System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving text data or voice data, a means for performing emotion analysis, character analysis, and impression determination on the received data, a means for selecting an impression desired by a user, a means for generating an optimum reply comment on the basis of the selected impression, and a means for presenting the generated reply comment 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 everyday communication, there are problems such as not knowing what the other person is thinking or not knowing how to respond. These problems are particularly noticeable in communication tools that use text or voice, and they hinder smooth communication. The present invention aims to facilitate communication by analyzing the other person's emotions and impressions and, based on that, providing advice on appropriate reply comments. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving text data or voice data, a means for performing emotion analysis, personality analysis, and impression determination on the received data, a means for allowing the user to select the desired impression, a means for generating an optimal reply comment based on the selected impression, and a means for presenting the generated reply comment to the user. The system further includes a means for converting voice data into text data and a means for sending the generated reply comment as a message. This allows the user to more accurately understand the other person's emotions and impressions and to communicate appropriately based on that understanding.

[0006] "Text data" refers to data that has been digitized as character information and is in a format that can be processed by computers or devices.

[0007] "Audio data" refers to audio information in a digitized form that can be processed by a computer or device.

[0008] "Sentiment analysis" refers to the process of analyzing human emotions from text or audio data and determining the type and intensity of those emotions.

[0009] "Personality analysis" refers to the process of analyzing an individual's personality traits and temperament from text or audio data and determining their characteristics.

[0010] "Impression assessment" refers to the process of analyzing text or audio data to determine the impression or attitude that the sender of that data has toward the receiver.

[0011] "User" refers to a person who communicates using this system.

[0012] "Generative AI" refers to artificial intelligence that generates appropriate reply comments based on the impressions selected by the user.

[0013] "Optimal reply comment" refers to the most appropriate reply content generated by AI to achieve the impression or effect desired by the user.

[0014] "Presenting" refers to displaying the generated reply comment to the user.

[0015] "Message" refers to information consisting of text or voice that a user sends or receives using a communication tool. [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 is a system that analyzes the emotions and impressions of the other party in daily communication and advises the user on appropriate reply comments based on the analysis. This system mainly involves interactions between a server, a terminal, and a user.

[0038] 1. Data Entry

[0039] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology and then sent to the server.

[0040] 2. Data Analysis

[0041] The server analyzes the received text data or converted text data using AI modules for emotion analysis, personality analysis, and impression determination.

[0042] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[0043] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[0044] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0045] 3. Proposal Generation

[0046] The device that receives the analysis results notifies the user, and the user selects on the device "what kind of impression they want to give."

[0047] Once the user selects the impression they want, they send that information to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression.

[0048] 4. Presenting the proposal

[0049] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[0050] The user selects the suggested reply comment, confirms it, and then sends it as a message.

[0051] Specific examples

[0052] One day, User A receives a message in the text messaging application they are using that asks, "How was your day?"

[0053] 1. Data Entry

[0054] The terminal sends this message to the server.

[0055] 2. Data Analysis

[0056] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0057] - Sentiment analysis AI: Determines that the tone is neutral.

[0058] - Personality analysis AI: Predicts whether the caller is interested.

[0059] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[0060] 3. Proposal Generation

[0061] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[0062] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[0063] 4. Presenting the proposal

[0064] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[0065] The present invention enables a user to accurately understand the emotions and impressions of others, enabling appropriate communication.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The terminal receives a message from a user, the message being text data or voice data.

[0069] Step 2:

[0070] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[0071] Step 3:

[0072] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[0073] Step 4:

[0074] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[0075] Step 5:

[0076] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[0077] Step 6:

[0078] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[0079] Step 7:

[0080] The user uses the terminal to select "what kind of impression they want to make," such as "I want to convey my gratitude."

[0081] Step 8:

[0082] The terminal transmits the user's selection to the server, where the selection information is recorded.

[0083] Step 9:

[0084] The server will then activate the AI ​​based on the selected impression to generate the best reply comment, for example, "Today was a good day, thank you for your concern!"

[0085] Step 10:

[0086] The server sends the generated reply comment to the terminal, which displays it to the user.

[0087] Step 11:

[0088] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[0089] Step 12:

[0090] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[0091] The above are the specific processing steps of the program of the present invention.

[0092] Example 1

[0093] 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."

[0094] Conventional communication tools have the problem that it is difficult to accurately understand the other person's feelings and impressions and generate appropriate replies. As a result, users often send misleading messages, making smooth communication difficult.

[0095] 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.

[0096] In this invention, the server includes: [means for analyzing data using emotion analysis AI, personality analysis AI, and impression determination AI on the server; [means for generating an optimal reply comment based on the impression selected using a generation AI model; and [means for presenting the generated reply comment to the user.] This makes it possible [to accurately analyze the other person's emotions and impressions, automatically generate an optimal reply comment based on that information, and present it to the user].

[0097] A "user" refers to an entity that uses a terminal to exchange messages with others.

[0098] "Text data" refers to digital data that consists of character information.

[0099] "Audio data" refers to digital data that records audio information.

[0100] "Terminal" refers to a computing device operated by a user (e.g., smartphone, tablet, PC).

[0101] "Server" refers to a computer system that analyzes data received from a user's terminal and generates a reply comment.

[0102] "Voice recognition technology" refers to technology that converts voice data into text data.

[0103] "Sentiment analysis AI" refers to artificial intelligence that determines the emotional tone (positive, negative, neutral, etc.) from text data.

[0104] "Personality analysis AI" refers to artificial intelligence that estimates the sender's personality traits from the content of text data.

[0105] "Impression judgment AI" refers to artificial intelligence that determines the impression a sender has of a receiver based on the results of emotional and personality analysis.

[0106] "Generative AI model" refers to artificial intelligence that generates optimal reply comments based on selected impressions.

[0107] A "reply comment" refers to a reply message that a user sends to another person.

[0108] The present invention is a system that analyzes the emotions and impressions of the other person in everyday communication and generates appropriate reply comments based on the analysis. This system functions in cooperation with a server, terminals, and users.

[0109] A user uses a device (such as a smartphone, tablet, or PC) to exchange text or voice messages with others. When the device receives a message, it sends the data to a server. In the case of voice data, the device's voice recognition technology (specifically, voice recognition software) is used to convert the voice data into text data. One example of the technology used in this case is a voice recognition API.

[0110] The server analyzes the received text data using various AI modules. Sentiment analysis AI, for example, uses natural language processing APIs to determine the emotional tone (e.g., positive, negative, neutral, etc.). Personality analysis AI estimates the sender's personality traits from the text content. Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trustworthiness, etc.) based on the results of these analyses.

[0111] The analysis results are sent from the server to the device, which then notifies the user. The user checks the displayed analysis results and selects the desired impression (e.g., they want to express gratitude). The selected impression information is sent back to the server, which then activates a generative AI model to generate an optimal reply comment. This generative AI model can utilize a generative language model API, for example.

[0112] The generated reply comment is sent from the server to the terminal, and the terminal displays it to the user. The user checks the displayed reply comment and, if appropriate, sends it as a message.

[0113] As a specific example, let us consider the case where user A receives the message "How was your day?".

[0114] 1. Data Entry

[0115] The terminal sends this message to the server.

[0116] 2. Data Analysis

[0117] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0118] Sentiment analysis AI: Determines that the tone is neutral.

[0119] Personality analysis AI: Estimates whether the caller is interested.

[0120] Impression determination AI: Determines that the sender is concerned about User A's situation.

[0121] 3. Proposal Generation

[0122] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[0123] The server launches the generative AI model and generates the optimal reply comment, such as "Today was a good day, thank you for your concern!". An example of a prompt sentence for the generative AI model is, "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'"

[0124] 4. Presenting the proposal

[0125] The server sends the generated reply comment to the terminal, which displays it to User A, who then confirms and sends it.

[0126] This system enables users to accurately understand the emotions and impressions of others and to communicate effectively and appropriately.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] A user receives a message through a text messaging application on a device, which provides input data as text or voice data. The device then detects the message and identifies its type (text or voice). If it is voice data, it uses voice recognition software to convert the voice into text data. The converted text data is provided as output.

[0130] Step 2:

[0131] The terminal transmits the obtained text data to the server. The input data is the text data received or converted by the terminal. The terminal transmits this data to the server using the communication module. The server receives the received data as input.

[0132] Step 3:

[0133] The text data received by the server is analyzed using an emotion analysis AI, personality analysis AI, and impression determination AI. The input data is the text data received by the server. The server activates the emotion analysis AI, analyzes the emotional tone of the text data (positive, negative, neutral, etc.), and obtains the results. Next, the personality analysis AI is used to analyze the sender's personality traits (e.g., extrovert, introvert, etc.). Finally, the impression determination AI analyzes the sender's impression (e.g., friendly, trustworthy, etc.) based on the results of the emotion analysis and personality analysis. The analysis results are obtained as output.

[0134] Step 4:

[0135] The server sends the analysis results to the terminal, and the terminal notifies the user of the results. The input data are the analysis results obtained by the server. The server sends these results to the terminal using a communication module. The terminal receives the received results as input and outputs them in a form that notifies the user. The terminal displays the analysis results via a user interface.

[0136] Step 5:

[0137] The user checks the analysis results on the device and selects the desired impression (e.g., expressing gratitude). The input data is the analysis results displayed on the device. The user decides on an option based on the analysis results and enters that selection information into the device. The selected impression is obtained as the output.

[0138] Step 6:

[0139] The terminal transmits the user's selection information to the server. The input data is the impression information selected by the user. The terminal transmits this information to the server via the communication module. The server receives the selection information as input.

[0140] Step 7:

[0141] The server uses a generative AI model to generate the optimal reply comment based on the selected impression information. The input data is the impression information selected by the user. The server starts the generative AI model and provides a prompt sentence as input data. Example prompt: "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'" The generative AI model outputs the optimal reply comment.

[0142] Step 8:

[0143] The server sends the generated reply comment to the terminal, which then displays it to the user. The input data is the reply comment generated by the generative AI model. The server sends this comment to the terminal via a communication module. The terminal receives the received reply comment as input and outputs it in a form that is displayed to the user. The user checks the displayed reply comment and sends it if it is appropriate.

[0144] (Application example 1)

[0145] 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."

[0146] In modern virtual stores, communication with customers is an important element, and it is necessary to quickly understand the customer's emotions and intentions and respond appropriately based on them. However, typical responses often rely on subjective judgment, which limits the ability to improve customer satisfaction. The purpose of this invention is to solve this problem and achieve smooth communication with customers by accurately analyzing customer emotions and generating appropriate responses.

[0147] 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.

[0148] In this invention, the server includes a means for receiving text data or voice data, a means for analyzing emotions, personality, and impressions of the received data, and a means for selecting the impression desired by the user, thereby enabling fast and accurate emotional analysis of customer comments and the generation of optimal reply comments.

[0149] "Audio data" is a data format for recording or transmitting audio.

[0150] "Text data" is a data format that includes characters and symbols.

[0151] "Sentiment analysis" is the process of determining sentiment from received data.

[0152] "Personality analysis" is the process of inferring the sender's personality traits from the content of the message.

[0153] "Impression determination" is a process of determining the impression that the sender has of the receiver based on the analysis results.

[0154] The "means for selecting the impression desired by the user" is a function that allows the user to select the impression that he or she wishes to give.

[0155] The "means for generating a reply comment" is a function for generating a reply comment based on the selected impression.

[0156] The "means for presenting the generated reply comment to the user" is a function for displaying the generated reply comment to the user.

[0157] "Speech recognition technology" is a technology that converts voice data into text data.

[0158] A "prompt" is a text instruction that is input to a generative AI model.

[0159] A "generative AI model" is an artificial intelligence model that generates natural language text based on a given prompt.

[0160] A "virtual store" is a store that exists in a virtual space on the Internet.

[0161] The present invention is a system for facilitating communication with customers in a virtual store. The system receives text data and voice data, analyzes them, and generates an optimal reply.

[0162] First, the user interacts with the customer using the device. When the customer speaks a question or request, the microphone in the device picks up the voice data. For example, a microphone built into smart glasses or a head-mounted display can serve this purpose. The voice data is then sent to the server in real time.

[0163] The voice data sent to the server is converted into text data using speech recognition technology. The speech_recognition library performs this process. After being converted into text data, it is analyzed by AI modules for sentiment analysis, personality analysis, and impression determination. The sentiment analysis module from the transformers library is used here.

[0164] Once the analysis is complete, the server provides the user with the option to select the impression they wish to give based on the results. This involves providing an interface where the user can select a particular impression (e.g., "trust").

[0165] Based on the selected impression, the server uses a generative AI model (e.g., GPT-3 (registered trademark)) to generate a reply comment. In this case, the generative AI model uses the following prompt sentence:

[0166] What customers say: What's your recommendation today?

[0167] Emotion: Interested

[0168] The impression you want to convey to your customers: Trust

[0169] Generate an appropriate reply.

[0170] The generated reply comment is sent to the terminal and displayed to the user, who then confirms it and sends it to the customer as a message.

[0171] This allows for a quick and appropriate response based on customer comments in the virtual store, improving customer satisfaction.

[0172] As a specific example, if a customer says, "What's today's recommendation?", the system converts the speech into text, determines the emotion as "interested," and if the user selects to give "trust," generates a reply saying, "This month's recommendation is a special pasta made with fresh sea urchin and caviar. We use ingredients carefully selected at the restaurant's discretion, so please give it a try." In this way, by implementing the invention, it becomes possible to communicate with customers smoothly and efficiently.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] The terminal captures what the customer says. If the customer utters voice data, the microphone in the terminal records the voice data. The captured voice data is sent to the server for analysis.

[0176] Input: Customer voice data

[0177] Output: The audio data is sent to the server.

[0178] Step 2:

[0179] The server receives the voice data and converts it into text data using speech recognition technology. It uses the speech_recognition library to analyze the voice data and generate text data.

[0180] Input: Audio data

[0181] Output: Converted text data

[0182] Step 3:

[0183] The server performs sentiment analysis, personality analysis, and impression determination on the text data. For sentiment analysis, the transformers library is used to classify the text data into emotional tones (e.g., positive, negative, neutral, etc.).

[0184] Input: Text data

[0185] Output: Emotion analysis results, personality analysis results, impression judgment results

[0186] Step 4:

[0187] The server notifies the terminal of the analysis results and displays an interface for the user to select "what impression they want to give." The user selects the impression they want from the presented options.

[0188] Input: Analysis results

[0189] Output: User-selected impressions

[0190] Step 5:

[0191] The server generates a reply comment based on the impression selected by the user, sends the prompt to a generative AI model (e.g., GPT-3), and receives an appropriate reply.

[0192] Input: User-selected impression, prompt statement

[0193] Output: Generated reply comment

[0194] Step 6:

[0195] The server transmits the generated reply comment to the terminal, which displays the generated reply comment to the user, who then confirms it.

[0196] Input: Generated reply comment

[0197] Output: Reply comment presented to the user

[0198] Step 7:

[0199] The user can review the suggested reply comment, adjust it if necessary, and then send it as a final message.

[0200] Input: User confirmation results, adjustment details

[0201] Output: Final message sent

[0202] The above are the specific processing steps for carrying out the present invention, which enable smooth communication with customers in a virtual store.

[0203] 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.

[0204] This invention is a system that analyzes the emotions and impressions of the other party in communication and advises on appropriate reply comments based on the results, and also includes an emotion engine that recognizes the user's emotions. This system mainly involves interactions between a server, a terminal, and a user.

[0205] 1. Data Entry

[0206] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[0207] 2. Data Analysis

[0208] The server analyzes the received text data or converted text data using emotion analysis AI, personality analysis AI, and impression determination AI modules.

[0209] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[0210] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[0211] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0212] 3. User Emotion Recognition

[0213] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. The emotion engine detects the user's facial expressions, gestures, typing speed, etc. when viewing a message to analyze the user's emotional state at that time.

[0214] 4. Proposal Generation

[0215] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen. When the user selects the impression they want to give, the system also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, appropriate suggestions will be made.

[0216] 5. Generating optimal reply comments

[0217] Once the user selects the desired impression, the information is sent to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression, such as "Today was a good day, thank you for caring!"

[0218] 6. Presenting the proposal

[0219] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[0220] The user selects a reply comment from the list, confirms it, and then sends it as a message.

[0221] Specific examples

[0222] For example, user A receives a message in a text messaging application that asks, "How was your day?"

[0223] 1. Data Entry

[0224] The terminal sends this message to the server.

[0225] 2. Data Analysis

[0226] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0227] - Sentiment analysis AI: Determines that the tone is neutral.

[0228] - Personality analysis AI: Predicts whether the caller is interested.

[0229] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[0230] 3. User Emotion Recognition

[0231] The emotion engine analyzes user A's facial expressions and behavioral data obtained through the device's camera and sensors, and determines that user A is currently in a relaxed state.

[0232] 4. Proposal Generation

[0233] The device notifies the analysis result to User A, who selects the impression "I want to express my gratitude."

[0234] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[0235] 5. Presenting the proposal

[0236] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[0237] Through the above processing, the present invention enables appropriate communication that also takes into account the user's emotional state.

[0238] The processing flow will be explained below.

[0239] Step 1:

[0240] The terminal receives a message from a user, the message being text data or voice data.

[0241] Step 2:

[0242] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[0243] Step 3:

[0244] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[0245] Step 4:

[0246] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[0247] Step 5:

[0248] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[0249] Step 6:

[0250] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[0251] Step 7:

[0252] The emotion engine is activated and recognizes the user's current emotional state. It collects data such as the user's facial expressions and input speed through the device's camera and sensors, and analyzes the user's emotions based on that data.

[0253] Step 8:

[0254] The user uses the device to select "what impression they want to make," such as "I want to express my gratitude." This selection also takes into account the user's current emotional state.

[0255] Step 9:

[0256] The terminal transmits the user's selection to the server, where the selection information and the user's emotional state data are recorded.

[0257] Step 10:

[0258] The server activates the AI ​​based on the selected impression and the user's emotional state data to generate an optimal reply comment, such as "Today was a good day, thank you for your concern!"

[0259] Step 11:

[0260] The server sends the generated reply comment to the terminal, which displays it to the user.

[0261] Step 12:

[0262] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[0263] Step 13:

[0264] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[0265] The above are the specific processing steps of the program of the present invention.

[0266] Example 2

[0267] 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."

[0268] In communication, it is difficult to accurately analyze the other person's emotions and impressions and generate appropriate reply comments that take the user's emotional state into consideration. Furthermore, conventional systems cannot reflect the user's real-time emotional state, so they cannot provide advice that matches the user's emotions.

[0269] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to receive message data using a terminal and transmit the data to the server; means for the server to analyze the received data using modules for emotion analysis, personality analysis, and impression determination; means for the terminal to sense real-time data such as the user's facial expression and input speed and recognize the user's emotional state; means for the user to select a desired impression and transmit the information to the server; means for the server to activate a generation AI to generate an optimal reply comment based on the selected impression; and means for the terminal to present the generated reply comment to the user. This makes it possible to generate an appropriate reply comment that reflects the user's emotional state.

[0270] "User" refers to an entity that uses the system to receive and reply to messages.

[0271] "Device" refers to a device, including a camera and sensors, used by a user to send and receive messages and to sense the user's emotional state.

[0272] "Server" refers to a computer system that analyzes data sent from a terminal and performs processing to generate appropriate suggestions and reply comments.

[0273] "Message data" refers to text data or voice data that a user receives or sends.

[0274] "Sentiment analysis" refers to the process of determining the emotional tone (positive, negative, neutral, etc.) of message data.

[0275] "Personality analysis" refers to the process of inferring the sender's personality traits from the content of message data.

[0276] "Impression determination" refers to the process of determining the impression (e.g., friendliness, trust, respect, etc.) that the sender has of the receiver based on the analysis results.

[0277] An "emotion engine" refers to a system that analyzes real-time behavioral and biometric data obtained from a user's device and recognizes the user's emotions.

[0278] "Generative AI" refers to an artificial intelligence model that generates optimal reply comments based on the impression the user wants to make.

[0279] A "reply comment" refers to a message created by a user to be sent to another party.

[0280] The present invention is a system that allows interaction between a user, a terminal, and a server, analyzes the emotions and impressions of the other party in communication, and based on that analyzes the emotions and impressions, advises on appropriate reply comments. This system includes an emotion engine and can also analyze the user's emotional state.

[0281] First, a user uses a device to exchange messages with others using a communication tool (e.g., a text messaging application). The received message data (text data or voice data) is sent from the device to a server. In the case of voice data, it is converted into text data using voice recognition technology before being sent to the server. General voice recognition software is used for voice recognition.

[0282] To analyze the received text data, the server uses modules called emotion analysis AI, personality analysis AI, and impression determination AI. The emotion analysis AI determines the emotional tone of the message (positive, negative, neutral, etc.), and the personality analysis AI infers the sender's personality traits from the message content. Based on these analysis results, the impression determination AI determines the impression the sender has of the recipient (e.g., friendliness, trust, respect, etc.). This allows for the accumulation of detailed data on emotions, personality, and impressions.

[0283] The device is also equipped with an emotion engine that recognizes the user's emotional state through real-time behavioral data (e.g., facial expressions, gestures, typing speed, etc.). This is achieved using hardware such as cameras and sensors. The emotion engine analyzes this input data and determines the user's emotional state when viewing a message.

[0284] The analysis results are sent to the device. The user checks the analysis results on the screen and selects "what kind of impression they want to convey." For example, if the user selects the impression "I want to express my gratitude," the server will activate the generation AI and generate the optimal reply comment. The generation AI used here generates a reply comment based on the impression selected by the user.

[0285] The generated reply comment is sent from the server to the terminal and presented to the user. The user can then confirm the suggested reply comment and send it as a message. This enables appropriate communication that takes into account the user's emotional state.

[0286] Prompt Sentence Examples

[0287] "Generate the best reply comment if the user selects the impression 'I want to express my gratitude.' Example: 'Today was a good day, thanks for caring!'"

[0288] By implementing this system, it will be possible to generate and display reply comments that are appropriate for the user's emotional state, which was difficult with conventional systems. This system integrates multiple AI modules, such as emotion analysis AI, personality analysis AI, impression determination AI, and generation AI, to enhance communication that takes the user's emotional state into consideration.

[0289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0290] Step 1:

[0291] A user receives a message on their device. The user opens a text messaging application and views a message from another person. For example, they receive a message saying, "How was your day?" The input is the received message data, and the output is the device sending this data to the server. Specifically, the user checks the message on their smartphone, and the device sends the text data to the server.

[0292] Step 2:

[0293] The server analyzes the received message data. The server then analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The input is the received text data, and the output is the analysis results of each AI module. Specifically, the server analyzes the data, the emotion analysis AI determines the tone of the message, the personality analysis AI estimates the sender's personality traits, and the impression determination AI determines the impression.

[0294] Step 3:

[0295] The device recognizes the user's emotions. The device's emotion engine acquires data such as the user's facial expressions, gestures, and input speed through cameras and sensors. The input is the user's real-time behavioral data, and the output is the user's emotional state as recognized by the emotion engine. Specifically, the device's camera captures a picture of the user's face, and the sensor detects the user's input speed. The emotion engine analyzes this data and determines the user's emotional state, such as whether they are relaxed.

[0296] Step 4:

[0297] The server generates a suggestion based on the analysis results. The user selects the desired impression, such as "I want to express my gratitude," and this information is sent to the server. The server then activates the generation AI based on the selected impression and the user's emotional state, and generates the optimal reply comment. The input is the user's selected impression information and emotional state, as well as the analyzed data, and the output is the generated reply comment. In concrete terms, the server receives the information "I want to express my gratitude," and the generation AI generates the comment "Today was a good day, thank you for thinking about me!"

[0298] Step 5:

[0299] The terminal presents the generated reply comment to the user. The terminal receives the reply comment sent from the server and notifies the user. The input is the generated reply comment, and the output is the notification to the user. Specifically, the terminal displays "Suggested reply comment: Today was a good day, thanks for your concern!" to the user, and the user confirms it.

[0300] Step 6:

[0301] The user submits a reply comment. The user reviews the proposed reply comment, corrects it if necessary, and then submits it. The input is the content of the generated reply comment, and the output is the message sent to the other person. In concrete terms, the user confirms the comment "Today was a good day, thanks for thinking about me!" and submits the message.

[0302] Through the above processing steps, the system can generate optimal reply comments that reflect the user's emotional state.

[0303] (Application example 2)

[0304] 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."

[0305] Conventional systems that generate reply comments appropriate to each user's emotional state have the problem of being unable to consider the user's own emotional state or the appropriate content at that time. Furthermore, few systems have the functionality to analyze the user's emotional state in real time and recommend optimal content based on that information. As a result, there is a risk that the user experience will be impaired and satisfaction with communication and content viewing will decrease.

[0306] The identification process by the identification 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 text data or voice data, means for performing emotion analysis, personality analysis, and impression determination on the received data, means for selecting the impression desired by the user, means for generating an optimal reply comment based on the selected impression, means for presenting the generated reply comment to the user, means for analyzing the user's emotional state in real time, and means for recommending content based on the user's emotional state. This makes it possible to generate an optimal reply comment taking into account the user's real-time emotional state and to recommend content that suits the user's current emotion.

[0307] "Text data" is character string information expressed electronically.

[0308] "Audio data" is information that represents audio in digital form.

[0309] "Sentiment analysis" is a technique for determining emotional tone (positive, negative, neutral, etc.) from text or audio data.

[0310] "Personality analysis" is a technology that estimates a caller's personality traits (e.g., extroversion, introversion, high sensitivity, etc.) from text or voice data.

[0311] "Impression assessment" is a technology that determines the impression (e.g., friendliness, trust, respect, etc.) that a sender has of a receiver based on the results of emotional and personality analysis.

[0312] "User" means an individual or corporation that uses the system.

[0313] A "reply comment" is a comment to reply to a received message, and is suggested by the generation AI.

[0314] "Emotional state" refers to a user's current emotional state, as measured in real time.

[0315] "Content" refers to information or media that can be viewed or used (e.g., movies, dramas, anime, music, articles, etc.).

[0316] "Content recommendation" is the process of automatically suggesting content that is appropriate for a user's emotional state.

[0317] The present invention is a system that analyzes a user's emotional state in real time and recommends optimal reply comments and content based on that information. This system mainly involves interactions between a server, a terminal, and a user.

[0318] Users use devices such as smartphones to exchange messages with others through communication tools (e.g., text messaging applications). When a device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[0319] The server analyzes the received text data or converted text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the message, and the personality analysis AI infers the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the message. The impression determination AI determines the sender's impression of the recipient (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0320] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. This emotion engine detects the user's facial expressions, gestures, and typing speed when viewing a message, and analyzes the user's emotional state at that time. For example, the device's camera and sensor module can be used to acquire the user's facial expressions and behavioral data, and the analysis can be performed in real time.

[0321] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen and select the impression they want to convey. The server also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, the server will suggest a reply comment that is appropriate for that situation.

[0322] Once the user selects the desired impression, the information is sent to the server, which then activates the generative AI model to generate the optimal reply comment based on the selected impression. For example, a comment like "Today was a good day, thank you for your concern!" may be generated. The server then sends the generated reply comment to the device, which then displays it to the user. The user can then confirm the presented reply comment and send it as a message.

[0323] Furthermore, the engine recommends content that matches the user's current mood based on their emotional state. This content recommendation engine selects content according to the user's emotional state, such as recommending relaxing music or videos when the user is relaxing.

[0324] As a concrete example, if User A receives a message in a text messaging application using the message "What do you think of this anime?", and User A is in a relaxed state, the system will select the impression that the user wants to express gratitude and generate a reply such as "Thank you for watching this anime!". In addition, if User A is tired, content that will help them relax will be recommended.

[0325] An example prompt is:

[0326] "Perform sentiment and personality analysis on the comment 'What do you think of this anime?' and generate and display the optimal reply if the user chooses to express gratitude. At the same time, if the user is tired, recommend content that will help them relax."

[0327] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0328] Step 1: Data entry

[0329] A user receives a comment via a text messaging application. The device captures the comment as text data or voice data, and if the voice data is voice data, converts it to text data using voice recognition technology. The device then sends the converted text data to a server.

[0330] Input: Received text or voice data

[0331] Output: Text data sent to the server

[0332] Step 2: Data analysis

[0333] The server analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the text data, and the personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the text data. Finally, the impression determination AI determines the sender's impression of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0334] Input: Text data

[0335] Output: Emotional tone, personality traits, impression judgment results

[0336] Step 3: Recognizing user emotions

[0337] The device uses a camera and sensor module to capture the user's facial expressions and behavioral data in real time, and sends this data to an emotion engine to analyze the user's current emotional state (e.g., relaxed, tired, stressed, etc.).

[0338] Input: User's facial expression data and behavior data

[0339] Output: Current emotional state

[0340] Step 4: Generate proposals

[0341] The server sends the analysis results to the device, which then notifies the user. The user checks the analysis results on the screen and selects the impression they want to convey. The user's current emotional state estimated by the emotion engine is also taken into consideration. Based on the selected impression and emotional state, the server activates a generative AI model to generate the optimal reply comment.

[0342] Input: Analysis results, selected impression, current emotional state

[0343] Output: Generated reply comment

[0344] Step 5: Present your proposal

[0345] The server sends the generated reply comment to the terminal, which displays the content to the user. The user checks the presented reply comment, makes corrections if necessary, and then sends it as a message.

[0346] Input: Generated reply comment

[0347] Output: Reply comments displayed to the user, message sent

[0348] Step 6: Content Recommendation

[0349] Based on the user's emotional state, the server activates a content recommendation engine to select content that suits the user's current mood (e.g., relaxing music or videos). The selected content information is sent to the device and displayed to the user.

[0350] Input: The user's current emotional state

[0351] Output: Recommended content information

[0352] Through the above processing steps, the system of the present invention is able to generate optimal reply comments that take into account the user's real-time emotional state and recommend content that suits the user's current emotions.

[0353] 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.

[0354] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0355] 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.

[0356] [Second embodiment]

[0357] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0358] 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.

[0359] 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).

[0360] 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.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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."

[0369] The present invention is a system that analyzes the emotions and impressions of the other party in daily communication and advises the user on appropriate reply comments based on the analysis. This system mainly involves interactions between a server, a terminal, and a user.

[0370] 1. Data Entry

[0371] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology and then sent to the server.

[0372] 2. Data Analysis

[0373] The server analyzes the received text data or converted text data using AI modules for emotion analysis, personality analysis, and impression determination.

[0374] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[0375] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[0376] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0377] 3. Proposal Generation

[0378] The device that receives the analysis results notifies the user, and the user selects on the device "what kind of impression they want to give."

[0379] Once the user selects the impression they want, they send that information to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression.

[0380] 4. Presenting the proposal

[0381] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[0382] The user selects the suggested reply comment, confirms it, and then sends it as a message.

[0383] Specific examples

[0384] One day, User A receives a message in the text messaging application they are using that asks, "How was your day?"

[0385] 1. Data Entry

[0386] The terminal sends this message to the server.

[0387] 2. Data Analysis

[0388] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0389] - Sentiment analysis AI: Determines that the tone is neutral.

[0390] - Personality analysis AI: Predicts whether the caller is interested.

[0391] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[0392] 3. Proposal Generation

[0393] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[0394] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[0395] 4. Presenting the proposal

[0396] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[0397] The present invention enables a user to accurately understand the emotions and impressions of others, enabling appropriate communication.

[0398] The processing flow will be explained below.

[0399] Step 1:

[0400] The terminal receives a message from a user, the message being text data or voice data.

[0401] Step 2:

[0402] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[0403] Step 3:

[0404] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[0405] Step 4:

[0406] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[0407] Step 5:

[0408] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[0409] Step 6:

[0410] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[0411] Step 7:

[0412] The user uses the terminal to select "what kind of impression they want to make," such as "I want to convey my gratitude."

[0413] Step 8:

[0414] The terminal transmits the user's selection to the server, where the selection information is recorded.

[0415] Step 9:

[0416] The server will then activate the AI ​​based on the selected impression to generate the best reply comment, for example, "Today was a good day, thank you for your concern!"

[0417] Step 10:

[0418] The server sends the generated reply comment to the terminal, which displays it to the user.

[0419] Step 11:

[0420] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[0421] Step 12:

[0422] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[0423] The above are the specific processing steps of the program of the present invention.

[0424] Example 1

[0425] 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."

[0426] Conventional communication tools have the problem that it is difficult to accurately understand the other person's feelings and impressions and generate appropriate replies. As a result, users often send misleading messages, making smooth communication difficult.

[0427] 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.

[0428] In this invention, the server includes: [means for analyzing data using emotion analysis AI, personality analysis AI, and impression determination AI on the server; [means for generating an optimal reply comment based on the impression selected using a generation AI model; and [means for presenting the generated reply comment to the user.] This makes it possible [to accurately analyze the other person's emotions and impressions, automatically generate an optimal reply comment based on that information, and present it to the user].

[0429] A "user" refers to an entity that uses a terminal to exchange messages with others.

[0430] "Text data" refers to digital data that consists of character information.

[0431] "Audio data" refers to digital data that records audio information.

[0432] "Terminal" refers to a computing device operated by a user (e.g., smartphone, tablet, PC).

[0433] "Server" refers to a computer system that analyzes data received from a user's terminal and generates a reply comment.

[0434] "Voice recognition technology" refers to technology that converts voice data into text data.

[0435] "Sentiment analysis AI" refers to artificial intelligence that determines the emotional tone (positive, negative, neutral, etc.) from text data.

[0436] "Personality analysis AI" refers to artificial intelligence that estimates the sender's personality traits from the content of text data.

[0437] "Impression judgment AI" refers to artificial intelligence that determines the impression a sender has of a receiver based on the results of emotional and personality analysis.

[0438] "Generative AI model" refers to artificial intelligence that generates optimal reply comments based on selected impressions.

[0439] A "reply comment" refers to a reply message that a user sends to another person.

[0440] The present invention is a system that analyzes the emotions and impressions of the other person in everyday communication and generates appropriate reply comments based on the analysis. This system functions in cooperation with a server, terminals, and users.

[0441] A user uses a device (such as a smartphone, tablet, or PC) to exchange text or voice messages with others. When the device receives a message, it sends the data to a server. In the case of voice data, the device's voice recognition technology (specifically, voice recognition software) is used to convert the voice data into text data. One example of the technology used in this case is a voice recognition API.

[0442] The server analyzes the received text data using various AI modules. Sentiment analysis AI, for example, uses natural language processing APIs to determine the emotional tone (e.g., positive, negative, neutral, etc.). Personality analysis AI estimates the sender's personality traits from the text content. Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trustworthiness, etc.) based on the results of these analyses.

[0443] The analysis results are sent from the server to the device, which then notifies the user. The user checks the displayed analysis results and selects the desired impression (e.g., they want to express gratitude). The selected impression information is sent back to the server, which then activates a generative AI model to generate an optimal reply comment. This generative AI model can utilize a generative language model API, for example.

[0444] The generated reply comment is sent from the server to the terminal, and the terminal displays it to the user. The user checks the displayed reply comment and, if appropriate, sends it as a message.

[0445] As a specific example, let us consider the case where user A receives the message "How was your day?".

[0446] 1. Data Entry

[0447] The terminal sends this message to the server.

[0448] 2. Data Analysis

[0449] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0450] Sentiment analysis AI: Determines that the tone is neutral.

[0451] Personality analysis AI: Estimates whether the caller is interested.

[0452] Impression determination AI: Determines that the sender is concerned about User A's situation.

[0453] 3. Proposal Generation

[0454] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[0455] The server launches the generative AI model and generates the optimal reply comment, such as "Today was a good day, thank you for your concern!". An example of a prompt sentence for the generative AI model is, "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'"

[0456] 4. Presenting the proposal

[0457] The server sends the generated reply comment to the terminal, which displays it to User A, who then confirms and sends it.

[0458] This system enables users to accurately understand the emotions and impressions of others and to communicate effectively and appropriately.

[0459] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0460] Step 1:

[0461] A user receives a message through a text messaging application on a device, which provides input data as text or voice data. The device then detects the message and identifies its type (text or voice). If it is voice data, it uses voice recognition software to convert the voice into text data. The converted text data is provided as output.

[0462] Step 2:

[0463] The terminal transmits the obtained text data to the server. The input data is the text data received or converted by the terminal. The terminal transmits this data to the server using the communication module. The server receives the received data as input.

[0464] Step 3:

[0465] The text data received by the server is analyzed using an emotion analysis AI, personality analysis AI, and impression determination AI. The input data is the text data received by the server. The server activates the emotion analysis AI, analyzes the emotional tone of the text data (positive, negative, neutral, etc.), and obtains the results. Next, the personality analysis AI is used to analyze the sender's personality traits (e.g., extrovert, introvert, etc.). Finally, the impression determination AI analyzes the sender's impression (e.g., friendly, trustworthy, etc.) based on the results of the emotion analysis and personality analysis. The analysis results are obtained as output.

[0466] Step 4:

[0467] The server sends the analysis results to the terminal, and the terminal notifies the user of the results. The input data are the analysis results obtained by the server. The server sends these results to the terminal using a communication module. The terminal receives the received results as input and outputs them in a form that notifies the user. The terminal displays the analysis results via a user interface.

[0468] Step 5:

[0469] The user checks the analysis results on the device and selects the desired impression (e.g., expressing gratitude). The input data is the analysis results displayed on the device. The user decides on an option based on the analysis results and enters that selection information into the device. The selected impression is obtained as the output.

[0470] Step 6:

[0471] The terminal transmits the user's selection information to the server. The input data is the impression information selected by the user. The terminal transmits this information to the server via the communication module. The server receives the selection information as input.

[0472] Step 7:

[0473] The server uses a generative AI model to generate the optimal reply comment based on the selected impression information. The input data is the impression information selected by the user. The server starts the generative AI model and provides a prompt sentence as input data. Example prompt: "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'" The generative AI model outputs the optimal reply comment.

[0474] Step 8:

[0475] The server sends the generated reply comment to the terminal, which then displays it to the user. The input data is the reply comment generated by the generative AI model. The server sends this comment to the terminal via a communication module. The terminal receives the received reply comment as input and outputs it in a form that is displayed to the user. The user checks the displayed reply comment and sends it if it is appropriate.

[0476] (Application example 1)

[0477] 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."

[0478] In modern virtual stores, communication with customers is an important element, and it is necessary to quickly understand the customer's emotions and intentions and respond appropriately based on them. However, typical responses often rely on subjective judgment, which limits the ability to improve customer satisfaction. The purpose of this invention is to solve this problem and achieve smooth communication with customers by accurately analyzing customer emotions and generating appropriate responses.

[0479] 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.

[0480] In this invention, the server includes a means for receiving text data or voice data, a means for analyzing emotions, personality, and impressions of the received data, and a means for selecting the impression desired by the user, thereby enabling fast and accurate emotional analysis of customer comments and the generation of optimal reply comments.

[0481] "Audio data" is a data format for recording or transmitting audio.

[0482] "Text data" is a data format that includes characters and symbols.

[0483] "Sentiment analysis" is the process of determining sentiment from received data.

[0484] "Personality analysis" is the process of inferring the sender's personality traits from the content of the message.

[0485] "Impression determination" is a process of determining the impression that the sender has of the receiver based on the analysis results.

[0486] The "means for selecting the impression desired by the user" is a function that allows the user to select the impression that he or she wishes to give.

[0487] The "means for generating a reply comment" is a function for generating a reply comment based on the selected impression.

[0488] The "means for presenting the generated reply comment to the user" is a function for displaying the generated reply comment to the user.

[0489] "Speech recognition technology" is a technology that converts voice data into text data.

[0490] A "prompt" is a text instruction that is input to a generative AI model.

[0491] A "generative AI model" is an artificial intelligence model that generates natural language text based on a given prompt.

[0492] A "virtual store" is a store that exists in a virtual space on the Internet.

[0493] The present invention is a system for facilitating communication with customers in a virtual store. The system receives text data and voice data, analyzes them, and generates an optimal reply.

[0494] First, the user interacts with the customer using the device. When the customer speaks a question or request, the microphone in the device picks up the voice data. For example, a microphone built into smart glasses or a head-mounted display can serve this purpose. The voice data is then sent to the server in real time.

[0495] The voice data sent to the server is converted into text data using speech recognition technology. The speech_recognition library performs this process. After being converted into text data, it is analyzed by AI modules for sentiment analysis, personality analysis, and impression determination. The sentiment analysis module from the transformers library is used here.

[0496] Once the analysis is complete, the server provides the user with the option to select the impression they wish to give based on the results. This involves providing an interface where the user can select a particular impression (e.g., "trust").

[0497] Based on the selected impression, the server uses a generative AI model (e.g., GPT-3) to generate a reply comment, using the following prompt:

[0498] What customers say: What's your recommendation today?

[0499] Emotion: Interested

[0500] The impression you want to convey to your customers: Trust

[0501] Generate an appropriate reply.

[0502] The generated reply comment is sent to the terminal and displayed to the user, who then confirms it and sends it to the customer as a message.

[0503] This allows for a quick and appropriate response based on customer comments in the virtual store, improving customer satisfaction.

[0504] As a specific example, if a customer says, "What's today's recommendation?", the system converts the speech into text, determines the emotion as "interested," and if the user selects to give "trust," generates a reply saying, "This month's recommendation is a special pasta made with fresh sea urchin and caviar. We use ingredients carefully selected at the restaurant's discretion, so please give it a try." In this way, by implementing the invention, it becomes possible to communicate with customers smoothly and efficiently.

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

[0506] Step 1:

[0507] The terminal captures what the customer says. If the customer utters voice data, the microphone in the terminal records the voice data. The captured voice data is sent to the server for analysis.

[0508] Input: Customer voice data

[0509] Output: The audio data is sent to the server.

[0510] Step 2:

[0511] The server receives the voice data and converts it into text data using speech recognition technology. It uses the speech_recognition library to analyze the voice data and generate text data.

[0512] Input: Audio data

[0513] Output: Converted text data

[0514] Step 3:

[0515] The server performs sentiment analysis, personality analysis, and impression determination on the text data. For sentiment analysis, the transformers library is used to classify the text data into emotional tones (e.g., positive, negative, neutral, etc.).

[0516] Input: Text data

[0517] Output: Emotion analysis results, personality analysis results, impression judgment results

[0518] Step 4:

[0519] The server notifies the terminal of the analysis results and displays an interface for the user to select "what impression they want to give." The user selects the impression they want from the presented options.

[0520] Input: Analysis results

[0521] Output: User-selected impressions

[0522] Step 5:

[0523] The server generates a reply comment based on the impression selected by the user, sends the prompt to a generative AI model (e.g., GPT-3), and receives an appropriate reply.

[0524] Input: User-selected impression, prompt statement

[0525] Output: Generated reply comment

[0526] Step 6:

[0527] The server transmits the generated reply comment to the terminal, which displays the generated reply comment to the user, who then confirms it.

[0528] Input: Generated reply comment

[0529] Output: Reply comment presented to the user

[0530] Step 7:

[0531] The user can review the suggested reply comment, adjust it if necessary, and then send it as a final message.

[0532] Input: User confirmation results, adjustment details

[0533] Output: Final message sent

[0534] The above are the specific processing steps for carrying out the present invention, which enable smooth communication with customers in a virtual store.

[0535] 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.

[0536] This invention is a system that analyzes the emotions and impressions of the other party in communication and advises on appropriate reply comments based on the results, and also includes an emotion engine that recognizes the user's emotions. This system mainly involves interactions between a server, a terminal, and a user.

[0537] 1. Data Entry

[0538] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[0539] 2. Data Analysis

[0540] The server analyzes the received text data or converted text data using emotion analysis AI, personality analysis AI, and impression determination AI modules.

[0541] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[0542] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[0543] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0544] 3. User Emotion Recognition

[0545] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. The emotion engine detects the user's facial expressions, gestures, typing speed, etc. when viewing a message to analyze the user's emotional state at that time.

[0546] 4. Proposal Generation

[0547] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen. When the user selects the impression they want to give, the system also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, appropriate suggestions will be made.

[0548] 5. Generating optimal reply comments

[0549] Once the user selects the desired impression, the information is sent to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression, such as "Today was a good day, thank you for caring!"

[0550] 6. Presenting the proposal

[0551] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[0552] The user selects a reply comment from the list, confirms it, and then sends it as a message.

[0553] Specific examples

[0554] For example, user A receives a message in a text messaging application that asks, "How was your day?"

[0555] 1. Data Entry

[0556] The terminal sends this message to the server.

[0557] 2. Data Analysis

[0558] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0559] - Sentiment analysis AI: Determines that the tone is neutral.

[0560] - Personality analysis AI: Predicts whether the caller is interested.

[0561] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[0562] 3. User Emotion Recognition

[0563] The emotion engine analyzes user A's facial expressions and behavioral data obtained through the device's camera and sensors, and determines that user A is currently in a relaxed state.

[0564] 4. Proposal Generation

[0565] The device notifies the analysis result to User A, who selects the impression "I want to express my gratitude."

[0566] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[0567] 5. Presenting the proposal

[0568] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[0569] Through the above processing, the present invention enables appropriate communication that also takes into account the user's emotional state.

[0570] The processing flow will be explained below.

[0571] Step 1:

[0572] The terminal receives a message from a user, the message being text data or voice data.

[0573] Step 2:

[0574] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[0575] Step 3:

[0576] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[0577] Step 4:

[0578] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[0579] Step 5:

[0580] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[0581] Step 6:

[0582] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[0583] Step 7:

[0584] The emotion engine is activated and recognizes the user's current emotional state. It collects data such as the user's facial expressions and input speed through the device's camera and sensors, and analyzes the user's emotions based on that data.

[0585] Step 8:

[0586] The user uses the device to select "what impression they want to make," such as "I want to express my gratitude." This selection also takes into account the user's current emotional state.

[0587] Step 9:

[0588] The terminal transmits the user's selection to the server, where the selection information and the user's emotional state data are recorded.

[0589] Step 10:

[0590] The server activates the AI ​​based on the selected impression and the user's emotional state data to generate an optimal reply comment, such as "Today was a good day, thank you for your concern!"

[0591] Step 11:

[0592] The server sends the generated reply comment to the terminal, which displays it to the user.

[0593] Step 12:

[0594] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[0595] Step 13:

[0596] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[0597] The above are the specific processing steps of the program of the present invention.

[0598] Example 2

[0599] 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."

[0600] In communication, it is difficult to accurately analyze the other person's emotions and impressions and generate appropriate reply comments that take the user's emotional state into consideration. Furthermore, conventional systems cannot reflect the user's real-time emotional state, so they cannot provide advice that matches the user's emotions.

[0601] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to receive message data using a terminal and transmit the data to the server; means for the server to analyze the received data using modules for emotion analysis, personality analysis, and impression determination; means for the terminal to sense real-time data such as the user's facial expression and input speed and recognize the user's emotional state; means for the user to select a desired impression and transmit the information to the server; means for the server to activate a generation AI to generate an optimal reply comment based on the selected impression; and means for the terminal to present the generated reply comment to the user. This makes it possible to generate an appropriate reply comment that reflects the user's emotional state.

[0602] "User" refers to an entity that uses the system to receive and reply to messages.

[0603] "Device" refers to a device, including a camera and sensors, used by a user to send and receive messages and to sense the user's emotional state.

[0604] "Server" refers to a computer system that analyzes data sent from a terminal and performs processing to generate appropriate suggestions and reply comments.

[0605] "Message data" refers to text data or voice data that a user receives or sends.

[0606] "Sentiment analysis" refers to the process of determining the emotional tone (positive, negative, neutral, etc.) of message data.

[0607] "Personality analysis" refers to the process of inferring the sender's personality traits from the content of message data.

[0608] "Impression determination" refers to the process of determining the impression (e.g., friendliness, trust, respect, etc.) that the sender has of the receiver based on the analysis results.

[0609] An "emotion engine" refers to a system that analyzes real-time behavioral and biometric data obtained from a user's device and recognizes the user's emotions.

[0610] "Generative AI" refers to an artificial intelligence model that generates optimal reply comments based on the impression the user wants to make.

[0611] A "reply comment" refers to a message created by a user to be sent to another party.

[0612] The present invention is a system that allows interaction between a user, a terminal, and a server, analyzes the emotions and impressions of the other party in communication, and based on that analyzes the emotions and impressions, advises on appropriate reply comments. This system includes an emotion engine and can also analyze the user's emotional state.

[0613] First, a user uses a device to exchange messages with others using a communication tool (e.g., a text messaging application). The received message data (text data or voice data) is sent from the device to a server. In the case of voice data, it is converted into text data using voice recognition technology before being sent to the server. General voice recognition software is used for voice recognition.

[0614] To analyze the received text data, the server uses modules called emotion analysis AI, personality analysis AI, and impression determination AI. The emotion analysis AI determines the emotional tone of the message (positive, negative, neutral, etc.), and the personality analysis AI infers the sender's personality traits from the message content. Based on these analysis results, the impression determination AI determines the impression the sender has of the recipient (e.g., friendliness, trust, respect, etc.). This allows for the accumulation of detailed data on emotions, personality, and impressions.

[0615] The device is also equipped with an emotion engine that recognizes the user's emotional state through real-time behavioral data (e.g., facial expressions, gestures, typing speed, etc.). This is achieved using hardware such as cameras and sensors. The emotion engine analyzes this input data and determines the user's emotional state when viewing a message.

[0616] The analysis results are sent to the device. The user checks the analysis results on the screen and selects "what kind of impression they want to convey." For example, if the user selects the impression "I want to express my gratitude," the server will activate the generation AI and generate the optimal reply comment. The generation AI used here generates a reply comment based on the impression selected by the user.

[0617] The generated reply comment is sent from the server to the terminal and presented to the user. The user can then confirm the suggested reply comment and send it as a message. This enables appropriate communication that takes into account the user's emotional state.

[0618] Prompt Sentence Examples

[0619] "Generate the best reply comment if the user selects the impression 'I want to express my gratitude.' Example: 'Today was a good day, thanks for caring!'"

[0620] By implementing this system, it will be possible to generate and display reply comments that are appropriate for the user's emotional state, which was difficult with conventional systems. This system integrates multiple AI modules, such as emotion analysis AI, personality analysis AI, impression determination AI, and generation AI, to enhance communication that takes the user's emotional state into consideration.

[0621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0622] Step 1:

[0623] A user receives a message on their device. The user opens a text messaging application and views a message from another person. For example, they receive a message saying, "How was your day?" The input is the received message data, and the output is the device sending this data to the server. Specifically, the user checks the message on their smartphone, and the device sends the text data to the server.

[0624] Step 2:

[0625] The server analyzes the received message data. The server then analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The input is the received text data, and the output is the analysis results of each AI module. Specifically, the server analyzes the data, the emotion analysis AI determines the tone of the message, the personality analysis AI estimates the sender's personality traits, and the impression determination AI determines the impression.

[0626] Step 3:

[0627] The device recognizes the user's emotions. The device's emotion engine acquires data such as the user's facial expressions, gestures, and input speed through cameras and sensors. The input is the user's real-time behavioral data, and the output is the user's emotional state as recognized by the emotion engine. Specifically, the device's camera captures a picture of the user's face, and the sensor detects the user's input speed. The emotion engine analyzes this data and determines the user's emotional state, such as whether they are relaxed.

[0628] Step 4:

[0629] The server generates a suggestion based on the analysis results. The user selects the desired impression, such as "I want to express my gratitude," and this information is sent to the server. The server then activates the generation AI based on the selected impression and the user's emotional state, and generates the optimal reply comment. The input is the user's selected impression information and emotional state, as well as the analyzed data, and the output is the generated reply comment. In concrete terms, the server receives the information "I want to express my gratitude," and the generation AI generates the comment "Today was a good day, thank you for thinking about me!"

[0630] Step 5:

[0631] The terminal presents the generated reply comment to the user. The terminal receives the reply comment sent from the server and notifies the user. The input is the generated reply comment, and the output is the notification to the user. Specifically, the terminal displays "Suggested reply comment: Today was a good day, thanks for your concern!" to the user, and the user confirms it.

[0632] Step 6:

[0633] The user submits a reply comment. The user reviews the proposed reply comment, corrects it if necessary, and then submits it. The input is the content of the generated reply comment, and the output is the message sent to the other person. In concrete terms, the user confirms the comment "Today was a good day, thanks for thinking about me!" and submits the message.

[0634] Through the above processing steps, the system can generate optimal reply comments that reflect the user's emotional state.

[0635] (Application example 2)

[0636] 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."

[0637] Conventional systems that generate reply comments appropriate to each user's emotional state have the problem of being unable to consider the user's own emotional state or the appropriate content at that time. Furthermore, few systems have the functionality to analyze the user's emotional state in real time and recommend optimal content based on that information. As a result, there is a risk that the user experience will be impaired and satisfaction with communication and content viewing will decrease.

[0638] The identification process by the identification 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 text data or voice data, means for performing emotion analysis, personality analysis, and impression determination on the received data, means for selecting the impression desired by the user, means for generating an optimal reply comment based on the selected impression, means for presenting the generated reply comment to the user, means for analyzing the user's emotional state in real time, and means for recommending content based on the user's emotional state. This makes it possible to generate an optimal reply comment taking into account the user's real-time emotional state and to recommend content that suits the user's current emotion.

[0639] "Text data" is character string information expressed electronically.

[0640] "Audio data" is information that represents audio in digital form.

[0641] "Sentiment analysis" is a technique for determining emotional tone (positive, negative, neutral, etc.) from text or audio data.

[0642] "Personality analysis" is a technology that estimates a caller's personality traits (e.g., extroversion, introversion, high sensitivity, etc.) from text or voice data.

[0643] "Impression assessment" is a technology that determines the impression (e.g., friendliness, trust, respect, etc.) that a sender has of a receiver based on the results of emotional and personality analysis.

[0644] "User" means an individual or corporation that uses the system.

[0645] A "reply comment" is a comment to reply to a received message, and is suggested by the generation AI.

[0646] "Emotional state" refers to a user's current emotional state, as measured in real time.

[0647] "Content" refers to information or media that can be viewed or used (e.g., movies, dramas, anime, music, articles, etc.).

[0648] "Content recommendation" is the process of automatically suggesting content that is appropriate for a user's emotional state.

[0649] The present invention is a system that analyzes a user's emotional state in real time and recommends optimal reply comments and content based on that information. This system mainly involves interactions between a server, a terminal, and a user.

[0650] Users use devices such as smartphones to exchange messages with others through communication tools (e.g., text messaging applications). When a device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[0651] The server analyzes the received text data or converted text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the message, and the personality analysis AI infers the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the message. The impression determination AI determines the sender's impression of the recipient (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0652] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. This emotion engine detects the user's facial expressions, gestures, and typing speed when viewing a message, and analyzes the user's emotional state at that time. For example, the device's camera and sensor module can be used to acquire the user's facial expressions and behavioral data, and the analysis can be performed in real time.

[0653] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen and select the impression they want to convey. The server also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, the server will suggest a reply comment that is appropriate for that situation.

[0654] Once the user selects the desired impression, the information is sent to the server, which then activates the generative AI model to generate the optimal reply comment based on the selected impression. For example, a comment like "Today was a good day, thank you for your concern!" may be generated. The server then sends the generated reply comment to the device, which then displays it to the user. The user can then confirm the presented reply comment and send it as a message.

[0655] Furthermore, the engine recommends content that matches the user's current mood based on their emotional state. This content recommendation engine selects content according to the user's emotional state, such as recommending relaxing music or videos when the user is relaxing.

[0656] As a concrete example, if User A receives a message in a text messaging application using the message "What do you think of this anime?", and User A is in a relaxed state, the system will select the impression that the user wants to express gratitude and generate a reply such as "Thank you for watching this anime!". In addition, if User A is tired, content that will help them relax will be recommended.

[0657] An example prompt is:

[0658] "Perform sentiment and personality analysis on the comment 'What do you think of this anime?' and generate and display the optimal reply if the user chooses to express gratitude. At the same time, if the user is tired, recommend content that will help them relax."

[0659] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0660] Step 1: Data entry

[0661] A user receives a comment via a text messaging application. The device captures the comment as text data or voice data, and if the voice data is voice data, converts it to text data using voice recognition technology. The device then sends the converted text data to a server.

[0662] Input: Received text or voice data

[0663] Output: Text data sent to the server

[0664] Step 2: Data analysis

[0665] The server analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the text data, and the personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the text data. Finally, the impression determination AI determines the sender's impression of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0666] Input: Text data

[0667] Output: Emotional tone, personality traits, impression judgment results

[0668] Step 3: Recognizing user emotions

[0669] The device uses a camera and sensor module to capture the user's facial expressions and behavioral data in real time, and sends this data to an emotion engine to analyze the user's current emotional state (e.g., relaxed, tired, stressed, etc.).

[0670] Input: User's facial expression data and behavior data

[0671] Output: Current emotional state

[0672] Step 4: Generate proposals

[0673] The server sends the analysis results to the device, which then notifies the user. The user checks the analysis results on the screen and selects the impression they want to convey. The user's current emotional state estimated by the emotion engine is also taken into consideration. Based on the selected impression and emotional state, the server activates a generative AI model to generate the optimal reply comment.

[0674] Input: Analysis results, selected impression, current emotional state

[0675] Output: Generated reply comment

[0676] Step 5: Present your proposal

[0677] The server sends the generated reply comment to the terminal, which displays the content to the user. The user checks the presented reply comment, makes corrections if necessary, and then sends it as a message.

[0678] Input: Generated reply comment

[0679] Output: Reply comments displayed to the user, message sent

[0680] Step 6: Content Recommendation

[0681] Based on the user's emotional state, the server activates a content recommendation engine to select content that suits the user's current mood (e.g., relaxing music or videos). The selected content information is sent to the device and displayed to the user.

[0682] Input: The user's current emotional state

[0683] Output: Recommended content information

[0684] Through the above processing steps, the system of the present invention is able to generate optimal reply comments that take into account the user's real-time emotional state and recommend content that suits the user's current emotions.

[0685] 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.

[0686] 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.

[0687] 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.

[0688] [Third embodiment]

[0689] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0690] 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.

[0691] 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).

[0692] 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.

[0693] 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.

[0694] 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).

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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."

[0701] The present invention is a system that analyzes the emotions and impressions of the other party in daily communication and advises the user on appropriate reply comments based on the analysis. This system mainly involves interactions between a server, a terminal, and a user.

[0702] 1. Data Entry

[0703] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology and then sent to the server.

[0704] 2. Data Analysis

[0705] The server analyzes the received text data or converted text data using AI modules for emotion analysis, personality analysis, and impression determination.

[0706] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[0707] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[0708] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0709] 3. Proposal Generation

[0710] The device that receives the analysis results notifies the user, and the user selects on the device "what kind of impression they want to give."

[0711] Once the user selects the impression they want, they send that information to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression.

[0712] 4. Presenting the proposal

[0713] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[0714] The user selects the suggested reply comment, confirms it, and then sends it as a message.

[0715] Specific examples

[0716] One day, User A receives a message in the text messaging application they are using that asks, "How was your day?"

[0717] 1. Data Entry

[0718] The terminal sends this message to the server.

[0719] 2. Data Analysis

[0720] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0721] - Sentiment analysis AI: Determines that the tone is neutral.

[0722] - Personality analysis AI: Predicts whether the caller is interested.

[0723] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[0724] 3. Proposal Generation

[0725] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[0726] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[0727] 4. Presenting the proposal

[0728] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[0729] The present invention enables a user to accurately understand the emotions and impressions of others, enabling appropriate communication.

[0730] The processing flow will be explained below.

[0731] Step 1:

[0732] The terminal receives a message from a user, the message being text data or voice data.

[0733] Step 2:

[0734] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[0735] Step 3:

[0736] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[0737] Step 4:

[0738] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[0739] Step 5:

[0740] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[0741] Step 6:

[0742] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[0743] Step 7:

[0744] The user uses the terminal to select "what kind of impression they want to make," such as "I want to convey my gratitude."

[0745] Step 8:

[0746] The terminal transmits the user's selection to the server, where the selection information is recorded.

[0747] Step 9:

[0748] The server will then activate the AI ​​based on the selected impression to generate the best reply comment, for example, "Today was a good day, thank you for your concern!"

[0749] Step 10:

[0750] The server sends the generated reply comment to the terminal, which displays it to the user.

[0751] Step 11:

[0752] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[0753] Step 12:

[0754] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[0755] The above are the specific processing steps of the program of the present invention.

[0756] Example 1

[0757] 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."

[0758] Conventional communication tools have the problem that it is difficult to accurately understand the other person's feelings and impressions and generate appropriate replies. As a result, users often send misleading messages, making smooth communication difficult.

[0759] 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.

[0760] In this invention, the server includes: [means for analyzing data using emotion analysis AI, personality analysis AI, and impression determination AI on the server; [means for generating an optimal reply comment based on the impression selected using a generation AI model; and [means for presenting the generated reply comment to the user.] This makes it possible [to accurately analyze the other person's emotions and impressions, automatically generate an optimal reply comment based on that information, and present it to the user].

[0761] A "user" refers to an entity that uses a terminal to exchange messages with others.

[0762] "Text data" refers to digital data that consists of character information.

[0763] "Audio data" refers to digital data that records audio information.

[0764] "Terminal" refers to a computing device operated by a user (e.g., smartphone, tablet, PC).

[0765] "Server" refers to a computer system that analyzes data received from a user's terminal and generates a reply comment.

[0766] "Voice recognition technology" refers to technology that converts voice data into text data.

[0767] "Sentiment analysis AI" refers to artificial intelligence that determines the emotional tone (positive, negative, neutral, etc.) from text data.

[0768] "Personality analysis AI" refers to artificial intelligence that estimates the sender's personality traits from the content of text data.

[0769] "Impression judgment AI" refers to artificial intelligence that determines the impression a sender has of a receiver based on the results of emotional and personality analysis.

[0770] "Generative AI model" refers to artificial intelligence that generates optimal reply comments based on selected impressions.

[0771] A "reply comment" refers to a reply message that a user sends to another person.

[0772] The present invention is a system that analyzes the emotions and impressions of the other person in everyday communication and generates appropriate reply comments based on the analysis. This system functions in cooperation with a server, terminals, and users.

[0773] A user uses a device (such as a smartphone, tablet, or PC) to exchange text or voice messages with others. When the device receives a message, it sends the data to a server. In the case of voice data, the device's voice recognition technology (specifically, voice recognition software) is used to convert the voice data into text data. One example of the technology used in this case is a voice recognition API.

[0774] The server analyzes the received text data using various AI modules. Sentiment analysis AI, for example, uses natural language processing APIs to determine the emotional tone (e.g., positive, negative, neutral, etc.). Personality analysis AI estimates the sender's personality traits from the text content. Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trustworthiness, etc.) based on the results of these analyses.

[0775] The analysis results are sent from the server to the device, which then notifies the user. The user checks the displayed analysis results and selects the desired impression (e.g., they want to express gratitude). The selected impression information is sent back to the server, which then activates a generative AI model to generate an optimal reply comment. This generative AI model can utilize a generative language model API, for example.

[0776] The generated reply comment is sent from the server to the terminal, and the terminal displays it to the user. The user checks the displayed reply comment and, if appropriate, sends it as a message.

[0777] As a specific example, let us consider the case where user A receives the message "How was your day?".

[0778] 1. Data Entry

[0779] The terminal sends this message to the server.

[0780] 2. Data Analysis

[0781] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0782] Sentiment analysis AI: Determines that the tone is neutral.

[0783] Personality analysis AI: Estimates whether the caller is interested.

[0784] Impression determination AI: Determines that the sender is concerned about User A's situation.

[0785] 3. Proposal Generation

[0786] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[0787] The server launches the generative AI model and generates the optimal reply comment, such as "Today was a good day, thank you for your concern!". An example of a prompt sentence for the generative AI model is, "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'"

[0788] 4. Presenting the proposal

[0789] The server sends the generated reply comment to the terminal, which displays it to User A, who then confirms and sends it.

[0790] This system enables users to accurately understand the emotions and impressions of others and to communicate effectively and appropriately.

[0791] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0792] Step 1:

[0793] A user receives a message through a text messaging application on a device, which provides input data as text or voice data. The device then detects the message and identifies its type (text or voice). If it is voice data, it uses voice recognition software to convert the voice into text data. The converted text data is provided as output.

[0794] Step 2:

[0795] The terminal transmits the obtained text data to the server. The input data is the text data received or converted by the terminal. The terminal transmits this data to the server using the communication module. The server receives the received data as input.

[0796] Step 3:

[0797] The text data received by the server is analyzed using an emotion analysis AI, personality analysis AI, and impression determination AI. The input data is the text data received by the server. The server activates the emotion analysis AI, analyzes the emotional tone of the text data (positive, negative, neutral, etc.), and obtains the results. Next, the personality analysis AI is used to analyze the sender's personality traits (e.g., extrovert, introvert, etc.). Finally, the impression determination AI analyzes the sender's impression (e.g., friendly, trustworthy, etc.) based on the results of the emotion analysis and personality analysis. The analysis results are obtained as output.

[0798] Step 4:

[0799] The server sends the analysis results to the terminal, and the terminal notifies the user of the results. The input data are the analysis results obtained by the server. The server sends these results to the terminal using a communication module. The terminal receives the received results as input and outputs them in a form that notifies the user. The terminal displays the analysis results via a user interface.

[0800] Step 5:

[0801] The user checks the analysis results on the device and selects the desired impression (e.g., expressing gratitude). The input data is the analysis results displayed on the device. The user decides on an option based on the analysis results and enters that selection information into the device. The selected impression is obtained as the output.

[0802] Step 6:

[0803] The terminal transmits the user's selection information to the server. The input data is the impression information selected by the user. The terminal transmits this information to the server via the communication module. The server receives the selection information as input.

[0804] Step 7:

[0805] The server uses a generative AI model to generate the optimal reply comment based on the selected impression information. The input data is the impression information selected by the user. The server starts the generative AI model and provides a prompt sentence as input data. Example prompt: "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'" The generative AI model outputs the optimal reply comment.

[0806] Step 8:

[0807] The server sends the generated reply comment to the terminal, which then displays it to the user. The input data is the reply comment generated by the generative AI model. The server sends this comment to the terminal via a communication module. The terminal receives the received reply comment as input and outputs it in a form that is displayed to the user. The user checks the displayed reply comment and sends it if it is appropriate.

[0808] (Application example 1)

[0809] 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."

[0810] In modern virtual stores, communication with customers is an important element, and it is necessary to quickly understand the customer's emotions and intentions and respond appropriately based on them. However, typical responses often rely on subjective judgment, which limits the ability to improve customer satisfaction. The purpose of this invention is to solve this problem and achieve smooth communication with customers by accurately analyzing customer emotions and generating appropriate responses.

[0811] 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.

[0812] In this invention, the server includes a means for receiving text data or voice data, a means for analyzing emotions, personality, and impressions of the received data, and a means for selecting the impression desired by the user, thereby enabling fast and accurate emotional analysis of customer comments and the generation of optimal reply comments.

[0813] "Audio data" is a data format for recording or transmitting audio.

[0814] "Text data" is a data format that includes characters and symbols.

[0815] "Sentiment analysis" is the process of determining sentiment from received data.

[0816] "Personality analysis" is the process of inferring the sender's personality traits from the content of the message.

[0817] "Impression determination" is a process of determining the impression that the sender has of the receiver based on the analysis results.

[0818] The "means for selecting the impression desired by the user" is a function that allows the user to select the impression that he or she wishes to give.

[0819] The "means for generating a reply comment" is a function for generating a reply comment based on the selected impression.

[0820] The "means for presenting the generated reply comment to the user" is a function for displaying the generated reply comment to the user.

[0821] "Speech recognition technology" is a technology that converts voice data into text data.

[0822] A "prompt" is a text instruction that is input to a generative AI model.

[0823] A "generative AI model" is an artificial intelligence model that generates natural language text based on a given prompt.

[0824] A "virtual store" is a store that exists in a virtual space on the Internet.

[0825] The present invention is a system for facilitating communication with customers in a virtual store. The system receives text data and voice data, analyzes them, and generates an optimal reply.

[0826] First, the user interacts with the customer using the device. When the customer speaks a question or request, the microphone in the device picks up the voice data. For example, a microphone built into smart glasses or a head-mounted display can serve this purpose. The voice data is then sent to the server in real time.

[0827] The voice data sent to the server is converted into text data using speech recognition technology. The speech_recognition library performs this process. After being converted into text data, it is analyzed by AI modules for sentiment analysis, personality analysis, and impression determination. The sentiment analysis module from the transformers library is used here.

[0828] Once the analysis is complete, the server provides the user with the option to select the impression they wish to give based on the results. This involves providing an interface where the user can select a particular impression (e.g., "trust").

[0829] Based on the selected impression, the server uses a generative AI model (e.g., GPT-3) to generate a reply comment, using the following prompt:

[0830] What customers say: What's your recommendation today?

[0831] Emotion: Interested

[0832] The impression you want to convey to your customers: Trust

[0833] Generate an appropriate reply.

[0834] The generated reply comment is sent to the terminal and displayed to the user, who then confirms it and sends it to the customer as a message.

[0835] This allows for a quick and appropriate response based on customer comments in the virtual store, improving customer satisfaction.

[0836] As a specific example, if a customer says, "What's today's recommendation?", the system converts the speech into text, determines the emotion as "interested," and if the user selects to give "trust," generates a reply saying, "This month's recommendation is a special pasta made with fresh sea urchin and caviar. We use ingredients carefully selected at the restaurant's discretion, so please give it a try." In this way, by implementing the invention, it becomes possible to communicate with customers smoothly and efficiently.

[0837] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0838] Step 1:

[0839] The terminal captures what the customer says. If the customer utters voice data, the microphone in the terminal records the voice data. The captured voice data is sent to the server for analysis.

[0840] Input: Customer voice data

[0841] Output: The audio data is sent to the server.

[0842] Step 2:

[0843] The server receives the voice data and converts it into text data using speech recognition technology. It uses the speech_recognition library to analyze the voice data and generate text data.

[0844] Input: Audio data

[0845] Output: Converted text data

[0846] Step 3:

[0847] The server performs sentiment analysis, personality analysis, and impression determination on the text data. For sentiment analysis, the transformers library is used to classify the text data into emotional tones (e.g., positive, negative, neutral, etc.).

[0848] Input: Text data

[0849] Output: Emotion analysis results, personality analysis results, impression judgment results

[0850] Step 4:

[0851] The server notifies the terminal of the analysis results and displays an interface for the user to select "what impression they want to give." The user selects the impression they want from the presented options.

[0852] Input: Analysis results

[0853] Output: User-selected impressions

[0854] Step 5:

[0855] The server generates a reply comment based on the impression selected by the user, sends the prompt to a generative AI model (e.g., GPT-3), and receives an appropriate reply.

[0856] Input: User-selected impression, prompt statement

[0857] Output: Generated reply comment

[0858] Step 6:

[0859] The server transmits the generated reply comment to the terminal, which displays the generated reply comment to the user, who then confirms it.

[0860] Input: Generated reply comment

[0861] Output: Reply comment presented to the user

[0862] Step 7:

[0863] The user can review the suggested reply comment, adjust it if necessary, and then send it as a final message.

[0864] Input: User confirmation results, adjustment details

[0865] Output: Final message sent

[0866] The above are the specific processing steps for carrying out the present invention, which enable smooth communication with customers in a virtual store.

[0867] 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.

[0868] This invention is a system that analyzes the emotions and impressions of the other party in communication and advises on appropriate reply comments based on the results, and also includes an emotion engine that recognizes the user's emotions. This system mainly involves interactions between a server, a terminal, and a user.

[0869] 1. Data Entry

[0870] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[0871] 2. Data Analysis

[0872] The server analyzes the received text data or converted text data using emotion analysis AI, personality analysis AI, and impression determination AI modules.

[0873] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[0874] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[0875] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0876] 3. User Emotion Recognition

[0877] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. The emotion engine detects the user's facial expressions, gestures, typing speed, etc. when viewing a message to analyze the user's emotional state at that time.

[0878] 4. Proposal Generation

[0879] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen. When the user selects the impression they want to give, the system also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, appropriate suggestions will be made.

[0880] 5. Generating optimal reply comments

[0881] Once the user selects the desired impression, the information is sent to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression, such as "Today was a good day, thank you for caring!"

[0882] 6. Presenting the proposal

[0883] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[0884] The user selects a reply comment from the list, confirms it, and then sends it as a message.

[0885] Specific examples

[0886] For example, user A receives a message in a text messaging application that asks, "How was your day?"

[0887] 1. Data Entry

[0888] The terminal sends this message to the server.

[0889] 2. Data Analysis

[0890] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[0891] - Sentiment analysis AI: Determines that the tone is neutral.

[0892] - Personality analysis AI: Predicts whether the caller is interested.

[0893] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[0894] 3. User Emotion Recognition

[0895] The emotion engine analyzes user A's facial expressions and behavioral data obtained through the device's camera and sensors, and determines that user A is currently in a relaxed state.

[0896] 4. Proposal Generation

[0897] The device notifies the analysis result to User A, who selects the impression "I want to express my gratitude."

[0898] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[0899] 5. Presenting the proposal

[0900] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[0901] Through the above processing, the present invention enables appropriate communication that also takes into account the user's emotional state.

[0902] The processing flow will be explained below.

[0903] Step 1:

[0904] The terminal receives a message from a user, the message being text data or voice data.

[0905] Step 2:

[0906] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[0907] Step 3:

[0908] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[0909] Step 4:

[0910] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[0911] Step 5:

[0912] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[0913] Step 6:

[0914] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[0915] Step 7:

[0916] The emotion engine is activated and recognizes the user's current emotional state. It collects data such as the user's facial expressions and input speed through the device's camera and sensors, and analyzes the user's emotions based on that data.

[0917] Step 8:

[0918] The user uses the device to select "what impression they want to make," such as "I want to express my gratitude." This selection also takes into account the user's current emotional state.

[0919] Step 9:

[0920] The terminal transmits the user's selection to the server, where the selection information and the user's emotional state data are recorded.

[0921] Step 10:

[0922] The server activates the AI ​​based on the selected impression and the user's emotional state data to generate an optimal reply comment, such as "Today was a good day, thank you for your concern!"

[0923] Step 11:

[0924] The server sends the generated reply comment to the terminal, which displays it to the user.

[0925] Step 12:

[0926] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[0927] Step 13:

[0928] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[0929] The above are the specific processing steps of the program of the present invention.

[0930] Example 2

[0931] 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."

[0932] In communication, it is difficult to accurately analyze the other person's emotions and impressions and generate appropriate reply comments that take the user's emotional state into consideration. Furthermore, conventional systems cannot reflect the user's real-time emotional state, so they cannot provide advice that matches the user's emotions.

[0933] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to receive message data using a terminal and transmit the data to the server; means for the server to analyze the received data using modules for emotion analysis, personality analysis, and impression determination; means for the terminal to sense real-time data such as the user's facial expression and input speed and recognize the user's emotional state; means for the user to select a desired impression and transmit the information to the server; means for the server to activate a generation AI to generate an optimal reply comment based on the selected impression; and means for the terminal to present the generated reply comment to the user. This makes it possible to generate an appropriate reply comment that reflects the user's emotional state.

[0934] "User" refers to an entity that uses the system to receive and reply to messages.

[0935] "Device" refers to a device, including a camera and sensors, used by a user to send and receive messages and to sense the user's emotional state.

[0936] "Server" refers to a computer system that analyzes data sent from a terminal and performs processing to generate appropriate suggestions and reply comments.

[0937] "Message data" refers to text data or voice data that a user receives or sends.

[0938] "Sentiment analysis" refers to the process of determining the emotional tone (positive, negative, neutral, etc.) of message data.

[0939] "Personality analysis" refers to the process of inferring the sender's personality traits from the content of message data.

[0940] "Impression determination" refers to the process of determining the impression (e.g., friendliness, trust, respect, etc.) that the sender has of the receiver based on the analysis results.

[0941] An "emotion engine" refers to a system that analyzes real-time behavioral and biometric data obtained from a user's device and recognizes the user's emotions.

[0942] "Generative AI" refers to an artificial intelligence model that generates optimal reply comments based on the impression the user wants to make.

[0943] A "reply comment" refers to a message created by a user to be sent to another party.

[0944] The present invention is a system that allows interaction between a user, a terminal, and a server, analyzes the emotions and impressions of the other party in communication, and based on that analyzes the emotions and impressions, advises on appropriate reply comments. This system includes an emotion engine and can also analyze the user's emotional state.

[0945] First, a user uses a device to exchange messages with others using a communication tool (e.g., a text messaging application). The received message data (text data or voice data) is sent from the device to a server. In the case of voice data, it is converted into text data using voice recognition technology before being sent to the server. General voice recognition software is used for voice recognition.

[0946] To analyze the received text data, the server uses modules called emotion analysis AI, personality analysis AI, and impression determination AI. The emotion analysis AI determines the emotional tone of the message (positive, negative, neutral, etc.), and the personality analysis AI infers the sender's personality traits from the message content. Based on these analysis results, the impression determination AI determines the impression the sender has of the recipient (e.g., friendliness, trust, respect, etc.). This allows for the accumulation of detailed data on emotions, personality, and impressions.

[0947] The device is also equipped with an emotion engine that recognizes the user's emotional state through real-time behavioral data (e.g., facial expressions, gestures, typing speed, etc.). This is achieved using hardware such as cameras and sensors. The emotion engine analyzes this input data and determines the user's emotional state when viewing a message.

[0948] The analysis results are sent to the device. The user checks the analysis results on the screen and selects "what kind of impression they want to convey." For example, if the user selects the impression "I want to express my gratitude," the server will activate the generation AI and generate the optimal reply comment. The generation AI used here generates a reply comment based on the impression selected by the user.

[0949] The generated reply comment is sent from the server to the terminal and presented to the user. The user can then confirm the suggested reply comment and send it as a message. This enables appropriate communication that takes into account the user's emotional state.

[0950] Prompt Sentence Examples

[0951] "Generate the best reply comment if the user selects the impression 'I want to express my gratitude.' Example: 'Today was a good day, thanks for caring!'"

[0952] By implementing this system, it will be possible to generate and display reply comments that are appropriate for the user's emotional state, which was difficult with conventional systems. This system integrates multiple AI modules, such as emotion analysis AI, personality analysis AI, impression determination AI, and generation AI, to enhance communication that takes the user's emotional state into consideration.

[0953] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0954] Step 1:

[0955] A user receives a message on their device. The user opens a text messaging application and views a message from another person. For example, they receive a message saying, "How was your day?" The input is the received message data, and the output is the device sending this data to the server. Specifically, the user checks the message on their smartphone, and the device sends the text data to the server.

[0956] Step 2:

[0957] The server analyzes the received message data. The server then analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The input is the received text data, and the output is the analysis results of each AI module. Specifically, the server analyzes the data, the emotion analysis AI determines the tone of the message, the personality analysis AI estimates the sender's personality traits, and the impression determination AI determines the impression.

[0958] Step 3:

[0959] The device recognizes the user's emotions. The device's emotion engine acquires data such as the user's facial expressions, gestures, and input speed through cameras and sensors. The input is the user's real-time behavioral data, and the output is the user's emotional state as recognized by the emotion engine. Specifically, the device's camera captures a picture of the user's face, and the sensor detects the user's input speed. The emotion engine analyzes this data and determines the user's emotional state, such as whether they are relaxed.

[0960] Step 4:

[0961] The server generates a suggestion based on the analysis results. The user selects the desired impression, such as "I want to express my gratitude," and this information is sent to the server. The server then activates the generation AI based on the selected impression and the user's emotional state, and generates the optimal reply comment. The input is the user's selected impression information and emotional state, as well as the analyzed data, and the output is the generated reply comment. In concrete terms, the server receives the information "I want to express my gratitude," and the generation AI generates the comment "Today was a good day, thank you for thinking about me!"

[0962] Step 5:

[0963] The terminal presents the generated reply comment to the user. The terminal receives the reply comment sent from the server and notifies the user. The input is the generated reply comment, and the output is the notification to the user. Specifically, the terminal displays "Suggested reply comment: Today was a good day, thanks for your concern!" to the user, and the user confirms it.

[0964] Step 6:

[0965] The user submits a reply comment. The user reviews the proposed reply comment, corrects it if necessary, and then submits it. The input is the content of the generated reply comment, and the output is the message sent to the other person. In concrete terms, the user confirms the comment "Today was a good day, thanks for thinking about me!" and submits the message.

[0966] Through the above processing steps, the system can generate optimal reply comments that reflect the user's emotional state.

[0967] (Application example 2)

[0968] 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."

[0969] Conventional systems that generate reply comments appropriate to each user's emotional state have the problem of being unable to consider the user's own emotional state or the appropriate content at that time. Furthermore, few systems have the functionality to analyze the user's emotional state in real time and recommend optimal content based on that information. As a result, there is a risk that the user experience will be impaired and satisfaction with communication and content viewing will decrease.

[0970] The identification process by the identification 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 text data or voice data, means for performing emotion analysis, personality analysis, and impression determination on the received data, means for selecting the impression desired by the user, means for generating an optimal reply comment based on the selected impression, means for presenting the generated reply comment to the user, means for analyzing the user's emotional state in real time, and means for recommending content based on the user's emotional state. This makes it possible to generate an optimal reply comment taking into account the user's real-time emotional state and to recommend content that suits the user's current emotion.

[0971] "Text data" is character string information expressed electronically.

[0972] "Audio data" is information that represents audio in digital form.

[0973] "Sentiment analysis" is a technique for determining emotional tone (positive, negative, neutral, etc.) from text or audio data.

[0974] "Personality analysis" is a technology that estimates a caller's personality traits (e.g., extroversion, introversion, high sensitivity, etc.) from text or voice data.

[0975] "Impression assessment" is a technology that determines the impression (e.g., friendliness, trust, respect, etc.) that a sender has of a receiver based on the results of emotional and personality analysis.

[0976] "User" means an individual or corporation that uses the system.

[0977] A "reply comment" is a comment to reply to a received message, and is suggested by the generation AI.

[0978] "Emotional state" refers to a user's current emotional state, as measured in real time.

[0979] "Content" refers to information or media that can be viewed or used (e.g., movies, dramas, anime, music, articles, etc.).

[0980] "Content recommendation" is the process of automatically suggesting content that is appropriate for a user's emotional state.

[0981] The present invention is a system that analyzes a user's emotional state in real time and recommends optimal reply comments and content based on that information. This system mainly involves interactions between a server, a terminal, and a user.

[0982] Users use devices such as smartphones to exchange messages with others through communication tools (e.g., text messaging applications). When a device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[0983] The server analyzes the received text data or converted text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the message, and the personality analysis AI infers the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the message. The impression determination AI determines the sender's impression of the recipient (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0984] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. This emotion engine detects the user's facial expressions, gestures, and typing speed when viewing a message, and analyzes the user's emotional state at that time. For example, the device's camera and sensor module can be used to acquire the user's facial expressions and behavioral data, and the analysis can be performed in real time.

[0985] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen and select the impression they want to convey. The server also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, the server will suggest a reply comment that is appropriate for that situation.

[0986] Once the user selects the desired impression, the information is sent to the server, which then activates the generative AI model to generate the optimal reply comment based on the selected impression. For example, a comment like "Today was a good day, thank you for your concern!" may be generated. The server then sends the generated reply comment to the device, which then displays it to the user. The user can then confirm the presented reply comment and send it as a message.

[0987] Furthermore, the engine recommends content that matches the user's current mood based on their emotional state. This content recommendation engine selects content according to the user's emotional state, such as recommending relaxing music or videos when the user is relaxing.

[0988] As a concrete example, if User A receives a message in a text messaging application using the message "What do you think of this anime?", and User A is in a relaxed state, the system will select the impression that the user wants to express gratitude and generate a reply such as "Thank you for watching this anime!". In addition, if User A is tired, content that will help them relax will be recommended.

[0989] An example prompt is:

[0990] "Perform sentiment and personality analysis on the comment 'What do you think of this anime?' and generate and display the optimal reply if the user chooses to express gratitude. At the same time, if the user is tired, recommend content that will help them relax."

[0991] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0992] Step 1: Data entry

[0993] A user receives a comment via a text messaging application. The device captures the comment as text data or voice data, and if the voice data is voice data, converts it to text data using voice recognition technology. The device then sends the converted text data to a server.

[0994] Input: Received text or voice data

[0995] Output: Text data sent to the server

[0996] Step 2: Data analysis

[0997] The server analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the text data, and the personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the text data. Finally, the impression determination AI determines the sender's impression of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[0998] Input: Text data

[0999] Output: Emotional tone, personality traits, impression judgment results

[1000] Step 3: Recognizing user emotions

[1001] The device uses a camera and sensor module to capture the user's facial expressions and behavioral data in real time, and sends this data to an emotion engine to analyze the user's current emotional state (e.g., relaxed, tired, stressed, etc.).

[1002] Input: User's facial expression data and behavior data

[1003] Output: Current emotional state

[1004] Step 4: Generate proposals

[1005] The server sends the analysis results to the device, which then notifies the user. The user checks the analysis results on the screen and selects the impression they want to convey. The user's current emotional state estimated by the emotion engine is also taken into consideration. Based on the selected impression and emotional state, the server activates a generative AI model to generate the optimal reply comment.

[1006] Input: Analysis results, selected impression, current emotional state

[1007] Output: Generated reply comment

[1008] Step 5: Present your proposal

[1009] The server sends the generated reply comment to the terminal, which displays the content to the user. The user checks the presented reply comment, makes corrections if necessary, and then sends it as a message.

[1010] Input: Generated reply comment

[1011] Output: Reply comments displayed to the user, message sent

[1012] Step 6: Content Recommendation

[1013] Based on the user's emotional state, the server activates a content recommendation engine to select content that suits the user's current mood (e.g., relaxing music or videos). The selected content information is sent to the device and displayed to the user.

[1014] Input: The user's current emotional state

[1015] Output: Recommended content information

[1016] Through the above processing steps, the system of the present invention is able to generate optimal reply comments that take into account the user's real-time emotional state and recommend content that suits the user's current emotions.

[1017] 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.

[1018] 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.

[1019] 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.

[1020] [Fourth embodiment]

[1021] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1022] 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.

[1023] 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).

[1024] 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.

[1025] 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.

[1026] 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).

[1027] 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.

[1028] 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.

[1029] 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.

[1030] 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.

[1031] 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.

[1032] 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.

[1033] 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."

[1034] The present invention is a system that analyzes the emotions and impressions of the other party in daily communication and advises the user on appropriate reply comments based on the analysis. This system mainly involves interactions between a server, a terminal, and a user.

[1035] 1. Data Entry

[1036] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology and then sent to the server.

[1037] 2. Data Analysis

[1038] The server analyzes the received text data or converted text data using AI modules for emotion analysis, personality analysis, and impression determination.

[1039] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[1040] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[1041] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[1042] 3. Proposal Generation

[1043] The device that receives the analysis results notifies the user, and the user selects on the device "what kind of impression they want to give."

[1044] Once the user selects the impression they want, they send that information to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression.

[1045] 4. Presenting the proposal

[1046] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[1047] The user selects the suggested reply comment, confirms it, and then sends it as a message.

[1048] Specific examples

[1049] One day, User A receives a message in the text messaging application they are using that asks, "How was your day?"

[1050] 1. Data Entry

[1051] The terminal sends this message to the server.

[1052] 2. Data Analysis

[1053] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[1054] - Sentiment analysis AI: Determines that the tone is neutral.

[1055] - Personality analysis AI: Predicts whether the caller is interested.

[1056] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[1057] 3. Proposal Generation

[1058] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[1059] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[1060] 4. Presenting the proposal

[1061] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[1062] The present invention enables a user to accurately understand the emotions and impressions of others, enabling appropriate communication.

[1063] The processing flow will be explained below.

[1064] Step 1:

[1065] The terminal receives a message from a user, the message being text data or voice data.

[1066] Step 2:

[1067] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[1068] Step 3:

[1069] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[1070] Step 4:

[1071] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[1072] Step 5:

[1073] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[1074] Step 6:

[1075] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[1076] Step 7:

[1077] The user uses the terminal to select "what kind of impression they want to make," such as "I want to convey my gratitude."

[1078] Step 8:

[1079] The terminal transmits the user's selection to the server, where the selection information is recorded.

[1080] Step 9:

[1081] The server will then activate the AI ​​based on the selected impression to generate the best reply comment, for example, "Today was a good day, thank you for your concern!"

[1082] Step 10:

[1083] The server sends the generated reply comment to the terminal, which displays it to the user.

[1084] Step 11:

[1085] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[1086] Step 12:

[1087] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[1088] The above are the specific processing steps of the program of the present invention.

[1089] Example 1

[1090] 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."

[1091] Conventional communication tools have the problem that it is difficult to accurately understand the other person's feelings and impressions and generate appropriate replies. As a result, users often send misleading messages, making smooth communication difficult.

[1092] 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.

[1093] In this invention, the server includes: [means for analyzing data using emotion analysis AI, personality analysis AI, and impression determination AI on the server; [means for generating an optimal reply comment based on the impression selected using a generation AI model; and [means for presenting the generated reply comment to the user.] This makes it possible [to accurately analyze the other person's emotions and impressions, automatically generate an optimal reply comment based on that information, and present it to the user].

[1094] A "user" refers to an entity that uses a terminal to exchange messages with others.

[1095] "Text data" refers to digital data that consists of character information.

[1096] "Audio data" refers to digital data that records audio information.

[1097] "Terminal" refers to a computing device operated by a user (e.g., smartphone, tablet, PC).

[1098] "Server" refers to a computer system that analyzes data received from a user's terminal and generates a reply comment.

[1099] "Voice recognition technology" refers to technology that converts voice data into text data.

[1100] "Sentiment analysis AI" refers to artificial intelligence that determines the emotional tone (positive, negative, neutral, etc.) from text data.

[1101] "Personality analysis AI" refers to artificial intelligence that estimates the sender's personality traits from the content of text data.

[1102] "Impression judgment AI" refers to artificial intelligence that determines the impression a sender has of a receiver based on the results of emotional and personality analysis.

[1103] "Generative AI model" refers to artificial intelligence that generates optimal reply comments based on selected impressions.

[1104] A "reply comment" refers to a reply message that a user sends to another person.

[1105] The present invention is a system that analyzes the emotions and impressions of the other person in everyday communication and generates appropriate reply comments based on the analysis. This system functions in cooperation with a server, terminals, and users.

[1106] A user uses a device (such as a smartphone, tablet, or PC) to exchange text or voice messages with others. When the device receives a message, it sends the data to a server. In the case of voice data, the device's voice recognition technology (specifically, voice recognition software) is used to convert the voice data into text data. One example of the technology used in this case is a voice recognition API.

[1107] The server analyzes the received text data using various AI modules. Sentiment analysis AI, for example, uses natural language processing APIs to determine the emotional tone (e.g., positive, negative, neutral, etc.). Personality analysis AI estimates the sender's personality traits from the text content. Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trustworthiness, etc.) based on the results of these analyses.

[1108] The analysis results are sent from the server to the device, which then notifies the user. The user checks the displayed analysis results and selects the desired impression (e.g., they want to express gratitude). The selected impression information is sent back to the server, which then activates a generative AI model to generate an optimal reply comment. This generative AI model can utilize a generative language model API, for example.

[1109] The generated reply comment is sent from the server to the terminal, and the terminal displays it to the user. The user checks the displayed reply comment and, if appropriate, sends it as a message.

[1110] As a specific example, let us consider the case where user A receives the message "How was your day?".

[1111] 1. Data Entry

[1112] The terminal sends this message to the server.

[1113] 2. Data Analysis

[1114] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[1115] Sentiment analysis AI: Determines that the tone is neutral.

[1116] Personality analysis AI: Estimates whether the caller is interested.

[1117] Impression determination AI: Determines that the sender is concerned about User A's situation.

[1118] 3. Proposal Generation

[1119] The device notifies user A of the analysis results, and user A selects the impression "I want to express my gratitude."

[1120] The server launches the generative AI model and generates the optimal reply comment, such as "Today was a good day, thank you for your concern!". An example of a prompt sentence for the generative AI model is, "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'"

[1121] 4. Presenting the proposal

[1122] The server sends the generated reply comment to the terminal, which displays it to User A, who then confirms and sends it.

[1123] This system enables users to accurately understand the emotions and impressions of others and to communicate effectively and appropriately.

[1124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1125] Step 1:

[1126] A user receives a message through a text messaging application on a device, which provides input data as text or voice data. The device then detects the message and identifies its type (text or voice). If it is voice data, it uses voice recognition software to convert the voice into text data. The converted text data is provided as output.

[1127] Step 2:

[1128] The terminal transmits the obtained text data to the server. The input data is the text data received or converted by the terminal. The terminal transmits this data to the server using the communication module. The server receives the received data as input.

[1129] Step 3:

[1130] The text data received by the server is analyzed using an emotion analysis AI, personality analysis AI, and impression determination AI. The input data is the text data received by the server. The server activates the emotion analysis AI, analyzes the emotional tone of the text data (positive, negative, neutral, etc.), and obtains the results. Next, the personality analysis AI is used to analyze the sender's personality traits (e.g., extrovert, introvert, etc.). Finally, the impression determination AI analyzes the sender's impression (e.g., friendly, trustworthy, etc.) based on the results of the emotion analysis and personality analysis. The analysis results are obtained as output.

[1131] Step 4:

[1132] The server sends the analysis results to the terminal, and the terminal notifies the user of the results. The input data are the analysis results obtained by the server. The server sends these results to the terminal using a communication module. The terminal receives the received results as input and outputs them in a form that notifies the user. The terminal displays the analysis results via a user interface.

[1133] Step 5:

[1134] The user checks the analysis results on the device and selects the desired impression (e.g., expressing gratitude). The input data is the analysis results displayed on the device. The user decides on an option based on the analysis results and enters that selection information into the device. The selected impression is obtained as the output.

[1135] Step 6:

[1136] The terminal transmits the user's selection information to the server. The input data is the impression information selected by the user. The terminal transmits this information to the server via the communication module. The server receives the selection information as input.

[1137] Step 7:

[1138] The server uses a generative AI model to generate the optimal reply comment based on the selected impression information. The input data is the impression information selected by the user. The server starts the generative AI model and provides a prompt sentence as input data. Example prompt: "The user selected the impression 'I want to express my gratitude.' Please generate an appropriate reply comment for the following message: 'How was your day?'" The generative AI model outputs the optimal reply comment.

[1139] Step 8:

[1140] The server sends the generated reply comment to the terminal, which then displays it to the user. The input data is the reply comment generated by the generative AI model. The server sends this comment to the terminal via a communication module. The terminal receives the received reply comment as input and outputs it in a form that is displayed to the user. The user checks the displayed reply comment and sends it if it is appropriate.

[1141] (Application example 1)

[1142] 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."

[1143] In modern virtual stores, communication with customers is an important element, and it is necessary to quickly understand the customer's emotions and intentions and respond appropriately based on them. However, typical responses often rely on subjective judgment, which limits the ability to improve customer satisfaction. The purpose of this invention is to solve this problem and achieve smooth communication with customers by accurately analyzing customer emotions and generating appropriate responses.

[1144] 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.

[1145] In this invention, the server includes a means for receiving text data or voice data, a means for analyzing emotions, personality, and impressions of the received data, and a means for selecting the impression desired by the user, thereby enabling fast and accurate emotional analysis of customer comments and the generation of optimal reply comments.

[1146] "Audio data" is a data format for recording or transmitting audio.

[1147] "Text data" is a data format that includes characters and symbols.

[1148] "Sentiment analysis" is the process of determining sentiment from received data.

[1149] "Personality analysis" is the process of inferring the sender's personality traits from the content of the message.

[1150] "Impression determination" is a process of determining the impression that the sender has of the receiver based on the analysis results.

[1151] The "means for selecting the impression desired by the user" is a function that allows the user to select the impression that he or she wishes to give.

[1152] The "means for generating a reply comment" is a function for generating a reply comment based on the selected impression.

[1153] The "means for presenting the generated reply comment to the user" is a function for displaying the generated reply comment to the user.

[1154] "Speech recognition technology" is a technology that converts voice data into text data.

[1155] A "prompt" is a text instruction that is input to a generative AI model.

[1156] A "generative AI model" is an artificial intelligence model that generates natural language text based on a given prompt.

[1157] A "virtual store" is a store that exists in a virtual space on the Internet.

[1158] The present invention is a system for facilitating communication with customers in a virtual store. The system receives text data and voice data, analyzes them, and generates an optimal reply.

[1159] First, the user interacts with the customer using the device. When the customer speaks a question or request, the microphone in the device picks up the voice data. For example, a microphone built into smart glasses or a head-mounted display can serve this purpose. The voice data is then sent to the server in real time.

[1160] The voice data sent to the server is converted into text data using speech recognition technology. The speech_recognition library performs this process. After being converted into text data, it is analyzed by AI modules for sentiment analysis, personality analysis, and impression determination. The sentiment analysis module from the transformers library is used here.

[1161] Once the analysis is complete, the server provides the user with the option to select the impression they wish to give based on the results. This involves providing an interface where the user can select a particular impression (e.g., "trust").

[1162] Based on the selected impression, the server uses a generative AI model (e.g., GPT-3) to generate a reply comment, using the following prompt:

[1163] What customers say: What's your recommendation today?

[1164] Emotion: Interested

[1165] The impression you want to convey to your customers: Trust

[1166] Generate an appropriate reply.

[1167] The generated reply comment is sent to the terminal and displayed to the user, who then confirms it and sends it to the customer as a message.

[1168] This allows for a quick and appropriate response based on customer comments in the virtual store, improving customer satisfaction.

[1169] As a specific example, if a customer says, "What's today's recommendation?", the system converts the speech into text, determines the emotion as "interested," and if the user selects to give "trust," generates a reply saying, "This month's recommendation is a special pasta made with fresh sea urchin and caviar. We use ingredients carefully selected at the restaurant's discretion, so please give it a try." In this way, by implementing the invention, it becomes possible to communicate with customers smoothly and efficiently.

[1170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1171] Step 1:

[1172] The terminal captures what the customer says. If the customer utters voice data, the microphone in the terminal records the voice data. The captured voice data is sent to the server for analysis.

[1173] Input: Customer voice data

[1174] Output: The audio data is sent to the server.

[1175] Step 2:

[1176] The server receives the voice data and converts it into text data using speech recognition technology. It uses the speech_recognition library to analyze the voice data and generate text data.

[1177] Input: Audio data

[1178] Output: Converted text data

[1179] Step 3:

[1180] The server performs sentiment analysis, personality analysis, and impression determination on the text data. For sentiment analysis, the transformers library is used to classify the text data into emotional tones (e.g., positive, negative, neutral, etc.).

[1181] Input: Text data

[1182] Output: Emotion analysis results, personality analysis results, impression judgment results

[1183] Step 4:

[1184] The server notifies the terminal of the analysis results and displays an interface for the user to select "what impression they want to give." The user selects the impression they want from the presented options.

[1185] Input: Analysis results

[1186] Output: User-selected impressions

[1187] Step 5:

[1188] The server generates a reply comment based on the impression selected by the user, sends the prompt to a generative AI model (e.g., GPT-3), and receives an appropriate reply.

[1189] Input: User-selected impression, prompt statement

[1190] Output: Generated reply comment

[1191] Step 6:

[1192] The server transmits the generated reply comment to the terminal, which displays the generated reply comment to the user, who then confirms it.

[1193] Input: Generated reply comment

[1194] Output: Reply comment presented to the user

[1195] Step 7:

[1196] The user can review the suggested reply comment, adjust it if necessary, and then send it as a final message.

[1197] Input: User confirmation results, adjustment details

[1198] Output: Final message sent

[1199] The above are the specific processing steps for carrying out the present invention, which enable smooth communication with customers in a virtual store.

[1200] 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.

[1201] This invention is a system that analyzes the emotions and impressions of the other party in communication and advises on appropriate reply comments based on the results, and also includes an emotion engine that recognizes the user's emotions. This system mainly involves interactions between a server, a terminal, and a user.

[1202] 1. Data Entry

[1203] A user uses a device to exchange messages with others through a communication tool (e.g., a text messaging application). When the device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[1204] 2. Data Analysis

[1205] The server analyzes the received text data or converted text data using emotion analysis AI, personality analysis AI, and impression determination AI modules.

[1206] - Sentiment analysis AI determines the emotional tone (positive, negative, neutral, etc.) of a message.

[1207] - Personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, sensitive, etc.) from the content of the message.

[1208] - Impression determination AI determines the impression the sender has of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[1209] 3. User Emotion Recognition

[1210] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. The emotion engine detects the user's facial expressions, gestures, typing speed, etc. when viewing a message to analyze the user's emotional state at that time.

[1211] 4. Proposal Generation

[1212] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen. When the user selects the impression they want to give, the system also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, appropriate suggestions will be made.

[1213] 5. Generating optimal reply comments

[1214] Once the user selects the desired impression, the information is sent to the server, which then activates the AI ​​to generate the optimal reply comment based on the selected impression, such as "Today was a good day, thank you for caring!"

[1215] 6. Presenting the proposal

[1216] The server sends the generated reply comment to the terminal, which displays the content to the user, who then confirms the suggested reply comment.

[1217] The user selects a reply comment from the list, confirms it, and then sends it as a message.

[1218] Specific examples

[1219] For example, user A receives a message in a text messaging application that asks, "How was your day?"

[1220] 1. Data Entry

[1221] The terminal sends this message to the server.

[1222] 2. Data Analysis

[1223] The server analyzes the received text data, "How was your day?", using emotion analysis AI, personality analysis AI, and impression determination AI.

[1224] - Sentiment analysis AI: Determines that the tone is neutral.

[1225] - Personality analysis AI: Predicts whether the caller is interested.

[1226] - Impression determination AI: Determines whether the sender is concerned about User A's situation.

[1227] 3. User Emotion Recognition

[1228] The emotion engine analyzes user A's facial expressions and behavioral data obtained through the device's camera and sensors, and determines that user A is currently in a relaxed state.

[1229] 4. Proposal Generation

[1230] The device notifies the analysis result to User A, who selects the impression "I want to express my gratitude."

[1231] The server launches a generative AI to generate the optimal reply comment: "Today was a great day, thanks for caring!"

[1232] 5. Presenting the proposal

[1233] The server sends the generated reply comment to the terminal, which displays the reply comment to User A, who then confirms and sends it.

[1234] Through the above processing, the present invention enables appropriate communication that also takes into account the user's emotional state.

[1235] The processing flow will be explained below.

[1236] Step 1:

[1237] The terminal receives a message from a user, the message being text data or voice data.

[1238] Step 2:

[1239] The terminal sends the received message to the server. If it is voice data, it is converted into text data using voice recognition technology, and the text data is sent to the server.

[1240] Step 3:

[1241] The server passes the received text data to an emotion analysis AI to analyze the emotional tone, which is determined as positive, negative, or neutral.

[1242] Step 4:

[1243] The server passes the text data to a personality analysis AI that analyzes the sender's personality traits, such as extroversion, introversion, or high sensitivity.

[1244] Step 5:

[1245] Based on the analyzed emotional tone and personality traits, the server uses impression-judging AI to determine the impression the sender has of the recipient. This impression can be expressed as friendliness, trust, respect, etc.

[1246] Step 6:

[1247] The device receives the analysis results from the server and notifies them to the user, who can then check the results on the screen.

[1248] Step 7:

[1249] The emotion engine is activated and recognizes the user's current emotional state. It collects data such as the user's facial expressions and input speed through the device's camera and sensors, and analyzes the user's emotions based on that data.

[1250] Step 8:

[1251] The user uses the device to select "what impression they want to make," such as "I want to express my gratitude." This selection also takes into account the user's current emotional state.

[1252] Step 9:

[1253] The terminal transmits the user's selection to the server, where the selection information and the user's emotional state data are recorded.

[1254] Step 10:

[1255] The server activates the AI ​​based on the selected impression and the user's emotional state data to generate an optimal reply comment, such as "Today was a good day, thank you for your concern!"

[1256] Step 11:

[1257] The server sends the generated reply comment to the terminal, which displays it to the user.

[1258] Step 12:

[1259] The user checks the reply comment provided and corrects it if necessary. Finally, the user submits the confirmed reply comment.

[1260] Step 13:

[1261] The device sends the reply comment that the user has confirmed as a message, thus completing the communication.

[1262] The above are the specific processing steps of the program of the present invention.

[1263] Example 2

[1264] 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."

[1265] In communication, it is difficult to accurately analyze the other person's emotions and impressions and generate appropriate reply comments that take the user's emotional state into consideration. Furthermore, conventional systems cannot reflect the user's real-time emotional state, so they cannot provide advice that matches the user's emotions.

[1266] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to receive message data using a terminal and transmit the data to the server; means for the server to analyze the received data using modules for emotion analysis, personality analysis, and impression determination; means for the terminal to sense real-time data such as the user's facial expression and input speed and recognize the user's emotional state; means for the user to select a desired impression and transmit the information to the server; means for the server to activate a generation AI to generate an optimal reply comment based on the selected impression; and means for the terminal to present the generated reply comment to the user. This makes it possible to generate an appropriate reply comment that reflects the user's emotional state.

[1267] "User" refers to an entity that uses the system to receive and reply to messages.

[1268] "Device" refers to a device, including a camera and sensors, used by a user to send and receive messages and to sense the user's emotional state.

[1269] "Server" refers to a computer system that analyzes data sent from a terminal and performs processing to generate appropriate suggestions and reply comments.

[1270] "Message data" refers to text data or voice data that a user receives or sends.

[1271] "Sentiment analysis" refers to the process of determining the emotional tone (positive, negative, neutral, etc.) of message data.

[1272] "Personality analysis" refers to the process of inferring the sender's personality traits from the content of message data.

[1273] "Impression determination" refers to the process of determining the impression (e.g., friendliness, trust, respect, etc.) that the sender has of the receiver based on the analysis results.

[1274] An "emotion engine" refers to a system that analyzes real-time behavioral and biometric data obtained from a user's device and recognizes the user's emotions.

[1275] "Generative AI" refers to an artificial intelligence model that generates optimal reply comments based on the impression the user wants to make.

[1276] A "reply comment" refers to a message created by a user to be sent to another party.

[1277] The present invention is a system that allows interaction between a user, a terminal, and a server, analyzes the emotions and impressions of the other party in communication, and based on that analyzes the emotions and impressions, advises on appropriate reply comments. This system includes an emotion engine and can also analyze the user's emotional state.

[1278] First, a user uses a device to exchange messages with others using a communication tool (e.g., a text messaging application). The received message data (text data or voice data) is sent from the device to a server. In the case of voice data, it is converted into text data using voice recognition technology before being sent to the server. General voice recognition software is used for voice recognition.

[1279] To analyze the received text data, the server uses modules called emotion analysis AI, personality analysis AI, and impression determination AI. The emotion analysis AI determines the emotional tone of the message (positive, negative, neutral, etc.), and the personality analysis AI infers the sender's personality traits from the message content. Based on these analysis results, the impression determination AI determines the impression the sender has of the recipient (e.g., friendliness, trust, respect, etc.). This allows for the accumulation of detailed data on emotions, personality, and impressions.

[1280] The device is also equipped with an emotion engine that recognizes the user's emotional state through real-time behavioral data (e.g., facial expressions, gestures, typing speed, etc.). This is achieved using hardware such as cameras and sensors. The emotion engine analyzes this input data and determines the user's emotional state when viewing a message.

[1281] The analysis results are sent to the device. The user checks the analysis results on the screen and selects "what kind of impression they want to convey." For example, if the user selects the impression "I want to express my gratitude," the server will activate the generation AI and generate the optimal reply comment. The generation AI used here generates a reply comment based on the impression selected by the user.

[1282] The generated reply comment is sent from the server to the terminal and presented to the user. The user can then confirm the suggested reply comment and send it as a message. This enables appropriate communication that takes into account the user's emotional state.

[1283] Prompt Sentence Examples

[1284] "Generate the best reply comment if the user selects the impression 'I want to express my gratitude.' Example: 'Today was a good day, thanks for caring!'"

[1285] By implementing this system, it will be possible to generate and display reply comments that are appropriate for the user's emotional state, which was difficult with conventional systems. This system integrates multiple AI modules, such as emotion analysis AI, personality analysis AI, impression determination AI, and generation AI, to enhance communication that takes the user's emotional state into consideration.

[1286] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1287] Step 1:

[1288] A user receives a message on their device. The user opens a text messaging application and views a message from another person. For example, they receive a message saying, "How was your day?" The input is the received message data, and the output is the device sending this data to the server. Specifically, the user checks the message on their smartphone, and the device sends the text data to the server.

[1289] Step 2:

[1290] The server analyzes the received message data. The server then analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The input is the received text data, and the output is the analysis results of each AI module. Specifically, the server analyzes the data, the emotion analysis AI determines the tone of the message, the personality analysis AI estimates the sender's personality traits, and the impression determination AI determines the impression.

[1291] Step 3:

[1292] The device recognizes the user's emotions. The device's emotion engine acquires data such as the user's facial expressions, gestures, and input speed through cameras and sensors. The input is the user's real-time behavioral data, and the output is the user's emotional state as recognized by the emotion engine. Specifically, the device's camera captures a picture of the user's face, and the sensor detects the user's input speed. The emotion engine analyzes this data and determines the user's emotional state, such as whether they are relaxed.

[1293] Step 4:

[1294] The server generates a suggestion based on the analysis results. The user selects the desired impression, such as "I want to express my gratitude," and this information is sent to the server. The server then activates the generation AI based on the selected impression and the user's emotional state, and generates the optimal reply comment. The input is the user's selected impression information and emotional state, as well as the analyzed data, and the output is the generated reply comment. In concrete terms, the server receives the information "I want to express my gratitude," and the generation AI generates the comment "Today was a good day, thank you for thinking about me!"

[1295] Step 5:

[1296] The terminal presents the generated reply comment to the user. The terminal receives the reply comment sent from the server and notifies the user. The input is the generated reply comment, and the output is the notification to the user. Specifically, the terminal displays "Suggested reply comment: Today was a good day, thanks for your concern!" to the user, and the user confirms it.

[1297] Step 6:

[1298] The user submits a reply comment. The user reviews the proposed reply comment, corrects it if necessary, and then submits it. The input is the content of the generated reply comment, and the output is the message sent to the other person. In concrete terms, the user confirms the comment "Today was a good day, thanks for thinking about me!" and submits the message.

[1299] Through the above processing steps, the system can generate optimal reply comments that reflect the user's emotional state.

[1300] (Application example 2)

[1301] 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."

[1302] Conventional systems that generate reply comments appropriate to each user's emotional state have the problem of being unable to consider the user's own emotional state or the appropriate content at that time. Furthermore, few systems have the functionality to analyze the user's emotional state in real time and recommend optimal content based on that information. As a result, there is a risk that the user experience will be impaired and satisfaction with communication and content viewing will decrease.

[1303] The identification process by the identification 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 text data or voice data, means for performing emotion analysis, personality analysis, and impression determination on the received data, means for selecting the impression desired by the user, means for generating an optimal reply comment based on the selected impression, means for presenting the generated reply comment to the user, means for analyzing the user's emotional state in real time, and means for recommending content based on the user's emotional state. This makes it possible to generate an optimal reply comment taking into account the user's real-time emotional state and to recommend content that suits the user's current emotion.

[1304] "Text data" is character string information expressed electronically.

[1305] "Audio data" is information that represents audio in digital form.

[1306] "Sentiment analysis" is a technique for determining emotional tone (positive, negative, neutral, etc.) from text or audio data.

[1307] "Personality analysis" is a technology that estimates a caller's personality traits (e.g., extroversion, introversion, high sensitivity, etc.) from text or voice data.

[1308] "Impression assessment" is a technology that determines the impression (e.g., friendliness, trust, respect, etc.) that a sender has of a receiver based on the results of emotional and personality analysis.

[1309] "User" means an individual or corporation that uses the system.

[1310] A "reply comment" is a comment to reply to a received message, and is suggested by the generation AI.

[1311] "Emotional state" refers to a user's current emotional state, as measured in real time.

[1312] "Content" refers to information or media that can be viewed or used (e.g., movies, dramas, anime, music, articles, etc.).

[1313] "Content recommendation" is the process of automatically suggesting content that is appropriate for a user's emotional state.

[1314] The present invention is a system that analyzes a user's emotional state in real time and recommends optimal reply comments and content based on that information. This system mainly involves interactions between a server, a terminal, and a user.

[1315] Users use devices such as smartphones to exchange messages with others through communication tools (e.g., text messaging applications). When a device receives a message, it sends the text or voice data to a server. In the case of voice data, it is first converted into text data using voice recognition technology, and then the text data is sent to the server.

[1316] The server analyzes the received text data or converted text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the message, and the personality analysis AI infers the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the message. The impression determination AI determines the sender's impression of the recipient (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[1317] The emotion engine analyzes additional input data (such as the user's real-time behavioral data and biometric data) obtained through the device to recognize the user's emotions. This emotion engine detects the user's facial expressions, gestures, and typing speed when viewing a message, and analyzes the user's emotional state at that time. For example, the device's camera and sensor module can be used to acquire the user's facial expressions and behavioral data, and the analysis can be performed in real time.

[1318] The server sends the analysis results to the device, which then notifies the user. The user can check the analysis results on the screen and select the impression they want to convey. The server also takes into account the user's current emotional state as estimated by the emotion engine. For example, if the user is tired or stressed, the server will suggest a reply comment that is appropriate for that situation.

[1319] Once the user selects the desired impression, the information is sent to the server, which then activates the generative AI model to generate the optimal reply comment based on the selected impression. For example, a comment like "Today was a good day, thank you for your concern!" may be generated. The server then sends the generated reply comment to the device, which then displays it to the user. The user can then confirm the presented reply comment and send it as a message.

[1320] Furthermore, the engine recommends content that matches the user's current mood based on their emotional state. This content recommendation engine selects content according to the user's emotional state, such as recommending relaxing music or videos when the user is relaxing.

[1321] As a concrete example, if User A receives a message in a text messaging application using the message "What do you think of this anime?", and User A is in a relaxed state, the system will select the impression that the user wants to express gratitude and generate a reply such as "Thank you for watching this anime!". In addition, if User A is tired, content that will help them relax will be recommended.

[1322] An example prompt is:

[1323] "Perform sentiment and personality analysis on the comment 'What do you think of this anime?' and generate and display the optimal reply if the user chooses to express gratitude. At the same time, if the user is tired, recommend content that will help them relax."

[1324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1325] Step 1: Data entry

[1326] A user receives a comment via a text messaging application. The device captures the comment as text data or voice data, and if the voice data is voice data, converts it to text data using voice recognition technology. The device then sends the converted text data to a server.

[1327] Input: Received text or voice data

[1328] Output: Text data sent to the server

[1329] Step 2: Data analysis

[1330] The server analyzes the received text data using the emotion analysis AI, personality analysis AI, and impression determination AI modules. The emotion analysis AI determines the emotional tone (positive, negative, neutral, etc.) from the text data, and the personality analysis AI estimates the sender's personality traits (e.g., extrovert, introvert, high sensitivity, etc.) from the content of the text data. Finally, the impression determination AI determines the sender's impression of the receiver (e.g., friendliness, trust, respect, etc.) based on the analysis results.

[1331] Input: Text data

[1332] Output: Emotional tone, personality traits, impression judgment results

[1333] Step 3: Recognizing user emotions

[1334] The device uses a camera and sensor module to capture the user's facial expressions and behavioral data in real time, and sends this data to an emotion engine to analyze the user's current emotional state (e.g., relaxed, tired, stressed, etc.).

[1335] Input: User's facial expression data and behavior data

[1336] Output: Current emotional state

[1337] Step 4: Generate proposals

[1338] The server sends the analysis results to the device, which then notifies the user. The user checks the analysis results on the screen and selects the impression they want to convey. The user's current emotional state estimated by the emotion engine is also taken into consideration. Based on the selected impression and emotional state, the server activates a generative AI model to generate the optimal reply comment.

[1339] Input: Analysis results, selected impression, current emotional state

[1340] Output: Generated reply comment

[1341] Step 5: Present your proposal

[1342] The server sends the generated reply comment to the terminal, which displays the content to the user. The user checks the presented reply comment, makes corrections if necessary, and then sends it as a message.

[1343] Input: Generated reply comment

[1344] Output: Reply comments displayed to the user, message sent

[1345] Step 6: Content Recommendation

[1346] Based on the user's emotional state, the server activates a content recommendation engine to select content that suits the user's current mood (e.g., relaxing music or videos). The selected content information is sent to the device and displayed to the user.

[1347] Input: The user's current emotional state

[1348] Output: Recommended content information

[1349] Through the above processing steps, the system of the present invention is able to generate optimal reply comments that take into account the user's real-time emotional state and recommend content that suits the user's current emotions.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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).

[1357] 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.

[1358] 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."

[1359] 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.

[1360] 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).

[1361] 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.

[1362] 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.

[1363] 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.

[1364] 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.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] The following is further disclosed regarding the above embodiment.

[1372] (Claim 1)

[1373] [Means for receiving text data or audio data;

[1374] [Means for analyzing emotions, personality, and determining impressions from received data;

[1375] [means for the user to select the impression they desire;

[1376] [Means for generating an optimal reply comment based on the selected impression;

[1377] [A system including a means for presenting the generated reply comment to a user.

[1378] (Claim 2)

[1379] The system of claim 1, further comprising: means for converting audio data into text data.

[1380] (Claim 3)

[1381] The system of claim 1, further comprising: means for sending the generated reply comment as a message.

[1382] "Example 1"

[1383] (Claim 1)

[1384] [Means for receiving text data or voice data from a user;

[1385] [Means for transmitting the received data to a server;

[1386] [Means for converting voice data into text data using voice recognition technology;

[1387] [Means for analyzing emotions, personality, and determining impressions from data received by the server;

[1388] [Means for notifying the terminal of the analysis results and displaying them to the user;

[1389] [means for the user to select the impression they desire;

[1390] [Means for generating an optimal reply comment in a server based on the selected impression;

[1391] [A system including a means for presenting the generated reply comment to a user.

[1392] (Claim 2)

[1393] [The system according to claim 1, wherein the server analyzes data using emotion analysis AI, personality analysis AI, and impression determination AI.

[1394] (Claim 3)

[1395] [The system of claim 1 generates an optimal reply comment based on the selected impression using a generative AI model.

[1396] "Application Example 1"

[1397] (Claim 1)

[1398] [Means for receiving text data or audio data;

[1399] [Means for analyzing emotions, personality, and determining impressions from received data;

[1400] [means for the user to select the impression they desire;

[1401] [Means for generating an optimal reply comment based on the selected impression;

[1402] [means for presenting the generated reply comment to the user;

[1403] [Means for converting voice data into text data using voice recognition technology;

[1404] [A system including means for generating a prompt sentence, sending it to a generative AI model, and generating a reply comment based on the prompt sentence.

[1405] (Claim 2)

[1406] [The system of claim 1, which converts voice data into text data.

[1407] (Claim 3)

[1408] [The system according to claim 1, wherein the generated reply comment is sent as a message.

[1409] "Example 2: Combining Emotion Engines"

[1410] (Claim 1)

[1411] [Means for a user to receive message data using a terminal and transmit the data to a server;

[1412] [Means for analyzing the data received by the server using modules for emotion analysis, personality analysis, and impression determination;

[1413] [Means for the device to sense real-time data such as the user's facial expressions and input speed and recognize the user's emotional state;

[1414] [Means for the user to select the desired impression and transmit that information to the server;

[1415] [Means for the server to generate an optimal reply comment by activating a generation AI based on the selected impression;

[1416] [A system including a means for the terminal to present the generated reply comment to the user.

[1417] (Claim 2)

[1418] The system of claim 1, further comprising: means for converting audio data into text data.

[1419] (Claim 3)

[1420] The system of claim 1, further comprising: means for sending the generated reply comment as a message.

[1421] "Application example 2 when combining emotion engines"

[1422] (Claim 1)

[1423] [Means for receiving text data or audio data;

[1424] [Means for analyzing emotions, personality, and determining impressions from received data;

[1425] [means for the user to select the impression they desire;

[1426] [Means for generating an optimal reply comment based on the selected impression;

[1427] [means for presenting the generated reply comment to the user;

[1428] [Means for analyzing the user's emotional state in real time;

[1429] [Means to recommend content based on the user's emotional state]

[1430] A system including:

[1431] (Claim 2)

[1432] The system of claim 1, further comprising: means for converting audio data into text data.

[1433] (Claim 3)

[1434] The system of claim 1, further comprising: means for sending the generated reply comment as a message. [Explanation of symbols]

[1435] 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 text data or audio data; A means for analyzing emotions, personality, and determining impressions from the received data; means for the user to select a desired impression; A means for generating an optimal reply comment based on the selected impression; The system includes a means for presenting the generated reply comment to a user.

2. 10. The system of claim 1, further comprising means for converting audio data to text data.

3. The system of claim 1 further comprising means for sending the generated reply comment as a message.

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