described in detail below.

[0684] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[0685] The device then receives the user's tweets and sends them to a server. The server then uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[0686] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[0687] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[0688] As a specific example, the following flow can be considered.

[0689] 1. User tweet: The user types into the device, "I made a big mistake at work."

[0690] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[0691] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[0692] 4. Message generation: The server generates a positive message based on the analysis results.

[0693] 5. Returning the message: The server returns the generated message to the terminal.

[0694] 6. Display to user: The terminal displays the received message to the user.

[0695] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[0699] Step 2:

[0700] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[0701] Step 3:

[0702] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[0703] Step 4:

[0704] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[0705] Step 5:

[0706] The server uses natural language processing techniques to analyze the received user input, specifically calling the OpenAI API to generate positive prompt-based messages.

[0707] Step 6:

[0708] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[0709] Step 7:

[0710] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[0711] Step 8:

[0712] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[0713] Step 9:

[0714] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[0715] This series of steps helps users shift their negative emotions into a positive perspective, reducing mental stress.

[0716] Example 1

[0717] 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."

[0718] In modern society, users often experience many negative emotions and stress in their daily lives and at work. Ignoring these emotions can have a negative impact on mental health, so appropriate measures are needed. Conventional systems have difficulty providing prompt and appropriate feedback on individual users' emotions and lack the means to convert negative emotions into positive ones.

[0719] 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.

[0720] In this invention, the server includes means for receiving text related to negative emotions from the user, means for transmitting the received text data to the server, means for analyzing the data received by the server based on natural language processing technology, means for generating a positive perspective or an encouraging message based on the analysis results, means for returning the generated message from the server to the terminal, and means for displaying the returned message to the user. This makes it possible to quickly and effectively convert the user's negative emotions into a positive perspective and reduce mental burden.

[0721] "User" refers to a person who uses the system to input text about negative emotions.

[0722] "Text" refers to character string data input by the user.

[0723] "Terminal" refers to an electronic device used by a user, such as a smartphone, PC, or tablet.

[0724] A "server" refers to a computer system that receives data sent from a terminal and performs processing such as analysis and message generation.

[0725] "Natural language processing technology" is a technology for analyzing text data, and refers to a method for understanding and processing linguistic content.

[0726] "Generative AI model" refers to artificial intelligence technology that generates positive messages based on user input.

[0727] "Positive perspectives and encouraging messages" refer to sentences that provide a positive outlook and comfort to users who have negative feelings.

[0728] "Personalized feedback" refers to responses or messages that are individually tailored to each user's characteristics and situation.

[0729] "Analysis" refers to the process of understanding the meaning and sentiment of text data entered by a user.

[0730] "Return" refers to the act of sending the generated message back from the server to the terminal.

[0731] "Display" refers to the act of visually presenting a message to a user on a terminal.

[0732] This invention relates to a system that receives input about negative emotions from a user, analyzes the input, and generates and returns a message of positive perspective or encouragement. The system includes a terminal through which the user inputs negative emotions, a server that analyzes the received data and generates a positive message, and a means for displaying the generated message to the user.

[0733] First, a user inputs text related to negative emotions using a device such as a smartphone, PC, or tablet. For example, if a user inputs "I made a big mistake at work," the device receives this text and sends the text data to the server's API endpoint using an HTTP POST request.

[0734] The server analyzes the received text data using natural language processing technology. For analysis, it uses a generative AI model such as OpenAI's GPT-3. The server analyzes the sentiment of the text and identifies negative elements. For example, it can extract negative sentiment from the sentence, "I made a big mistake at work."

[0735] The server then generates a message of positive perspective and encouragement based on the analysis results. Using a generative AI model, it generates personalized messages based on the user's characteristics and circumstances. For example, it generates a message like, "Failure is an opportunity for growth. You can do better next time."

[0736] The generated positive message is returned from the server to the device. The server uses an HTTP response to send the message to the device. The device receives the returned message and displays it in the user interface. For example, a pop-up message might say, "Failure is an opportunity to grow. You'll do better next time."

[0737] Some examples of specific prompts include:

[0738] How can you generate a positive message when a user says, "I made a big mistake at work"?

[0739] Can you give me an example of API communication for transferring user tweets to the server?

[0740] How can we analyze negative sentiment data and generate positive messages?

[0741] Create a positive response to the negative, "I made a big mistake at work."

[0742] What is an example of an API call to send the generated positive message back to the device?

[0743] Can you give me an example of a UI design that displays positive messages received to the user?

[0744] This allows users to quickly and effectively transform negative emotions into positive ones, reducing mental strain.

[0745] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0746] Step 1:

[0747] User Input

[0748] Users use devices such as smartphones, computers, and tablets to input text related to negative emotions.

[0749] Input: Negative text (e.g., "I made a big mistake at work.")

[0750] Specific behavior: The device's user interface displays a text input field and a submit button, the user enters text, and presses the submit button.

[0751] Step 2:

[0752] Sending input data

[0753] The terminal receives the text data entered by the user and sends it to the server using an HTTP POST request.

[0754] Input: Negative text data entered by the user

[0755] Output: Text data is sent to the server's API endpoint

[0756] Specific operation: The device constructs an HTTP request and sends data to the server over the Internet.

[0757] Step 3:

[0758] Receiving data

[0759] The server receives the text data sent from the terminal.

[0760] Input: Negative text data sent from the device

[0761] Output: Text data stored in the server's memory or storage

[0762] Specific operation: The server receives the request at the API endpoint and stores the text data in memory or in a database.

[0763] Step 4:

[0764] Data analysis

[0765] The server analyzes the received text data using natural language processing (NLP) technology and uses a generative AI model to identify negative elements.

[0766] Input: Saved negative text data

[0767] Output: Analysis results (identification of negative elements and classification of emotions)

[0768] Specific operation: The server calls an NLP library or generative AI model (e.g., GPT-3) to perform sentiment analysis of text data and extract negative elements.

[0769] Step 5:

[0770] Message Generation

[0771] The server generates positive perspectives and encouraging messages based on the analysis results.

[0772] Input: Analysis results (information on negative elements)

[0773] Output: Generated positive message (e.g., "Failure is an opportunity to grow. You can do better next time.")

[0774] Specific operation: The server uses the generative AI model to generate an appropriate positive message.

[0775] Step 6:

[0776] Message bounce

[0777] The server returns the generated positive message to the terminal in an HTTP response.

[0778] Input: Generated positive message

[0779] Output: HTTP response with a positive message

[0780] Specific operation: The server constructs an HTTP response and sends it back to the device over the Internet.

[0781] Step 7:

[0782] What the user sees

[0783] The terminal receives a positive message returned from the server.

[0784] Input: Positive message returned by the server

[0785] Output: A positive message displayed in the user interface.

[0786] Specific operation: The device receives the HTTP response and displays a message in the user interface (e.g., a popup or message field).

[0787] (Application example 1)

[0788] 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."

[0789] In modern society, users experience a wide range of negative emotions. For example, customers may feel stressed if they make an order error or encounter a service issue while shopping at a physical store. If such negative experiences are left unaddressed, it can lead to lower customer satisfaction and fewer repeat visits. However, there is currently no system that can instantly transform these negative emotions into positive ones.

[0790] 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.

[0791] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input and generating a message of positive perspective or encouragement, means for returning the generated message to the user, and means for displaying the generated message to the user. This allows immediate positive feedback to be provided to customers who have experienced negative emotions in a physical store, improving customer satisfaction and the rate of repeat visits.

[0792] A "user" is a person who uses the system and is the subject who inputs negative emotions.

[0793] "Negative emotions" are negative emotions such as dissatisfaction, discomfort, anxiety, and depression experienced by the user.

[0794] "Input" is information provided by a user to a system in the form of text, speech, or the like.

[0795] The "receiving means" is a function for acquiring user input as digital data.

[0796] "Means of analysis" refers to the function of deciphering received data using natural language processing technology, etc., and understanding its contents.

[0797] "Means for generating positive perspectives and encouraging messages" is a function that generates messages that make users feel positive based on the analysis results.

[0798] The "means for returning" is a function for sending the generated message to the user.

[0799] The "means for displaying" is a function for displaying the returned message in a form that can be visually confirmed by the user.

[0800] "Natural language processing technology" is an artificial intelligence technology that analyzes text data and understands its meaning and intent.

[0801] A "generative AI model" is a machine learning model used to generate appropriate responses to user input.

[0802] A "prompt sentence" is a text sentence that is given as specific input to a generative AI model.

[0803] "Personalized feedback" is a response that is customized according to the characteristics and circumstances of each individual user.

[0804] A "brick and mortar store" is a commercial establishment that exists in a physical location and can be visited in person by customers.

[0805] The present invention is a system that analyzes a user's input of negative emotions and generates, returns, and displays a positive perspective or encouraging message. The system includes a means for receiving an input regarding negative emotions from a user, a means for analyzing the received input and generating a positive message, a means for returning the generated message to the user, and a means for displaying the generated message to the user.

[0806] Hardware and software used

[0807] User devices: smartphones, tablets, computers, etc.

[0808] Server: performs data analysis and message generation

[0809] Natural Language Processing (NLP): SpaCy, BERT, etc.

[0810] Generative AI models: GPT-3, etc.

[0811] Data communication library: requests library

[0812] Data processing and calculation

[0813] 1. Receiving Input

[0814] The user terminal receives input about the user's negative emotions. For example, the user inputs "I made a mistake in my order and I'm a little frustrated" on a smartphone.

[0815] 2. Data transmission

[0816] The user terminal sends the received input data to the server using the requests library for data communication.

[0817] 3. Parsing the Input

[0818] The server analyzes the data received from the user using natural language processing techniques such as SpaCy and BERT, which tokenize the text data and understand its content.

[0819] 4. Message Creation

[0820] Based on the analysis results, the server uses a generative AI model (e.g., GPT-3) to generate a positive or encouraging message. It provides a prompt and asks the model to generate the message.

[0821] 5. Replying and Displaying Messages

[0822] The server returns the generated positive message to the user terminal, which displays the received message to the user.

[0823] Specific examples

[0824] User input: "I'm a little frustrated because I made a mistake with my order."

[0825] Analysis: Tokenizes user input to identify negative sentiment.

[0826] Generated message: "Everyone makes small mistakes. Please let our staff know and we'll take care of it right away."

[0827] Example prompt: User input: "I made a mistake with my order and I'm a little annoyed." ||| Positive response:

[0828] This allows the present invention to quickly address negative emotions experienced by customers in physical stores and increase customer satisfaction. In particular, by generating prompt sentences using a generative AI model, it is possible to provide positive messages customized for each individual user.

[0829] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0830] Step 1:

[0831] The user uses a smartphone device to input negative emotions or experiences into a text box. An example input is "I made a mistake in my order and I'm a little frustrated." Once input is complete, the user presses the send button to send the input data to the server. Input: Negative text from the user. Output: Text data sent to the server.

[0832] Step 2:

[0833] The server receives text data from the user. The input at this stage is the user's negative input data, and the output is the received text data itself. The server receives data using the requests library.

[0834] Step 3:

[0835] The server analyzes the received text data using natural language processing technology. This analysis uses SpaCy and BERT to tokenize the text and understand its content. The input is the received text data, and the output is the analyzed negative sentiment type and content.

[0836] Step 4:

[0837] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a positive perspective or encouraging message. Specifically, the server inserts the user's negative input into a prompt and asks the AI ​​model to generate a message. The input is the analysis result and the prompt to be generated, and the output is a positive message. An example of a prompt: "I'm a little frustrated because I made a mistake with my order ||| Positive response:"

[0838] Step 5:

[0839] The server sends the generated positive message back to the user terminal. At this stage, the input is the generated positive message and the output is the data sent to the user terminal. The requests library is used for data communication.

[0840] Step 6:

[0841] The user terminal receives the positive message returned from the server and displays it to the user. The input is the positive message received from the server, and the output is the message to be displayed. Specifically, the message is displayed on the smartphone screen.

[0842] 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.

[0843] The present invention relates to a system that receives input about a user's negative emotions, analyzes the input, generates positive perspectives and encouraging messages, and combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[0844] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[0845] The device then receives the user's input and sends the data to a server, which uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[0846] A distinctive feature of the present invention is the use of an emotion engine. This emotion engine analyzes the user's emotional state from their input and generates more appropriate positive perspectives and encouraging messages based on that emotional state. The emotion engine reads emotional changes and tone from the user's input data and generates the most appropriate message accordingly.

[0847] Furthermore, the emotion engine can refer to the user's past input data to understand the user's emotional tendencies, providing more personalized feedback. This feature not only helps convert negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[0848] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[0849] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[0850] As a specific example, the following flow can be considered.

[0851] 1. User tweet: The user types into the device, "I made a big mistake at work."

[0852] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[0853] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[0854] 4. Use of emotion engine: The emotion engine analyzes the user's input and understands their emotional state.

[0855] 5. Message generation: The server generates a positive perspective or personalized message based on the results of the emotion engine.

[0856] 6. Returning the message: The server returns the generated message to the terminal.

[0857] 7. Display to user: The terminal displays the received message to the user.

[0858] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[0859] The processing flow will be explained below.

[0860] Step 1:

[0861] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[0862] Step 2:

[0863] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[0864] Step 3:

[0865] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[0866] Step 4:

[0867] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[0868] Step 5:

[0869] The server uses an emotion engine to analyze the received user input, which analyzes the user's emotional state from the input and is used to generate positive messages based on the emotional state.

[0870] Step 6:

[0871] The server uses natural language processing technology to generate positive prompt-based messages, which then incorporate the results of the emotion engine to generate more personalized messages.

[0872] Step 7:

[0873] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[0874] Step 8:

[0875] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[0876] Step 9:

[0877] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[0878] Step 10:

[0879] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[0880] Through this series of steps, the present invention provides a system that converts a user's negative emotions into positive ones and reduces mental burden. Furthermore, the emotion engine provides more appropriate feedback based on the user's emotional state, which is also effective for long-term emotion management.

[0881] Example 2

[0882] 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."

[0883] Conventional systems have had problems in responding adequately to users' negative emotions, making it difficult to contribute to improving users' mental stability and happiness. In particular, it was difficult to accurately grasp emotional changes and tone and provide appropriate messages accordingly, resulting in a lack of personalized feedback. Furthermore, analysis using natural language processing alone was unable to deeply understand the user's emotional state, making it impossible to generate effective positive messages for the user.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0885] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input using natural language processing technology, means for analyzing the user's emotional state using an emotion engine, means for generating a positive perspective or an encouraging message using a generative AI model based on the analysis results, means for returning the generated message to the user, and means for understanding the user's emotional tendencies by referring to the user's past input data, thereby enabling a deep understanding of the user's emotions and providing personalized feedback.

[0886] "User" refers to the entity that uses this system to input negative emotions.

[0887] "Input" refers to text data that a user provides to a system through a terminal.

[0888] "Emotional input" refers to text data that allows a user to express their emotional state or feelings.

[0889] "Terminal" refers to an electronic device used by a user to provide input and communicate with the system, including, for example, a smartphone, a personal computer, or a tablet.

[0890] "Means for receiving" refers to the function of the terminal to receive user input and send it to the server.

[0891] "Means for transmitting" refers to a function for transmitting input data received by the terminal to the server.

[0892] "Server" refers to a computer system for analyzing received data, generating positive messages, and returning results to the terminal.

[0893] "Means for analyzing" refers to a function for analyzing received input data in a specific way (e.g., natural language processing technology).

[0894] "Natural language processing technology" refers to methods and technologies for analyzing text data and understanding its meaning. Specifically, it includes keyword extraction, sentiment analysis, etc.

[0895] An "emotion engine" refers to algorithms and technologies for analyzing a user's emotional state from input text data.

[0896] "Generative AI models" refer to artificial intelligence algorithms that generate text based on input data, such as OpenAI GPT-3.

[0897] "Means for generation" refers to the function for generating positive perspectives and encouraging messages based on the analysis results.

[0898] "Means for returning" refers to a function for returning a generated message from the server to the terminal.

[0899] The "means for displaying" refers to a function for displaying a message received by the terminal to the user.

[0900] "Past input data" refers to input data that a user has previously provided to the system.

[0901] "Personalized feedback" refers to appropriate response messages provided based on a user's individual characteristics and emotional tendencies.

[0902] The present invention relates to a system that receives input about a user's negative emotions, analyzes it, generates positive perspectives and encouraging messages, and further combines it with an emotion engine that recognizes the user's emotions.

[0903] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses may be a smartphone, PC, tablet, etc. The device receives this input and sends the data to a server via the Internet.

[0904] The server uses natural language processing (NLP) techniques to analyze the received data. Specifically, it uses the Python libraries NLTK and spaCy to analyze the specific negative content of the user's input. For example, from the input "I made a big mistake at work," it extracts "work" and "mistake" as important keywords and identifies the negative content.

[0905] The server then uses an emotion engine (e.g., Microsoft Azure's Text Analytics API) to analyze the user's emotional state. This engine reads the emotional inflection and tone of the user's input and generates an optimal positive message based on that emotional state.

[0906] Based on the analysis results of the emotion engine, the server uses a generative AI model (e.g., OpenAI GPT-3) to generate positive and encouraging messages. Examples of prompts for the generative AI model include the following:

[0907] User:

[0908] "I made a big mistake at work. What should I do?"

[0909] System (generative AI model):

[0910] "Failure is an opportunity to grow. You can do better next time. You're a hard worker, so use that experience to improve yourself even more."

[0911] The server then sends the generated message back to the device, which then displays it on its user interface. This allows the user to reframe negative emotions and situations in a positive way, reducing their mental burden.

[0912] Furthermore, the system can refer to the user's past input data to understand their emotional tendencies, providing more personalized feedback. This feature not only helps to turn negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[0913] In this way, the system achieves a series of steps from analyzing the user's emotions to generating personalized positive messages, contributing to improving the user's mental stability and sense of happiness.

[0914] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0915] Step 1: Getting User Input

[0916] The user uses the device to input a tweet about a negative emotion. For example, the user might type, "I made a big mistake at work," into the device's input field. This input is saved as text data on the device.

[0917] Step 2: Submitting input data

[0918] The device converts the received text data into JSON format and sends it to the server using an HTTP POST request. The input data (tweets) is sent to the server via the Internet, and the server receives the data.

[0919] Step 3: Data analysis with natural language processing (NLP)

[0920] The server analyzes the received JSON data. First, it uses Python libraries such as NLTK and spaCy to extract important keywords and sentiment from users' tweets. For example, negative sentiment can be identified from keywords such as "work" and "big mistake." This process makes it possible to determine the type of negative content contained in the input data.

[0921] Step 4: Emotional state analysis by the emotion engine

[0922] The server then calls an emotion engine based on the extracted keywords and context information. Here, the engine uses, for example, Microsoft Azure's Text Analytics API to perform a more detailed analysis of the user's emotional state. The engine identifies the intensity and type of emotion from the user's tweets and returns that information. Based on the input data and the analysis results, the emotional state (e.g., "sadness intensity 50%) is obtained.

[0923] Step 5: Generate a positive message

[0924] Based on the analysis results of the emotion engine, the server inputs a prompt into a generative AI model (e.g., OpenAI GPT-3) to generate a positive message. For example, the prompt could read, "The user said, 'I made a big mistake at work.' Please provide this user with an encouraging message," and the generative AI model would generate a message saying, "Failure is an opportunity for growth. You'll do better next time." This process generates an encouraging message that is appropriate for the user's emotional state.

[0925] Step 6: Returning the message

[0926] The server converts the generated positive message into JSON format and returns it to the terminal as an HTTP response, where the generated message is output and sent to the terminal.

[0927] Step 7: Display a message to the user

[0928] The device extracts positive messages from the received JSON data and displays them to the user. For example, a pop-up window on the device might say, "Failure is an opportunity for growth. You can do better next time." This process allows the user to receive encouraging messages and turn negative emotions into positive ones.

[0929] (Application example 2)

[0930] 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."

[0931] The goal is to improve the purchasing experience by reducing the negative emotions and stress felt by users when shopping in virtual stores. Also, a system is needed that can detect the user's emotional state in real time and provide appropriate feedback.

[0932] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input related to negative emotions from the user, means for analyzing the received input and generating a positive perspective or an encouraging message, means for returning the generated message to the user, and means for displaying the message to the user using smart glasses. This makes it possible to convert the user's negative emotions into positive ones in real time and improve the shopping experience in the virtual store.

[0933] (definition statement)

[0934] The "means for receiving input relating to negative emotions from the user" is a function that allows the user to input information relating to negative emotions via the terminal and receive the information.

[0935] "Means for analyzing received input and generating positive perspectives and encouraging messages" is a function for processing received data on the user's negative emotions and generating positive replies and encouraging messages.

[0936] The "means for returning the generated message to the user" is a function for sending the generated positive message to the user's terminal.

[0937] The "means for displaying a message to a user using smart glasses" is a function for displaying the generated positive message on the display of the smart glasses.

[0938] "Natural language processing technology" refers to algorithms and methods for analyzing and generating natural language data, and is a technology used to understand and process user input text.

[0939] "Personalized feedback" is a method for providing optimized, individual feedback based on the characteristics and past data of each individual user.

[0940] MODE FOR CARRYING OUT THE INVENTION

[0941] The present invention is a system that converts negative emotions felt by users while shopping in a virtual store into positive ones. In this system, users input their emotions through smart glasses, and the data is analyzed by a server, which then generates and returns an appropriate message.

[0942] System construction

[0943] Smart Glasses:

[0944] Users wear smart glasses to shop in a virtual store, and the glasses allow users to input voice and text, and provide real-time feedback.

[0945] server:

[0946] The server has the following main roles:

[0947] 1. Receiving emotion input: Receive data about negative emotions input by the user through the smart glasses.

[0948] 2. Data analysis: The received data is analyzed on the server using natural language processing technology, specifically Google Cloud Natural Language API and IBM Watson Emotion Analysis.

[0949] 3. Positive Message Generation: The emotion engine analyzes the user's input and generates appropriate positive messages, taking into account the user's past data to provide personalized feedback.

[0950] Program processing

[0951] First, the user inputs text such as "I'm having trouble finding the item in stock" into the smart glasses. This input is sent to the server via the communication module built into the smart glasses.

[0952] Server-side steps:

[0953] 1. The server receives the input data.

[0954] 2. Analyze the received data using natural language processing technology to identify negative emotional content.

[0955] 3. Based on the results of this analysis, the emotion engine generates positive perspectives and encouraging messages.

[0956] 4. The generated message might be something like, "You may find some stock online. If not, look forward to our new models!"

[0957] Steps for smart glasses:

[0958] 1. Receive a positive message back from the server.

[0959] 2. Display the received message on the smart glasses display.

[0960] Specific examples

[0961] If a user searches for a discontinued product in stock in your virtual store but can't find it:

[0962] Input statement: The user types "I'm having trouble finding the item in stock" into the smart glasses.

[0963] Output message: The smart glasses display will display the message "Stock may also be found online. If not, look forward to our new model!"

[0964] Prompt Sentence Examples

[0965] User input: "I'm having trouble finding it in stock."

[0966] Sentiment analysis result: "user_feelings: frustration"

[0967] Appropriate positive message: "You may find stock online. If you still can't find it, look forward to our new models!"

[0968] This makes shopping in a virtual store a more positive and stress-free experience, increasing users' motivation to buy.

[0969] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0970] System program processing flow

[0971] Step 1:

[0972] The user uses the smart glasses to input a message related to a negative emotion. For example, the user may input, "I'm having trouble finding the item in stock." The smart glasses receive this input and store it in their internal memory.

[0973] Step 2:

[0974] The device (smart glasses) sends the received input data about negative emotions to the server, where the input data is packaged in JSON format and sent as an HTTP request.

[0975] Step 3:

[0976] The server receives this HTTP request and parses the JSON data. It extracts the user's input message from the received data and inputs it into a natural language processing model. The natural language processing model (e.g., Google Cloud Natural Language API) analyzes the input text for negative sentiment and identifies the emotional state.

[0977] Step 4:

[0978] The server uses an emotion engine to generate positive and encouraging messages based on the analysis results. The generative AI model creates appropriate messages by taking into account the user's context and emotional state. For example, it generates a message saying, "You may be able to find the item online. If not, look forward to our new model!"

[0979] Step 5:

[0980] The server then packages the generated message in JSON format again and sends it back to the device (smart glasses). The server then sends data containing a positive message as an HTTP response.

[0981] Step 6:

[0982] The device (smart glasses) receives this HTTP response and extracts a positive message from the JSON data. It displays the extracted message on the display and notifies the user. At this time, the display shows the following message: "Stock may also be found online. If not, look forward to our new model!"

[0983] summary

[0984] Step 1: The user inputs negative emotions into the smart glasses.

[0985] Step 2: The terminal sends the input data to the server.

[0986] Step 3: The server receives the data and analyzes it using a natural language processing model.

[0987] Step 4: The server generates a positive message using the emotion engine.

[0988] Step 5: The server sends the generated message back to the terminal.

[0989] Step 6: The device receives the message and displays it on the smart glasses display.

[0990] 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.

[0991] 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.

[0992] 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.

[0993] [Fourth embodiment]

[0994] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0995] 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.

[0996] 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).

[0997] 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.

[0998] 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.

[0999] 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).

[1000] 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.

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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."

[1007] The present invention relates to a system that receives input from a user regarding negative emotions, analyzes the input, and generates and returns messages of positive perspective and encouragement. An embodiment of this system, i.e., specific operations, will be described in detail below.

[1008] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[1009] The device then receives the user's tweets and sends them to a server. The server then uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[1010] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[1011] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[1012] As a specific example, the following flow can be considered.

[1013] 1. User tweet: The user types into the device, "I made a big mistake at work."

[1014] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[1015] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[1016] 4. Message generation: The server generates a positive message based on the analysis results.

[1017] 5. Returning the message: The server returns the generated message to the terminal.

[1018] 6. Display to user: The terminal displays the received message to the user.

[1019] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[1020] The processing flow will be explained below.

[1021] Step 1:

[1022] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[1023] Step 2:

[1024] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[1025] Step 3:

[1026] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[1027] Step 4:

[1028] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[1029] Step 5:

[1030] The server uses natural language processing techniques to analyze the received user input, specifically calling the OpenAI API to generate positive prompt-based messages.

[1031] Step 6:

[1032] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[1033] Step 7:

[1034] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[1035] Step 8:

[1036] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[1037] Step 9:

[1038] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[1039] This series of steps helps users shift their negative emotions into a positive perspective, reducing mental stress.

[1040] Example 1

[1041] 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."

[1042] In modern society, users often experience many negative emotions and stress in their daily lives and at work. Ignoring these emotions can have a negative impact on mental health, so appropriate measures are needed. Conventional systems have difficulty providing prompt and appropriate feedback on individual users' emotions and lack the means to convert negative emotions into positive ones.

[1043] 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.

[1044] In this invention, the server includes means for receiving text related to negative emotions from the user, means for transmitting the received text data to the server, means for analyzing the data received by the server based on natural language processing technology, means for generating a positive perspective or an encouraging message based on the analysis results, means for returning the generated message from the server to the terminal, and means for displaying the returned message to the user. This makes it possible to quickly and effectively convert the user's negative emotions into a positive perspective and reduce mental burden.

[1045] "User" refers to a person who uses the system to input text about negative emotions.

[1046] "Text" refers to character string data input by the user.

[1047] "Terminal" refers to an electronic device used by a user, such as a smartphone, PC, or tablet.

[1048] A "server" refers to a computer system that receives data sent from a terminal and performs processing such as analysis and message generation.

[1049] "Natural language processing technology" is a technology for analyzing text data, and refers to a method for understanding and processing linguistic content.

[1050] "Generative AI model" refers to artificial intelligence technology that generates positive messages based on user input.

[1051] "Positive perspectives and encouraging messages" refer to sentences that provide a positive outlook and comfort to users who have negative feelings.

[1052] "Personalized feedback" refers to responses or messages that are individually tailored to each user's characteristics and situation.

[1053] "Analysis" refers to the process of understanding the meaning and sentiment of text data entered by a user.

[1054] "Return" refers to the act of sending the generated message back from the server to the terminal.

[1055] "Display" refers to the act of visually presenting a message to a user on a terminal.

[1056] This invention relates to a system that receives input about negative emotions from a user, analyzes the input, and generates and returns a message of positive perspective or encouragement. The system includes a terminal through which the user inputs negative emotions, a server that analyzes the received data and generates a positive message, and a means for displaying the generated message to the user.

[1057] First, a user inputs text related to negative emotions using a device such as a smartphone, PC, or tablet. For example, if a user inputs "I made a big mistake at work," the device receives this text and sends the text data to the server's API endpoint using an HTTP POST request.

[1058] The server analyzes the received text data using natural language processing technology. For analysis, it uses a generative AI model such as OpenAI's GPT-3. The server analyzes the sentiment of the text and identifies negative elements. For example, it can extract negative sentiment from the sentence, "I made a big mistake at work."

[1059] The server then generates a message of positive perspective and encouragement based on the analysis results. Using a generative AI model, it generates personalized messages based on the user's characteristics and circumstances. For example, it generates a message like, "Failure is an opportunity for growth. You can do better next time."

[1060] The generated positive message is returned from the server to the device. The server uses an HTTP response to send the message to the device. The device receives the returned message and displays it in the user interface. For example, a pop-up message might say, "Failure is an opportunity to grow. You'll do better next time."

[1061] Some examples of specific prompts include:

[1062] How can you generate a positive message when a user says, "I made a big mistake at work"?

[1063] Can you give me an example of API communication for transferring user tweets to the server?

[1064] How can we analyze negative sentiment data and generate positive messages?

[1065] Create a positive response to the negative, "I made a big mistake at work."

[1066] What is an example of an API call to send the generated positive message back to the device?

[1067] Can you give me an example of a UI design that displays positive messages received to the user?

[1068] This allows users to quickly and effectively transform negative emotions into positive ones, reducing mental strain.

[1069] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1070] Step 1:

[1071] User Input

[1072] Users use devices such as smartphones, computers, and tablets to input text related to negative emotions.

[1073] Input: Negative text (e.g., "I made a big mistake at work.")

[1074] Specific behavior: The device's user interface displays a text input field and a submit button, the user enters text, and presses the submit button.

[1075] Step 2:

[1076] Sending input data

[1077] The terminal receives the text data entered by the user and sends it to the server using an HTTP POST request.

[1078] Input: Negative text data entered by the user

[1079] Output: Text data is sent to the server's API endpoint

[1080] Specific operation: The device constructs an HTTP request and sends data to the server over the Internet.

[1081] Step 3:

[1082] Receiving data

[1083] The server receives the text data sent from the terminal.

[1084] Input: Negative text data sent from the device

[1085] Output: Text data stored in the server's memory or storage

[1086] Specific operation: The server receives the request at the API endpoint and stores the text data in memory or in a database.

[1087] Step 4:

[1088] Data analysis

[1089] The server analyzes the received text data using natural language processing (NLP) technology and uses a generative AI model to identify negative elements.

[1090] Input: Saved negative text data

[1091] Output: Analysis results (identification of negative elements and classification of emotions)

[1092] Specific operation: The server calls an NLP library or generative AI model (e.g., GPT-3) to perform sentiment analysis of text data and extract negative elements.

[1093] Step 5:

[1094] Message Generation

[1095] The server generates positive perspectives and encouraging messages based on the analysis results.

[1096] Input: Analysis results (information on negative elements)

[1097] Output: Generated positive message (e.g., "Failure is an opportunity to grow. You can do better next time.")

[1098] Specific operation: The server uses the generative AI model to generate an appropriate positive message.

[1099] Step 6:

[1100] Message bounce

[1101] The server returns the generated positive message to the terminal in an HTTP response.

[1102] Input: Generated positive message

[1103] Output: HTTP response with a positive message

[1104] Specific operation: The server constructs an HTTP response and sends it back to the device over the Internet.

[1105] Step 7:

[1106] What the user sees

[1107] The terminal receives a positive message returned from the server.

[1108] Input: Positive message returned by the server

[1109] Output: A positive message displayed in the user interface.

[1110] Specific operation: The device receives the HTTP response and displays a message in the user interface (e.g., a popup or message field).

[1111] (Application example 1)

[1112] 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."

[1113] In modern society, users experience a wide range of negative emotions. For example, customers may feel stressed if they make an order error or encounter a service issue while shopping at a physical store. If such negative experiences are left unaddressed, it can lead to lower customer satisfaction and fewer repeat visits. However, there is currently no system that can instantly transform these negative emotions into positive ones.

[1114] 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.

[1115] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input and generating a message of positive perspective or encouragement, means for returning the generated message to the user, and means for displaying the generated message to the user. This allows immediate positive feedback to be provided to customers who have experienced negative emotions in a physical store, improving customer satisfaction and the rate of repeat visits.

[1116] A "user" is a person who uses the system and is the subject who inputs negative emotions.

[1117] "Negative emotions" are negative emotions such as dissatisfaction, discomfort, anxiety, and depression experienced by the user.

[1118] "Input" is information provided by a user to a system in the form of text, speech, or the like.

[1119] The "receiving means" is a function for acquiring user input as digital data.

[1120] "Means of analysis" refers to the function of deciphering received data using natural language processing technology, etc., and understanding its contents.

[1121] "Means for generating positive perspectives and encouraging messages" is a function that generates messages that make users feel positive based on the analysis results.

[1122] The "means for returning" is a function for sending the generated message to the user.

[1123] The "means for displaying" is a function for displaying the returned message in a form that can be visually confirmed by the user.

[1124] "Natural language processing technology" is an artificial intelligence technology that analyzes text data and understands its meaning and intent.

[1125] A "generative AI model" is a machine learning model used to generate appropriate responses to user input.

[1126] A "prompt sentence" is a text sentence that is given as specific input to a generative AI model.

[1127] "Personalized feedback" is a response that is customized according to the characteristics and circumstances of each individual user.

[1128] A "brick and mortar store" is a commercial establishment that exists in a physical location and can be visited in person by customers.

[1129] The present invention is a system that analyzes a user's input of negative emotions and generates, returns, and displays a positive perspective or encouraging message. The system includes a means for receiving an input regarding negative emotions from a user, a means for analyzing the received input and generating a positive message, a means for returning the generated message to the user, and a means for displaying the generated message to the user.

[1130] Hardware and software used

[1131] User devices: smartphones, tablets, computers, etc.

[1132] Server: performs data analysis and message generation

[1133] Natural Language Processing (NLP): SpaCy, BERT, etc.

[1134] Generative AI models: GPT-3, etc.

[1135] Data communication library: requests library

[1136] Data processing and calculation

[1137] 1. Receiving Input

[1138] The user terminal receives input about the user's negative emotions. For example, the user inputs "I made a mistake in my order and I'm a little frustrated" on a smartphone.

[1139] 2. Data transmission

[1140] The user terminal sends the received input data to the server using the requests library for data communication.

[1141] 3. Parsing the Input

[1142] The server analyzes the data received from the user using natural language processing techniques such as SpaCy and BERT, which tokenize the text data and understand its content.

[1143] 4. Message Creation

[1144] Based on the analysis results, the server uses a generative AI model (e.g., GPT-3) to generate a positive or encouraging message. It provides a prompt and asks the model to generate the message.

[1145] 5. Replying and Displaying Messages

[1146] The server returns the generated positive message to the user terminal, which displays the received message to the user.

[1147] Specific examples

[1148] User input: "I'm a little frustrated because I made a mistake with my order."

[1149] Analysis: Tokenizes user input to identify negative sentiment.

[1150] Generated message: "Everyone makes small mistakes. Please let our staff know and we'll take care of it right away."

[1151] Example prompt: User input: "I made a mistake with my order and I'm a little annoyed." ||| Positive response:

[1152] This allows the present invention to quickly address negative emotions experienced by customers in physical stores and increase customer satisfaction. In particular, by generating prompt sentences using a generative AI model, it is possible to provide positive messages customized for each individual user.

[1153] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1154] Step 1:

[1155] The user uses a smartphone device to input negative emotions or experiences into a text box. An example input is "I made a mistake in my order and I'm a little frustrated." Once input is complete, the user presses the send button to send the input data to the server. Input: Negative text from the user. Output: Text data sent to the server.

[1156] Step 2:

[1157] The server receives text data from the user. The input at this stage is the user's negative input data, and the output is the received text data itself. The server receives data using the requests library.

[1158] Step 3:

[1159] The server analyzes the received text data using natural language processing technology. This analysis uses SpaCy and BERT to tokenize the text and understand its content. The input is the received text data, and the output is the analyzed negative sentiment type and content.

[1160] Step 4:

[1161] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a positive perspective or encouraging message. Specifically, the server inserts the user's negative input into a prompt and asks the AI ​​model to generate a message. The input is the analysis result and the prompt to be generated, and the output is a positive message. An example of a prompt: "I'm a little frustrated because I made a mistake with my order ||| Positive response:"

[1162] Step 5:

[1163] The server sends the generated positive message back to the user terminal. At this stage, the input is the generated positive message and the output is the data sent to the user terminal. The requests library is used for data communication.

[1164] Step 6:

[1165] The user terminal receives the positive message returned from the server and displays it to the user. The input is the positive message received from the server, and the output is the message to be displayed. Specifically, the message is displayed on the smartphone screen.

[1166] 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.

[1167] The present invention relates to a system that receives input about a user's negative emotions, analyzes the input, generates positive perspectives and encouraging messages, and combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[1168] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses can be a smartphone, PC, tablet, or other device.

[1169] The device then receives the user's input and sends the data to a server, which uses natural language processing technology to analyze the received data. This technology determines the specific negative content of the user's input and generates a positive message accordingly.

[1170] A distinctive feature of the present invention is the use of an emotion engine. This emotion engine analyzes the user's emotional state from their input and generates more appropriate positive perspectives and encouraging messages based on that emotional state. The emotion engine reads emotional changes and tone from the user's input data and generates the most appropriate message accordingly.

[1171] Furthermore, the emotion engine can refer to the user's past input data to understand the user's emotional tendencies, providing more personalized feedback. This feature not only helps convert negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[1172] The server generates positive and encouraging messages based on the analysis results. For example, "Failure is an opportunity to grow. You can do better next time." The generated messages are personalized based on the individual characteristics and circumstances of each user, providing optimal feedback for the user.

[1173] The server then sends the generated message back to the device, which then displays it to the user, allowing the user to reframe negative emotions and situations in a positive way, reducing mental stress.

[1174] As a specific example, the following flow can be considered.

[1175] 1. User tweet: The user types into the device, "I made a big mistake at work."

[1176] 2. Receiving input data: The terminal receives this input data and sends it to the server.

[1177] 3. Data analysis: The data received by the server is analyzed using natural language processing technology.

[1178] 4. Use of emotion engine: The emotion engine analyzes the user's input and understands their emotional state.

[1179] 5. Message generation: The server generates a positive perspective or personalized message based on the results of the emotion engine.

[1180] 6. Returning the message: The server returns the generated message to the terminal.

[1181] 7. Display to user: The terminal displays the received message to the user.

[1182] In this way, the present invention provides a system that converts a user's negative emotions into positive ones, thereby improving mental stability and a sense of happiness.

[1183] The processing flow will be explained below.

[1184] Step 1:

[1185] The user inputs negative emotions into the device. For example, the user might input, "I made a big mistake at work."

[1186] Step 2:

[1187] The terminal receives the user's input and stores it in the user_input variable, while verifying that the input was received correctly.

[1188] Step 3:

[1189] The terminal sends the user's input to the server, specifically, by sending the contents of the user_input variable to the server via the network.

[1190] Step 4:

[1191] The server receives user input sent from the device. It converts the received data into an appropriate prompt format for processing. For example, it can be formatted as "User's tweet: I made a big mistake at work. Positive AI teacher's response:"

[1192] Step 5:

[1193] The server uses an emotion engine to analyze the received user input, which analyzes the user's emotional state from the input and is used to generate positive messages based on the emotional state.

[1194] Step 6:

[1195] The server uses natural language processing technology to generate positive prompt-based messages, which then incorporate the results of the emotion engine to generate more personalized messages.

[1196] Step 7:

[1197] The server receives the response from the OpenAI API and extracts the generated positive message from it, for example, "Failure is an opportunity to grow. You will do better next time."

[1198] Step 8:

[1199] The server returns the extracted positive message to the terminal, which involves sending the message back over the network to the terminal.

[1200] Step 9:

[1201] The terminal receives the positive message sent from the server and arranges the received message appropriately on the display screen for display to the user.

[1202] Step 10:

[1203] The user checks the positive message displayed on the device, which allows the user to receive positive feedback in response to their negative emotions.

[1204] Through this series of steps, the present invention provides a system that converts a user's negative emotions into positive ones and reduces mental burden. Furthermore, the emotion engine provides more appropriate feedback based on the user's emotional state, which is also effective for long-term emotion management.

[1205] Example 2

[1206] 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."

[1207] Conventional systems have had problems in responding adequately to users' negative emotions, making it difficult to contribute to improving users' mental stability and happiness. In particular, it was difficult to accurately grasp emotional changes and tone and provide appropriate messages accordingly, resulting in a lack of personalized feedback. Furthermore, analysis using natural language processing alone was unable to deeply understand the user's emotional state, making it impossible to generate effective positive messages for the user.

[1208] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1209] In this invention, the server includes means for receiving input regarding negative emotions from a user, means for analyzing the received input using natural language processing technology, means for analyzing the user's emotional state using an emotion engine, means for generating a positive perspective or an encouraging message using a generative AI model based on the analysis results, means for returning the generated message to the user, and means for understanding the user's emotional tendencies by referring to the user's past input data, thereby enabling a deep understanding of the user's emotions and providing personalized feedback.

[1210] "User" refers to the entity that uses this system to input negative emotions.

[1211] "Input" refers to text data that a user provides to a system through a terminal.

[1212] "Emotional input" refers to text data that allows a user to express their emotional state or feelings.

[1213] "Terminal" refers to an electronic device used by a user to provide input and communicate with the system, including, for example, a smartphone, a personal computer, or a tablet.

[1214] "Means for receiving" refers to the function of the terminal to receive user input and send it to the server.

[1215] "Means for transmitting" refers to a function for transmitting input data received by the terminal to the server.

[1216] "Server" refers to a computer system for analyzing received data, generating positive messages, and returning results to the terminal.

[1217] "Means for analyzing" refers to a function for analyzing received input data in a specific way (e.g., natural language processing technology).

[1218] "Natural language processing technology" refers to methods and technologies for analyzing text data and understanding its meaning. Specifically, it includes keyword extraction, sentiment analysis, etc.

[1219] An "emotion engine" refers to algorithms and technologies for analyzing a user's emotional state from input text data.

[1220] "Generative AI models" refer to artificial intelligence algorithms that generate text based on input data, such as OpenAI GPT-3.

[1221] "Means for generation" refers to the function for generating positive perspectives and encouraging messages based on the analysis results.

[1222] "Means for returning" refers to a function for returning a generated message from the server to the terminal.

[1223] The "means for displaying" refers to a function for displaying a message received by the terminal to the user.

[1224] "Past input data" refers to input data that a user has previously provided to the system.

[1225] "Personalized feedback" refers to appropriate response messages provided based on a user's individual characteristics and emotional tendencies.

[1226] The present invention relates to a system that receives input about a user's negative emotions, analyzes it, generates positive perspectives and encouraging messages, and further combines it with an emotion engine that recognizes the user's emotions.

[1227] First, a user uses a device to input a tweet about a negative emotion. For example, they might input, "I made a big mistake at work." The device the user uses may be a smartphone, PC, tablet, etc. The device receives this input and sends the data to a server via the Internet.

[1228] The server uses natural language processing (NLP) techniques to analyze the received data. Specifically, it uses the Python libraries NLTK and spaCy to analyze the specific negative content of the user's input. For example, from the input "I made a big mistake at work," it extracts "work" and "mistake" as important keywords and identifies the negative content.

[1229] The server then uses an emotion engine (e.g., Microsoft Azure's Text Analytics API) to analyze the user's emotional state. This engine reads the emotional inflection and tone of the user's input and generates an optimal positive message based on that emotional state.

[1230] Based on the analysis results of the emotion engine, the server uses a generative AI model (e.g., OpenAI GPT-3) to generate positive and encouraging messages. Examples of prompts for the generative AI model include the following:

[1231] User:

[1232] "I made a big mistake at work. What should I do?"

[1233] System (generative AI model):

[1234] "Failure is an opportunity to grow. You can do better next time. You're a hard worker, so use that experience to improve yourself even more."

[1235] The server then sends the generated message back to the device, which then displays it on its user interface. This allows the user to reframe negative emotions and situations in a positive way, reducing their mental burden.

[1236] Furthermore, the system can refer to the user's past input data to understand their emotional tendencies, providing more personalized feedback. This feature not only helps to turn negative emotions into positive ones, but also contributes to the user's long-term emotional management.

[1237] In this way, the system achieves a series of steps from analyzing the user's emotions to generating personalized positive messages, contributing to improving the user's mental stability and sense of happiness.

[1238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1239] Step 1: Getting User Input

[1240] The user uses the device to input a tweet about a negative emotion. For example, the user might type, "I made a big mistake at work," into the device's input field. This input is saved as text data on the device.

[1241] Step 2: Submitting input data

[1242] The device converts the received text data into JSON format and sends it to the server using an HTTP POST request. The input data (tweets) is sent to the server via the Internet, and the server receives the data.

[1243] Step 3: Data analysis with natural language processing (NLP)

[1244] The server analyzes the received JSON data. First, it uses Python libraries such as NLTK and spaCy to extract important keywords and sentiment from users' tweets. For example, negative sentiment can be identified from keywords such as "work" and "big mistake." This process makes it possible to determine the type of negative content contained in the input data.

[1245] Step 4: Emotional state analysis by the emotion engine

[1246] The server then calls an emotion engine based on the extracted keywords and context information. Here, the engine uses, for example, Microsoft Azure's Text Analytics API to perform a more detailed analysis of the user's emotional state. The engine identifies the intensity and type of emotion from the user's tweets and returns that information. Based on the input data and the analysis results, the emotional state (e.g., "sadness intensity 50%) is obtained.

[1247] Step 5: Generate a positive message

[1248] Based on the analysis results of the emotion engine, the server inputs a prompt into a generative AI model (e.g., OpenAI GPT-3) to generate a positive message. For example, the prompt could read, "The user said, 'I made a big mistake at work.' Please provide this user with an encouraging message," and the generative AI model would generate a message saying, "Failure is an opportunity for growth. You'll do better next time." This process generates an encouraging message that is appropriate for the user's emotional state.

[1249] Step 6: Returning the message

[1250] The server converts the generated positive message into JSON format and returns it to the terminal as an HTTP response, where the generated message is output and sent to the terminal.

[1251] Step 7: Display a message to the user

[1252] The device extracts positive messages from the received JSON data and displays them to the user. For example, a pop-up window on the device might say, "Failure is an opportunity for growth. You can do better next time." This process allows the user to receive encouraging messages and turn negative emotions into positive ones.

[1253] (Application example 2)

[1254] 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."

[1255] The goal is to improve the purchasing experience by reducing the negative emotions and stress felt by users when shopping in virtual stores. Also, a system is needed that can detect the user's emotional state in real time and provide appropriate feedback.

[1256] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input related to negative emotions from the user, means for analyzing the received input and generating a positive perspective or an encouraging message, means for returning the generated message to the user, and means for displaying the message to the user using smart glasses. This makes it possible to convert the user's negative emotions into positive ones in real time and improve the shopping experience in the virtual store.

[1257] (definition statement)

[1258] The "means for receiving input relating to negative emotions from the user" is a function that allows the user to input information relating to negative emotions via the terminal and receive the information.

[1259] "Means for analyzing received input and generating positive perspectives and encouraging messages" is a function for processing received data on the user's negative emotions and generating positive replies and encouraging messages.

[1260] The "means for returning the generated message to the user" is a function for sending the generated positive message to the user's terminal.

[1261] The "means for displaying a message to a user using smart glasses" is a function for displaying the generated positive message on the display of the smart glasses.

[1262] "Natural language processing technology" refers to algorithms and methods for analyzing and generating natural language data, and is a technology used to understand and process user input text.

[1263] "Personalized feedback" is a method for providing optimized, individual feedback based on the characteristics and past data of each individual user.

[1264] MODE FOR CARRYING OUT THE INVENTION

[1265] The present invention is a system that converts negative emotions felt by users while shopping in a virtual store into positive ones. In this system, users input their emotions through smart glasses, and the data is analyzed by a server, which then generates and returns an appropriate message.

[1266] System construction

[1267] Smart Glasses:

[1268] Users wear smart glasses to shop in a virtual store, and the glasses allow users to input voice and text, and provide real-time feedback.

[1269] server:

[1270] The server has the following main roles:

[1271] 1. Receiving emotion input: Receive data about negative emotions input by the user through the smart glasses.

[1272] 2. Data analysis: The received data is analyzed on the server using natural language processing technology, specifically Google Cloud Natural Language API and IBM Watson Emotion Analysis.

[1273] 3. Positive Message Generation: The emotion engine analyzes the user's input and generates appropriate positive messages, taking into account the user's past data to provide personalized feedback.

[1274] Program processing

[1275] First, the user inputs text such as "I'm having trouble finding the item in stock" into the smart glasses. This input is sent to the server via the communication module built into the smart glasses.

[1276] Server-side steps:

[1277] 1. The server receives the input data.

[1278] 2. Analyze the received data using natural language processing technology to identify negative emotional content.

[1279] 3. Based on the results of this analysis, the emotion engine generates positive perspectives and encouraging messages.

[1280] 4. The generated message might be something like, "You may find some stock online. If not, look forward to our new models!"

[1281] Steps for smart glasses:

[1282] 1. Receive a positive message back from the server.

[1283] 2. Display the received message on the smart glasses display.

[1284] Specific examples

[1285] If a user searches for a discontinued product in stock in your virtual store but can't find it:

[1286] Input statement: The user types "I'm having trouble finding the item in stock" into the smart glasses.

[1287] Output message: The smart glasses display will display the message "Stock may also be found online. If not, look forward to our new model!"

[1288] Prompt Sentence Examples

[1289] User input: "I'm having trouble finding it in stock."

[1290] Sentiment analysis result: "user_feelings: frustration"

[1291] Appropriate positive message: "You may find stock online. If you still can't find it, look forward to our new models!"

[1292] This makes shopping in a virtual store a more positive and stress-free experience, increasing users' motivation to buy.

[1293] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1294] System program processing flow

[1295] Step 1:

[1296] The user uses the smart glasses to input a message related to a negative emotion. For example, the user may input, "I'm having trouble finding the item in stock." The smart glasses receive this input and store it in their internal memory.

[1297] Step 2:

[1298] The device (smart glasses) sends the received input data about negative emotions to the server, where the input data is packaged in JSON format and sent as an HTTP request.

[1299] Step 3:

[1300] The server receives this HTTP request and parses the JSON data. It extracts the user's input message from the received data and inputs it into a natural language processing model. The natural language processing model (e.g., Google Cloud Natural Language API) analyzes the input text for negative sentiment and identifies the emotional state.

[1301] Step 4:

[1302] The server uses an emotion engine to generate positive and encouraging messages based on the analysis results. The generative AI model creates appropriate messages by taking into account the user's context and emotional state. For example, it generates a message saying, "You may be able to find the item online. If not, look forward to our new model!"

[1303] Step 5:

[1304] The server then packages the generated message in JSON format again and sends it back to the device (smart glasses). The server then sends data containing a positive message as an HTTP response.

[1305] Step 6:

[1306] The device (smart glasses) receives this HTTP response and extracts a positive message from the JSON data. It displays the extracted message on the display and notifies the user. At this time, the display shows the following message: "Stock may also be found online. If not, look forward to our new model!"

[1307] summary

[1308] Step 1: The user inputs negative emotions into the smart glasses.

[1309] Step 2: The terminal sends the input data to the server.

[1310] Step 3: The server receives the data and analyzes it using a natural language processing model.

[1311] Step 4: The server generates a positive message using the emotion engine.

[1312] Step 5: The server sends the generated message back to the terminal.

[1313] Step 6: The device receives the message and displays it on the smart glasses display.

[1314] 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.

[1315] 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.

[1316] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1317] 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.

[1318] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1319] 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.

[1320] 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).

[1321] 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.

[1322] 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."

[1323] 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.

[1324] 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).

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] The following is further disclosed regarding the above embodiment.

[1336] (Claim 1)

[1337] means for receiving input from a user regarding a negative emotion;

[1338] A means of analyzing the input received and generating positive perspectives and encouraging messages;

[1339] means for returning the generated message to the user;

[1340] A system including:

[1341] (Claim 2)

[1342] 10. The system of claim 1, wherein natural language processing techniques are used to analyze the user's input.

[1343] (Claim 3)

[1344] 10. The system of claim 1, further comprising means for providing personalized feedback to the user.

[1345] "Example 1"

[1346] (Claim 1)

[1347] means for receiving text from a user regarding negative sentiment;

[1348] means for transmitting the received text data to a server;

[1349] A means for analyzing the data received by the server based on natural language processing technology;

[1350] A means of generating positive perspectives and encouraging messages based on the analysis results;

[1351] means for returning the generated message from the server to the terminal;

[1352] means for displaying the returned message to the user;

[1353] A system including:

[1354] (Claim 2)

[1355] 10. The system of claim 1, wherein the system generates positive messages using a generative artificial intelligence model.

[1356] (Claim 3)

[1357] 10. The system of claim 1, further comprising means for providing personalized feedback to the user.

[1358] "Application Example 1"

[1359] (Claim 1)

[1360] means for receiving input from a user regarding a negative emotion;

[1361] A means of analyzing the input received and generating positive perspectives and encouraging messages;

[1362] means for returning the generated message to the user;

[1363] means for displaying the generated message to a user;

[1364] A system including:

[1365] (Claim 2)

[1366] 10. The system of claim 1, wherein natural language processing techniques are used to analyze the user's input.

[1367] (Claim 3)

[1368] 10. The system of claim 1, further comprising means for providing personalized feedback to the user.

[1369] (Claim 4)

[1370] 10. The system of claim 1, wherein the system analyzes the received data using natural language processing techniques and generates positive messages using a generative AI model.

[1371] (Claim 5)

[1372] 10. The system of claim 1, further comprising means for inputting prompt sentences into the generative AI model to generate positive perspectives and encouraging messages.

[1373] (Claim 6)

[1374] The system of claim 1, which receives input based on negative emotions experienced by a user in a physical store and generates a positive message.

[1375] "Example 2: Combining Emotion Engines"

[1376] (Claim 1)

[1377] means for receiving input from a user regarding a negative emotion;

[1378] means for analyzing the received input using natural language processing techniques;

[1379] means for analyzing a user's emotional state using an emotion engine;

[1380] A means to generate positive perspectives and encouraging messages using a generative AI model based on the analysis results; and

[1381] means for returning the generated message to the user;

[1382] A system including:

[1383] (Claim 2)

[1384] 2. The system according to claim 1, further comprising means for grasping the user's emotional tendency by referring to the user's past input data.

[1385] (Claim 3)

[1386] 10. The system of claim 1, further comprising means for providing personalized feedback to the user.

[1387] "Application example 2 when combining emotion engines"

[1388] (Claim 1)

[1389] means for receiving input from a user regarding a negative emotion;

[1390] A means of analyzing the input received and generating positive perspectives and encouraging messages;

[1391] means for returning the generated message to the user;

[1392] means for displaying a message to a user utilizing the smart glasses;

[1393] A system including:

[1394] (Claim 2)

[1395] 10. The system of claim 1, wherein natural language processing techniques are used to analyze the user's input.

[1396] (Claim 3)

[1397] 10. The system of claim 1, further comprising means for providing personalized feedback to the user. [Explanation of symbols]

[1398] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving input from a user regarding a negative emotion; A means of analyzing the input received and generating positive perspectives and encouraging messages; means for returning the generated message to the user; A system including:

2. 10. The system of claim 1, wherein natural language processing techniques are used to analyze the user's input.

3. The system of claim 1 further comprising means for providing personalized feedback to the user.

Images