system

Abstract

We provide the system. [Solution] A system including means for translating, correcting, and storing expressions using generative AI, means for utilizing the generative AI as an intermediary, and means for providing the expressions generated by the generative AI to the user.

Description

Technical Field

[0006] ,

[0004] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional communication tools have a problem that it is difficult to accurately convey the emotional state and intention of a user, and as a result, misunderstandings and frictions cannot be prevented.

Means for Solving the Problems

[0005] This invention can accurately convey the emotional state and intention of a user and prevent misunderstandings and frictions by generating an expression based on an input from the user using generative AI, translating, correcting, and storing the expression. Furthermore, the generative AI can generate an expression considering the emotional state of the user to achieve more appropriate communication.

Brief Description of the Drawings

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16]This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] This is a sequence diagram showing the processing flow of the data processing system in Example 1 of the Form 1 when an emotion engine is combined. [Figure 18] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] This is a sequence diagram showing the processing flow of a data processing system in another embodiment. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0008] First, let's explain the terminology used in the following explanation.

[0009] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (TENSOR PROCESSING UNIT®).

[0010] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0011] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0012] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0014] [First Embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0019] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] One embodiment of the present invention is a system that translates, corrects, and stores expressions using generative AI. This system receives input from a user and uses generative AI to generate an expression based on that input. The generated expression is translated, corrected, and stored, and the results are provided to the user. For example, if a user inputs "I am angry" in English, the generative AI receives this input and translates it into the Japanese expression "I am angry." The generative AI can also correct this expression, adjusting the degree of emotion, for example, to "I am a little angry." Furthermore, the generative AI can store this expression for later reference.

[0029] "Example of form 2"

[0030] Another embodiment of the present invention involves a system in which a generative AI generates expressions while considering the user's emotional state. In this system, the generative AI analyzes the user's emotional state and generates expressions based on the results. For example, if a user provides input indicating anger, the generative AI can consider that emotional state and generate expressions that will help the user calm down. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0031] The processing flows of each exemplary embodiment will be described below.

[0032] "Exemplary Embodiment 1"

[0033] Step 1: The system receives an input from the user. For example, the user inputs "I am angry" in English.

[0034] Step 2: The generative AI generates an expression based on the input from the user. In this example, it translates "I am angry" into a Japanese expression "私は怒っています".

[0035] Step 3: The generative AI corrects the generated expression. For example, it adjusts the degree of emotion like changing "私は怒っています" to "私は少し怒っています".

[0036] Step 4: The generative AI stores the generated and corrected expression. This expression can be referred to later.

[0037] Step 5: The system provides the expression generated by the generative AI to the user.

[0038] "Exemplary Embodiment 2"

[0039] Step 1: The system receives an input from the user. This input indicates the user's emotional state.

[0040] Step 2: The generative AI analyzes the user's emotional state.

[0041] Step 3: The generative AI generates an expression based on the analysis result. For example, when the user inputs an expression indicating anger, the generative AI generates an expression that helps the user calm down considering the emotional state.

[0042] Step 4: The system provides the user with an expression generated by a generative AI. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0043] (Example 1)

[0044] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0045] In today's information society, users are required to communicate smoothly between different languages. However, conventional translation systems have difficulty accurately conveying emotional nuances and are insufficient in generating expressions that take into account the user's emotional state. Furthermore, there is a lack of systems that can efficiently store and refer to the generated expressions at a later date.

[0046] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0047] In this invention, the server includes means for receiving input from a user, means for pre-processing said input, and means for generating expressions using a generative AI model. This enables the generation of natural expressions that take into account the user's emotional state, as well as translation, correction, and storage.

[0048] A "user" is an entity that provides input to a system and receives the generated representation.

[0049] "Input" refers to the text data and information that a user provides to the system.

[0050] "Preprocessing" is the process of shaping or transforming data so that the generative AI model can process the input appropriately.

[0051] A "generative AI model" is an algorithm or system that performs natural language processing and generates new expressions based on the input.

[0052] "Representation" refers to the format of text or information generated by a generative AI model.

[0053] Translation is the process of converting a generated expression into a different language.

[0054] "Correction" is the process of modifying the content of a generated expression and adjusting it to a more appropriate form.

[0055] "Storage" refers to the process of saving generated expressions to a database or similar system so that they can be referenced later.

[0056] A "server" is a computer system that receives input from users, generates expressions using a generative AI model, and then translates, corrects, and stores them.

[0057] One embodiment of this invention begins with a user inputting text into the system via a terminal. The user inputs an English sentence, for example, "I am angry." The terminal sends this input to the server.

[0058] The server preprocesses the received input data. Preprocessing involves formatting the data and removing unnecessary characters so that the generative AI model can process it appropriately. Once preprocessed, the data is input to the generative AI model. This generative AI model is an algorithm for natural language processing, such as OpenAI's GPT-4®.

[0059] The generative AI model generates new expressions based on the input. For example, for the input "I am angry", it generates the Japanese expression "私は怒っています". The generated expression is further translated and corrected as necessary. In the correction process, the degree of emotion can be adjusted, for example, it is corrected to "私は少し怒っています".

[0060] Finally, the server stores the generated expression in the database. This stored data is for the user to refer to later. The server sends the translated and corrected expression to the terminal, and the user can check the result through the terminal.

[0061] As a specific example, when the user inputs "I am angry", the system translates it to "私は怒っています" and further corrects it to "私は少し怒っています". This result is provided to the user and stored in the database.

[0062] An example of a prompt sentence is "Please translate the following English sentence into Japanese and adjust the degree of emotion: I am angry".

[0063] The flow of the specific process in Example 1 will be described using FIG. 11.

[0064] Step 1:

[0065] The user uses the terminal to input text into the system. For example, the user inputs an English sentence such as "I am angry". The terminal sends this input to the server. The input is text data expressing the user's emotion.

[0066] Step 2:

[0067] The server preprocesses the input data received from the terminal. In the preprocessing, the data is formatted and unnecessary characters are removed so that the generative AI model can process it appropriately. The input is raw data from the user, and the output is formatted text data.

[0068] Step 3:

[0069] The server inputs the pre - processed data into the generative AI model. This model performs natural language processing and generates new expressions based on the input. For example, for the input "I am angry", it generates the Japanese expression "私は怒っています". The input is the formatted text data, and the output is the generated expression.

[0070] Step 4:

[0071] The server translates the generated expression. Using the generative AI model, it converts the generated expression into a different language. The input is the generated expression, and the output is the translated expression.

[0072] Step 5:

[0073] The server corrects the translated expression. Using the generative AI model, it modifies the expression to adjust the degree of emotion. For example, it modifies "私は怒っています" to "私は少し怒っています". The input is the translated expression, and the output is the corrected expression.

[0074] Step 6:

[0075] The server stores the corrected expression in the database. The stored data is for the user to refer to later. The input is the corrected expression, and the output is the data stored in the database.

[0076] Step 7:

[0077] The server sends the final expression to the terminal. The user can view the translated and corrected results through the terminal. The input is the corrected expression, and the output is the result provided to the user.

[0078] (Application Example 1)

[0079] Next, we will describe Application Example 1 of Form 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."

[0080] In today's information and communication environment, user-generated content is often multilingual and expresses diverse emotions. Therefore, it is necessary to appropriately translate this content, adjust the emotional intensity, and deliver it to other users. However, conventional systems struggle with real-time translation and emotional adjustment, hindering improvements in the user experience. Solving this problem is crucial.

[0081] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0082] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for translating user input in real time and adjusting the degree of emotion; and means for distributing the adjusted expressions to other users. This makes it possible to appropriately translate user-generated content into multiple languages, adjust the degree of emotion, and distribute it to other users in real time.

[0083] "Generative AI" is an artificial intelligence technology that generates expressions based on user input and performs translation, correction, and adjustment of emotional intensity.

[0084] "Translation" is the process of converting content expressed in one language into another language.

[0085] "Correction" is the process of correcting errors in generated expressions to make them accurate.

[0086] "Storage" refers to the process of recording generated expressions so that they can be referenced later.

[0087] "Real-time" refers to processing user input immediately and providing results instantly.

[0088] "Emotional intensity" is an indicator that shows the strength and type of emotion contained in the user's expression.

[0089] "Distribution" refers to the process of sending and sharing a generated expression with other users.

[0090] A "user" is an entity that uses the system to generate content and perform tasks such as translation and sentiment adjustment.

[0091] A "user" is the entity that receives the distributed content.

[0092] The system for implementing this invention has the functionality to translate, correct, and store user input using generative AI, and further adjust the degree of emotion before distributing it to other users. The server utilizes a generative AI model to receive user input and perform translation and emotion adjustment in real time. Specifically, the server receives user input as text data and sends prompts to the generative AI model. An example of a generative AI model used is OpenAI's GPT-3 (registered trademark).

[0093] The server receives the translation and sentiment adjustment results returned from the generative AI model and stores them in a database. This stored data is referenced when distributing to other users. The terminal's role is to send the user's input to the server, receive the response from the server, and display it.

[0094] For example, if a user enters "I am very happy," the server sends the prompt "Translate and adjust the sentiment of the following text: 'I am very happy'" to the generative AI model. The generative AI model translates this to "I am very happy" and adjusts the degree of emotion to "I am happy." This result is stored by the server and distributed to other users.

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The user enters text using a terminal. The entered text is sent from the terminal to the server. The input data is natural language text that includes the user's emotions and intentions.

[0098] Step 2:

[0099] The server generates a prompt message to send the received text to the AI ​​model. This prompt message instructs the model on how to process the input text. For example, it might be in the format, "Translate and adjust the sentiment of the following text: 'I am very happy'".

[0100] Step 3:

[0101] The server sends the generated prompt text to the generative AI model. The generative AI model translates the input text based on the prompt text and adjusts the degree of emotion. The input is the prompt text, and the output is the translated text and the adjusted emotion expression.

[0102] Step 4:

[0103] The server receives the translation and sentiment adjustment results returned by the generative AI model. The server stores these results in a database. The stored data is available for later reference and ready to be distributed to other users.

[0104] Step 5:

[0105] The server distributes stored translation and sentiment-adjusted results to other users. The terminal receives the response from the server and displays it to the user. This allows users to receive sentiment-adjusted content from other users in real time.

[0106] (Example 2)

[0107] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0108] Conventional systems using generative artificial intelligence have not adequately considered the user's emotional state when generating expressions, making it difficult to provide appropriate communication to the user. Furthermore, the instructions for generating expressions that respond to emotions are insufficient, making it impossible to provide responses that match the user's feelings.

[0109] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0110] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for analyzing the user's emotional state; and means for giving instructions to the generative artificial intelligence based on the analysis results. This makes it possible to generate appropriate expressions according to the user's emotional state and provide better communication to the user.

[0111] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate expressions based on user input, and then translate, correct, and store them.

[0112] An "intermediary" is an entity that utilizes generative artificial intelligence to facilitate the smooth exchange of information between users and other systems or services.

[0113] A "user" is an entity that operates a system using generative artificial intelligence, inputs information, and receives the generated expression.

[0114] "Emotional state" refers to the type and intensity of emotions analyzed from the information entered by the user.

[0115] "Analysis" is the process of analyzing user input information to identify specific emotional states.

[0116] An "instruction" is a command or guideline given to a generative artificial intelligence system based on the analysis results, for generating a specific expression.

[0117] "Expression" refers to means of communication, such as text and speech, that generative artificial intelligence generates based on user input and emotional states.

[0118] A description of embodiments for carrying out this invention will be given.

[0119] The server provides a system that uses generative artificial intelligence to generate expressions that correspond to the user's emotional state. This system receives input from the user and uses sentiment analysis software to analyze that emotional state. Specific software examples include a "natural language understanding API" and a "sentiment analysis API." This software analyzes the user's input text and identifies their emotional state.

[0120] The server generates prompt statements and provides instructions to the generative artificial intelligence based on the analyzed emotional state. These prompt statements instruct the generative AI on what kind of expressions it should generate. For example, if the user is expressing anger, the server will generate a prompt statement such as, "If the user is angry, please generate expressions that will help them calm down."

[0121] Generative artificial intelligence generates appropriate expressions based on prompt text. Specifically, it uses a generative AI model (e.g., a natural language generation model) to generate text that corresponds to the user's emotional state. This generated expression is then sent to the user via the device to provide appropriate communication.

[0122] For example, if a user inputs "Why is it so slow!", the server uses emotion analysis software to identify the emotion as "anger." Then, it has a generative artificial intelligence system generate a calming message such as, "We apologize for the delay. We are doing our best to resolve the issue, so please wait a moment," and sends it to the user. In this way, it is possible to provide an appropriate response tailored to the user's emotional state.

[0123] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0124] Step 1:

[0125] The user enters text through the terminal. For example, they might type the message, "Why is it so slow!" This input data is then sent to the server.

[0126] Step 2:

[0127] The server passes the received text data to sentiment analysis software. Specifically, it uses natural language processing techniques to analyze the text and identify the emotional state. The input is the user's text data, and the output is an emotional state such as "anger."

[0128] Step 3:

[0129] The server generates prompt sentences to input into the generative AI model based on the analyzed emotional state. For example, it might create a prompt sentence such as, "If the user is angry, generate a calming expression." This prompt sentence becomes the input to the generative AI model.

[0130] Step 4:

[0131] The server sends a prompt to the generative AI model, which generates an expression that corresponds to the user's emotional state. The generative AI model then generates an appropriate response based on the prompt. The input is a prompt, and the output is an expression such as, "We apologize for the wait. We are doing our best to resolve the issue, so please wait a moment."

[0132] Step 5:

[0133] The server sends the generated expression to the terminal and displays it to the user. The user can then see the generated response on the terminal and receive appropriate communication that matches their emotions.

[0134] (Application Example 2)

[0135] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0136] In modern information and communication technology, there is a demand for appropriate communication that takes into account the emotional state of the user. However, conventional systems have struggled to accurately analyze the user's emotions and generate appropriate responses based on them. As a result, they have failed to alleviate the user's anxiety or anger, sometimes leading to decreased satisfaction.

[0137] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0138] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for utilizing generative artificial intelligence as an intermediary; means for providing expressions generated by generative artificial intelligence to the user; means for analyzing the user's emotional state; and means for generating an appropriate response based on the emotional state. This enables appropriate communication that is in line with the user's emotions.

[0139] "Generative artificial intelligence" is an artificial intelligence technology that can generate expressions based on user input, and then translate, correct, and store them.

[0140] A "mediator" is an entity in which generative artificial intelligence plays a role in facilitating the exchange of information between the user and the system.

[0141] "User" refers to an individual or group that uses the system and is the entity that receives the expressions provided by the generative artificial intelligence.

[0142] "Emotional state" refers to the psychological state or emotions that a user exhibits in a specific situation, and is the subject of analysis by the system.

[0143] A "response" is an expression generated by a generative artificial intelligence system based on the user's emotional state, and serves as a means of communication with the user.

[0144] The system for carrying out this invention operates in a network environment including a server and terminals. The server processes user input using generative artificial intelligence and generates an appropriate response. Specifically, the server uses a sentiment analysis API (e.g., Google® Cloud Natural Language API) to analyze the emotional state from the user's input text. Based on this analysis, a generative artificial intelligence model (e.g., OpenAI's GPT model) generates a response that corresponds to the user's emotions.

[0145] The terminal provides an interface for user input and displays responses from the server. When a user enters text into the terminal, that data is sent to the server for sentiment analysis and response generation. The generated response is then sent back to the terminal and displayed to the user.

[0146] For example, if a user enters "I'm worried about the recent security breach," the server uses a sentiment analysis API to determine that the user is "anxious." Based on this emotional state, a generative artificial intelligence model generates a response such as, "Please rest assured that our team monitors the system 24 / 7 and we have implemented the latest security measures." An example of a prompt would be, "The user is feeling anxious. Generate a reassuring response."

[0147] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0148] Step 1:

[0149] The user enters text into the device. The entered text is sent to the server as data to analyze the user's emotional state.

[0150] Step 2:

[0151] The server sends the received text to a sentiment analysis API. The sentiment analysis API analyzes the content of the text and identifies the user's emotional state (e.g., anxiety, anger, joy, etc.). The analysis results are returned to the server.

[0152] Step 3:

[0153] The server receives the analysis results from the sentiment analysis API and creates a prompt message for the generative AI model. The prompt message contains instructions for generating an appropriate response based on the user's emotional state. For example, it might generate a prompt message such as, "The user is feeling anxious. Generate a reassuring response."

[0154] Step 4:

[0155] The server sends a prompt to the generative AI model, instructing it to generate a response that corresponds to the user's emotional state. The generative AI model generates an appropriate response based on the prompt and returns the result to the server.

[0156] Step 5:

[0157] The server sends the response received from the generated AI model to the terminal. The terminal displays this response to the user. The user can review the generated response and provide further input as needed.

[0158] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0159] "Example of form 1"

[0160] One embodiment of the present invention provides a system that combines an emotion engine. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI stores the generated and corrected expressions and provides them to the user.

[0161] "Example of form 2"

[0162] Another embodiment of the present invention provides a system that combines an emotion engine and a generative AI. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI provides the generated and corrected expression to the user. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0163] The following describes the processing flow for each example of the form.

[0164] "Example of form 1"

[0165] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[0166] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[0167] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[0168] Step 4: Store the expressions generated and corrected by the generative AI, and provide those expressions to the user.

[0169] "Example of form 2"

[0170] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[0171] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[0172] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[0173] Step 4: The generative AI provides the user with the generated and corrected expressions. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0174] (Example 1)

[0175] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0176] Conventional information processing systems struggled to accurately translate users' natural language input and generate expressions that took emotional states into account. Furthermore, they lacked the functionality to correct and store generated expressions, making it impossible to provide users with appropriate feedback. This resulted in a failure to generate expressions that accurately reflected user intent, leading to a decline in the quality of communication.

[0177] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0178] In this invention, the server includes means for receiving natural language input using an information processing device, means for generating expressions based on the received natural language input using a generative AI model, and means for recognizing emotional states from the received natural language input using an emotion analysis device. This makes it possible to generate, translate, correct, and store appropriate expressions that take into account the emotional state based on the user's natural language input.

[0179] An "information processing device" is a device that receives natural language input from a user and sends the data to a server.

[0180] A "generative AI model" is an artificial intelligence model that generates expressions based on received natural language input and performs translation and correction.

[0181] An "emotion analysis device" is a device that recognizes a user's emotional state from the natural language input it receives.

[0182] "Natural language input" refers to text data in human language that a user inputs through an information processing device.

[0183] "Representation" refers to translated or corrected text data generated by a generative AI model based on user input.

[0184] "Translation" is the process of converting text expressed in one language into another language.

[0185] "Correction" is the process of adjusting the content of a generated expression and modifying it to a more appropriate form.

[0186] "Storage" refers to the process of saving generated expressions in a database or similar system so that they can be referenced later.

[0187] The following system configurations are possible as embodiments for carrying out this invention.

[0188] The user uses a terminal to input text in natural language. For example, the user might type "I am angry." This input is sent from the terminal to the server.

[0189] The server receives natural language input from the user using an information processing device. The received data is input as a prompt to a generative AI model. The generative AI model generates a representation based on the input natural language. In this process, an emotion analyzer is used to recognize the user's emotional state from the input. For example, from the input "I am angry," the AI ​​recognizes that the user is in an angry emotional state.

[0190] The generative AI model translates the input expression, taking into account the recognized emotional state, and makes corrections as needed. For example, it can translate "I am angry" into the Japanese expression "I am angry" and then adjust the degree of emotion to "I am a little angry."

[0191] The generated expressions are stored in a database by the server, allowing them to be referenced later. Finally, the server sends the generated expressions to the terminal, providing them to the user. The user can then view the generated expressions through the terminal.

[0192] As a concrete example, a possible prompt sentence to input into a generative AI model is, "Translate and adjust the expression 'I am angry' to Japanese, considering the emotional state as recognized by the emotion engine." This prompt sentence allows the system to perform appropriate translation and emotion adjustment.

[0193] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0194] Step 1:

[0195] The user inputs text in natural language using a terminal. For example, the user inputs "I am angry". This input is sent from the terminal to the server. The input data is natural language text containing the user's emotional state.

[0196] Step 2:

[0197] The server uses an information processing device to receive the natural language input sent from the terminal. The received data is input as a prompt sentence into the generation AI model. The input here is the user's natural language text, and the output is the prompt sentence for the generation AI model.

[0198] Step 3:

[0199] The server uses an emotion analysis device to recognize the user's emotional state from the received natural language input. For example, from the input "I am angry", it is recognized that the user is in an angry emotional state. The input of this step is natural language text, and the output is the recognized emotional state.

[0200] Step 4: <00006​​​​​​​​​​​​​​​​​​The server stores the generated and corrected expressions in a database, making them available for later reference. The input for this step is the corrected expression, and the output is the data stored in the database.

[0206] Step 7:

[0207] The server sends the final generation result to the terminal and provides it to the user. The user can then verify the generated representation through the terminal. The input for this step is the representation obtained from the database, and the output is the representation provided to the user.

[0208] (Application Example 1)

[0209] Next, we will describe Application Example 1 of Form 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."

[0210] In today's information society, users have access to a vast amount of information, but finding information that is relevant to their emotions and circumstances is difficult. Furthermore, the lack of emotionally responsive information makes it difficult to increase user satisfaction.

[0211] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0212] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for analyzing the user's emotional state and recommending information based on that emotion; and means for providing the information in a format suitable for the user's emotion. This makes it possible to provide appropriate information according to the user's emotions.

[0213] "Generative AI" is an artificial intelligence technology that generates new expressions based on user input and performs translation and correction.

[0214] Translation is the process of converting content expressed in one language into another language.

[0215] "Correction" is the process of correcting errors or inappropriate parts of a generated expression.

[0216] "Storage" is the process of saving generated expressions and information so that they can be referenced later.

[0217] A "mediator" is a generative AI that acts as an intermediary between the user and the information, playing a role in generating and providing that information.

[0218] "Emotional state" refers to the psychological state or emotions inferred from the information entered by the user.

[0219] "Methods of recommending information" refer to the process of selecting and presenting appropriate information to the user based on their emotional state.

[0220] "Means of providing information in an appropriate format" refers to the process of presenting information in the most optimal format according to the user's emotions and circumstances.

[0221] The system for carrying out this invention includes a program that combines a generative AI and an emotion analysis engine to provide information that responds to the user's emotions. The server receives input from the user and recognizes the user's emotional state using the emotion analysis engine. Specifically, it extracts emotions from the input text using an emotion analysis API (e.g., an emotion analysis API).

[0222] Next, the server uses a generative AI model (e.g., a generative AI model) to generate appropriate information based on the recognized emotions. This information is provided in a format suitable for the user's emotions. For example, if the user enters "I am stressed," the emotion analysis API recognizes "stress," and the generative AI model recommends relaxing music or meditation guides.

[0223] The device displays information provided by the server to the user. This allows the user to easily obtain information that matches their emotions.

[0224] As a concrete example, the following is an example of a prompt: "If the user is feeling stressed, recommend relaxing content." By inputting this prompt into a generative AI model, it becomes possible to provide information tailored to the user's emotions.

[0225] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0226] Step 1:

[0227] The user enters text containing emotions through their device. This input is sent to the server. The input data is text information that indicates the user's emotions.

[0228] Step 2:

[0229] The server sends the received text to a sentiment analysis API to analyze the user's emotional state. The input is the user's text, and the output is the emotional state recognized by the sentiment analysis API. Specifically, the API analyzes the text and identifies the type of emotion (e.g., joy, sadness, stress).

[0230] Step 3:

[0231] The server inputs a prompt message into the generative AI model based on the emotional state obtained from the emotion analysis API. The input is the emotional state, and the output is information or content based on that emotion. Specifically, the generative AI model receives the prompt message and generates information appropriate to the user's emotions.

[0232] Step 4:

[0233] The server sends information obtained from the generated AI model to the terminal. The input is the generated information, and the output is the content provided to the user. Specifically, the server converts the information into an appropriate format and sends it to the user's terminal for display.

[0234] Step 5:

[0235] The terminal displays information received from the server to the user. The input is information sent from the server, and the output is content that the user can visually confirm. Specifically, the terminal displays the information on the screen, allowing the user to view it.

[0236] (Example 2)

[0237] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0238] In modern communication, it is crucial to generate appropriate expressions that take into account the user's emotional state. However, conventional systems have struggled to accurately recognize user emotions and adjust expressions accordingly. As a result, they have been unable to provide appropriate feedback that responds to the user's emotions, leading to a decline in the quality of communication.

[0239] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0240] In this invention, the server includes means for receiving user input, means for recognizing the user's emotional state from the input, and means for generating expressions that take the emotional state into consideration. This makes it possible to generate appropriate expressions according to the user's emotional state and improve the quality of communication.

[0241] "Means for receiving user input" refers to a function that allows a server to acquire information entered by a user through a terminal.

[0242] "Means for recognizing a user's emotional state" refers to a function that analyzes user input information, extracts emotional information from it, and identifies the user's emotional state.

[0243] "Means for generating expressions" refers to a function that creates appropriate linguistic expressions based on the recognized emotional state of the user.

[0244] "Means of providing generated expressions to users" refers to functions for presenting generated linguistic expressions to users.

[0245] "Means for translating, correcting, and storing generated expressions" refers to functions for converting generated linguistic expressions into other languages, modifying them as necessary, and saving them for later reference.

[0246] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0247] A description of embodiments for carrying out this invention will be given.

[0248] This system uses a server, a terminal, and a generative AI model to generate expressions that take into account the user's emotional state. The user inputs text using the terminal, and this input is sent to the server. The server uses an emotion engine to recognize the user's emotional state from the input. The emotion engine utilizes natural language processing technology to analyze the input text and identify the user's emotions.

[0249] Next, the server uses generative AI to generate appropriate expressions based on the recognized emotional state. The generative AI translates the input language, adjusts the degree of emotion, and creates expressions that provide appropriate feedback to the user. The generated expressions are sent from the server to the terminal and provided to the user.

[0250] For example, if a user enters "I am angry," the emotion engine recognizes the emotion of anger. The generative AI takes this emotional state into consideration and generates the expression "I am a little angry." This expression adjusts the degree of emotion to help the user calm down.

[0251] An example of a prompt would be, "If a user provides input indicating anger, please explain how the generative AI generates a response."

[0252] In this way, the system enables appropriate communication tailored to the user's emotional state.

[0253] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0254] Step 1:

[0255] The user uses a terminal to input text. For example, they might type the sentence "I am angry." This input is sent from the terminal to the server. The input data is in text format, and the server receives it.

[0256] Step 2:

[0257] The server passes the received text to the emotion engine. The emotion engine uses natural language processing techniques to recognize the user's emotional state from the input text. Specifically, it analyzes the text, extracts keywords and context related to the emotion, and identifies that the user is feeling angry. The output is the recognized emotional state.

[0258] Step 3:

[0259] The server uses a generative AI to generate expressions based on the emotional state obtained from the emotion engine. The generative AI translates the input English sentence into Japanese and further adjusts the degree of emotion. Specifically, it converts "I am angry" to "I am a little angry." The output is the adjusted Japanese expression.

[0260] Step 4:

[0261] The server sends the generated expression to the terminal. The terminal displays this expression to the user. The user reviews the generated expression through the terminal and receives feedback. This allows the user to obtain an appropriate expression that matches their emotional state.

[0262] (Application Example 2)

[0263] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0264] There is a need to achieve appropriate communication that takes into account the user's emotional state. In particular, in security services, a swift and appropriate response that responds to the user's emotions is necessary, but conventional systems have the challenge of not being able to respond while taking emotions into account.

[0265] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0266] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for utilizing generative AI as an intermediary; means for providing expressions generated by generative AI to the user; means for analyzing the user's emotional state and generating an appropriate response based on that emotional state; and means for providing that response to the user. This enables appropriate communication that takes the user's emotional state into consideration.

[0267] A "generative AI" is an artificial intelligence system that generates expressions based on user input, and then translates, corrects, and stores them.

[0268] A "mediator" is a system that utilizes generative AI to facilitate communication between users and the system.

[0269] "Emotional state" refers to the psychological state analyzed from the user's input, and includes emotions such as anger and anxiety.

[0270] A "response" is an expression provided to the user, generated by a generative AI that takes the user's emotional state into consideration.

[0271] "Security services" are services designed to ensure the safety of users, and include emergency response and customer support.

[0272] The system for implementing this invention combines generative AI and an emotion analysis engine to achieve communication that takes into account the user's emotional state. The server receives input from the user and analyzes the user's emotional state using the emotion analysis engine. This analysis uses the Google Cloud Natural Language API. After the emotional state is identified, the server uses generative AI, specifically OpenAI's GPT model, to generate an appropriate response that corresponds to the user's emotions. The generated response is then provided to the user.

[0273] This system will be installed on devices such as smartphones and robots and used in customer support for security services. For example, if a user enters "The security alarm is malfunctioning," the server will detect the user's anxiety, and a generative AI will generate a response such as "Please rest assured. We will check it immediately."

[0274] Examples of prompt messages include the following:

[0275] User's emotional state: Anxiety

[0276] User input: The security alarm is malfunctioning

[0277] Response generation prompt: Please generate a response to reassure the user.

[0278] In this way, appropriate communication considering the user's emotional state becomes possible.

[0279] The flow of specific processing in Application Example 2 will be described using FIG. 18. [[ID=2D]]

[0280] Step 1:

[0281] The user enters text through the terminal. The entered text is sent to the server as data for analyzing the user's emotional state.

[0282] Step 2:

[0283] The server sends the received text to the Google Cloud Natural Language API for sentiment analysis. The input is the user's text, and the output is the emotional state (e.g., anxiety, anger, etc.). The server passes this emotional state to the next processing step.

[0284] Step 3:

[0285] The server generates a prompt sentence considering the emotional state. This prompt sentence is input to the generation AI model and includes instructions for generating a response according to the user's emotion.

[0286] Step 4:

[0287] The server takes prompt text as input to a generative AI model (OpenAI's GPT model) and generates an appropriate response. The input is the prompt text, and the output is the response provided to the user.

[0288] Step 5:

[0289] The server sends the generated response to the terminal, providing it to the user. The user receives this response through the terminal and can gain a sense of reassurance.

[0290] (Other examples)

[0291] Next, other embodiments will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0292] In recent years, text generation technology using generative AI models has attracted attention, but there is a lack of systems that can effectively process user input and appropriately translate, correct, and provide the generated text. Therefore, the challenge is to achieve high-quality text generation that meets the diverse needs of users.

[0293] The identification process performed by the identification processing unit 290 of the data processing device 12 in other embodiments is realized by the following means.

[0294] In this invention, the server includes means for receiving input from a user, means for preprocessing the input data, and means for generating prompt sentences to instruct a generative AI model to generate a text representation. This enables high-quality text generation based on user input.

[0295] "Means of receiving user input" refers to an interface for users to provide information to the system, and has the function of acquiring data in digital format.

[0296] "Means for preprocessing input data" refers to a data cleaning module that normalizes the data received from the user and removes noise, preparing the data for the generative AI model to function properly.

[0297] A "generative AI model" is an artificial intelligence model that uses natural language generation algorithms to generate text based on a given prompt; OpenAI's GPT-3 is an example of this type.

[0298] A "prompt" is text generated to instruct a generative AI model on a specific task, indicating what kind of text the model should generate.

[0299] A "translation module" is a software component used to translate generated text into a specified language, such as one that uses the Google Cloud Translation API.

[0300] A "correction module" is a software component that implements an algorithm for detecting and correcting grammatical errors in translated text.

[0301] A "database" is a storage system for saving corrected text, and it uses a database management system such as MySQL (registered trademark).

[0302] A "user interface" is an interface through which a user interacts with a system, and it has a display function to provide the user with saved text.

[0303] The following describes the "modes for carrying out the invention."

[0304] The present invention is a system that receives an input from a user, generates text using a generative AI model based on it, and performs translation, correction, storage, and provision. In this system, each element of the server, terminal, and user operates in cooperation.

[0305] The server provides a web interface to receive an input from the user. The data input by the user is preprocessed using a data cleaning module on the server (e.g., the pandas library in Python). (Registered trademark) In this preprocessing, normalization of the input data and removal of noise are performed.

[0306] Next, based on the preprocessed data, the server generates a prompt sentence for instructing the generative AI model (e.g., GPT-3 of OpenAI) to generate a text representation. This prompt sentence functions as an instruction to the generative AI model and is in a form such as "Please generate a review of a new product in the following format:".

[0307] The generated prompt sentence is input into the generative AI model, and the model generates a text representation using a natural language generation algorithm. The generated text is translated into a specified language using a translation module on the server (e.g., Google Cloud Translation API). Then, using a grammar checking algorithm (e.g., Grammarly API), grammatical errors in the translated text are corrected.

[0308] The corrected text is stored in the server's database (e.g., MySQL). Finally, the server provides the stored text to the terminal via the user interface. The user can check the text provided on the terminal and use it as needed.

[0309] Example of a prompt sentence: Please generate a review of a new product in the following format:

[0310] Thus, the present invention enables high-quality text generation based on user input, allowing for the rapid and accurate provision of information to the user.

[0311] The flow of a specific process in another embodiment will be explained using Figure 19.

[0312] Step 1:

[0313] The user enters text data through a web interface. For example, they might enter, "Please write a review of the new product." This input data is sent to the server. The server retrieves the received data in digital format and prepares it for the next processing step.

[0314] Step 2:

[0315] The server processes the received user input using a data cleaning module (e.g., Python). (Registered trademark) The data is preprocessed using the pandas library. Specifically, the input data is normalized, and unnecessary noise and special characters are removed. This process yields data in a format suitable for generative AI models.

[0316] Step 3:

[0317] The server generates prompts based on the pre-processed data, instructing the generative AI model to generate text representations. For example, it might create a prompt such as, "Generate a review of the new product in the following format:" This prompt functions as an instruction to the generative AI model.

[0318] Step 4:

[0319] The server inputs the generated prompt text into a generation AI model (e.g., OpenAI's GPT-3). The model uses a natural language generation algorithm to generate a text representation based on the prompt. For example, the model might generate a review sentence such as, "This product is very easy to use and has an excellent design."

[0320] Step 5:

[0321] The server translates the generated text into the specified language using a translation module (e.g., Google Cloud Translation API). The translated text is then grammatically corrected using a grammar checking algorithm (e.g., Grammarly API). This process results in accurate and readable text.

[0322] Step 6:

[0323] The server saves the corrected text to a database (e.g., MySQL). This save ensures that the data is retained so that users can access it later.

[0324] Step 7:

[0325] The server provides the stored text to the terminal via the user interface. The user can then view the provided text on the terminal and use it as needed.

[0326] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0327] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0328] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0329] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0330] [Second Embodiment]

[0331] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0332] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0333] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0334] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0335] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0336] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0337] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0338] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0339] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0340] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0341] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0342] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0343] "Example of form 1"

[0344] One embodiment of the present invention is a system that translates, corrects, and stores expressions using generative AI. This system receives input from a user and uses generative AI to generate an expression based on that input. The generated expression is translated, corrected, and stored, and the results are provided to the user. For example, if a user inputs "I am angry" in English, the generative AI receives this input and translates it into the Japanese expression "I am angry." The generative AI can also correct this expression, adjusting the degree of emotion, for example, to "I am a little angry." Furthermore, the generative AI can store this expression for later reference.

[0345] "Example of form 2"

[0346] As another embodiment of the present invention, there is a system in which generative AI generates an expression in consideration of the user's emotional state. In this system, the generative AI analyzes the user's emotional state and generates an expression based on the result. For example, when the user inputs an expression indicating anger, the generative AI can generate an expression that calms the user down in consideration of that emotional state. In this way, the present invention realizes appropriate communication considering the user's emotional state.

[0347] The processing flow of each exemplary embodiment will be described below.

[0348] "Exemplary Embodiment 1"

[0349] Step 1: The system receives an input from the user. For example, the user inputs "I am angry" in English.

[0350] Step 2: The generative AI generates an expression based on the input from the user. In this example, it translates "I am angry" into a Japanese expression "私は怒っています".

[0351] Step 3: The generative AI corrects the generated expression. For example, it adjusts the degree of emotion like changing "私は怒っています" to "私は少し怒っています".

[0352] Step 4: The generative AI stores the generated and corrected expression. This expression can be referred to later.

[0353] Step 5: The system provides the expression generated by the generative AI to the user.

[0354] "Exemplary Embodiment 2"

[0355] Step 1: The system receives an input from the user. This input indicates the user's emotional state.

[0356] Step 2: The generative AI analyzes the user's emotional state.

[0357] Step 3: The generative AI generates expressions based on the analysis results. For example, if the user provides input indicating anger, the generative AI will consider that emotional state and generate expressions that will help the user calm down.

[0358] Step 4: The system provides the user with an expression generated by a generative AI. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0359] (Example 1)

[0360] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0361] In today's information society, users are required to communicate smoothly between different languages. However, conventional translation systems have difficulty accurately conveying emotional nuances and are insufficient in generating expressions that take into account the user's emotional state. Furthermore, there is a lack of systems that can efficiently store and refer to the generated expressions at a later date.

[0362] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0363] In this invention, the server includes means for receiving input from a user, means for pre-processing said input, and means for generating expressions using a generative AI model. This enables the generation of natural expressions that take into account the user's emotional state, as well as translation, correction, and storage.

[0364] A "user" is an entity that provides input to a system and receives the generated representation.

[0365] "Input" refers to the text data and information that a user provides to the system.

[0366] "Preprocessing" is the process of shaping or transforming data so that the generative AI model can process the input appropriately.

[0367] A "generative AI model" is an algorithm or system that performs natural language processing and generates new expressions based on the input.

[0368] "Representation" refers to the format of text or information generated by a generative AI model.

[0369] Translation is the process of converting a generated expression into a different language.

[0370] "Correction" is the process of modifying the content of a generated expression and adjusting it to a more appropriate form.

[0371] "Storage" refers to the process of saving generated expressions to a database or similar system so that they can be referenced later.

[0372] A "server" is a computer system that receives input from users, generates expressions using a generative AI model, and then translates, corrects, and stores them.

[0373] One embodiment of this invention begins with a user inputting text into the system via a terminal. The user inputs an English sentence, for example, "I am angry." The terminal sends this input to the server.

[0374] The server preprocesses the received input data. Preprocessing involves formatting the data and removing unnecessary characters so that the generative AI model can process it appropriately. The preprocessed data is then input into the generative AI model. This generative AI model uses an algorithm for natural language processing, such as OpenAI's GPT-4.

[0375] The generative AI model generates new expressions based on the input. For example, for the input "I am angry", it generates the Japanese expression "私は怒っています". The generated expression is further translated and corrected if necessary. In the correction process, the degree of emotion can be adjusted and corrected, for example, to "私は少し怒っています".

[0376] Finally, the server stores the generated expression in the database. This stored data is for the user to refer to later. The server sends the translated and corrected expression to the terminal, and the user can check the result through the terminal.

[0377] As a specific example, when the user inputs "I am angry", the system translates it to "私は怒っています" and further corrects it to "私は少し怒っています". This result is provided to the user and stored in the database.

[0378] An example of a prompt sentence is "Please translate the following English sentence into Japanese and adjust the degree of emotion: I am angry".

[0379] The flow of the specific process in Example 1 will be described using FIG. 11.

[0380] Step 1:

[0381] The user uses the terminal to input text into the system. For example, input an English sentence such as "I am angry". The terminal sends this input to the server. The input is text data expressing the user's emotion.

[0382] Step 2:

[0383] The server preprocesses the input data received from the terminal. In the preprocessing, the data is formatted and unnecessary characters are removed so that the generative AI model can process it appropriately. The input is raw data from the user, and the output is formatted text data.

[0384] Step 3:

[0385] The server inputs the preprocessed data into the generative AI model. This model performs natural language processing and generates new expressions based on the input. For example, for the input "I am angry", it generates the Japanese expression "私は怒っています". The input is the formatted text data, and the output is the generated expression.

[0386] Step 4:

[0387] The server translates the generated expression. Using the generative AI model, it converts the generated expression into a different language. The input is the generated expression, and the output is the translated expression.

[0388] Step 5:

[0389] The server corrects the translated expression. Using the generative AI model, it modifies the expression to adjust the degree of emotion. For example, it corrects "私は怒っています" to "私は少し怒っています". The input is the translated expression, and the output is the corrected expression.

[0390] Step 6:

[0391] The server stores the corrected expression in the database. The stored data is for the user to be able to refer to later. The input is the corrected expression, and the output is the data stored in the database.

[0392] Step 7:

[0393] The server sends the final expression to the terminal. The user can view the translated and corrected results through the terminal. The input is the corrected expression, and the output is the result provided to the user.

[0394] (Application Example 1)

[0395] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] In today's information and communication environment, user-generated content is often multilingual and expresses diverse emotions. Therefore, it is necessary to appropriately translate this content, adjust the emotional intensity, and deliver it to other users. However, conventional systems struggle with real-time translation and emotional adjustment, hindering improvements in the user experience. Solving this problem is crucial.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0398] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for translating user input in real time and adjusting the degree of emotion; and means for distributing the adjusted expressions to other users. This makes it possible to appropriately translate user-generated content into multiple languages, adjust the degree of emotion, and distribute it to other users in real time.

[0399] "Generative AI" is an artificial intelligence technology that generates expressions based on user input and performs translation, correction, and adjustment of emotional intensity.

[0400] "Translation" is the process of converting content expressed in one language into another language.

[0401] "Correction" is the process of correcting errors in generated expressions to make them accurate.

[0402] "Storage" refers to the process of recording generated expressions so that they can be referenced later.

[0403] "Real-time" refers to processing user input immediately and providing results instantly.

[0404] "Emotional intensity" is an indicator that shows the strength and type of emotion contained in the user's expression.

[0405] "Distribution" refers to the process of sending and sharing a generated expression with other users.

[0406] A "user" is an entity that uses the system to generate content and perform tasks such as translation and sentiment adjustment.

[0407] A "user" is the entity that receives the distributed content.

[0408] The system for implementing this invention has the functionality to translate, correct, and store user input using generative AI, and further adjust the degree of emotion before distributing it to other users. The server utilizes a generative AI model to receive user input and perform translation and emotion adjustment in real time. Specifically, the server receives user input as text data and sends prompts to the generative AI model. An example of a generative AI model used is OpenAI's GPT-3.

[0409] The server receives the translation and sentiment adjustment results returned from the generative AI model and stores them in a database. This stored data is referenced when distributing to other users. The terminal's role is to send the user's input to the server, receive the response from the server, and display it.

[0410] For example, if a user enters "I am very happy," the server sends the prompt "Translate and adjust the sentiment of the following text: 'I am very happy'" to the generative AI model. The generative AI model translates this to "I am very happy" and adjusts the degree of emotion to "I am happy." This result is stored by the server and distributed to other users.

[0411] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0412] Step 1:

[0413] The user enters text using a terminal. The entered text is sent from the terminal to the server. The input data is natural language text that includes the user's emotions and intentions.

[0414] Step 2:

[0415] The server generates a prompt message to send the received text to the AI ​​model. This prompt message instructs the model on how to process the input text. For example, it might be in the format, "Translate and adjust the sentiment of the following text: 'I am very happy'".

[0416] Step 3:

[0417] The server sends the generated prompt text to the generative AI model. The generative AI model translates the input text based on the prompt text and adjusts the degree of emotion. The input is the prompt text, and the output is the translated text and the adjusted emotion expression.

[0418] Step 4:

[0419] The server receives the translation and sentiment adjustment results returned by the generative AI model. The server stores these results in a database. The stored data is available for later reference and ready to be distributed to other users.

[0420] Step 5:

[0421] The server distributes stored translation and sentiment-adjusted results to other users. The terminal receives the response from the server and displays it to the user. This allows users to receive sentiment-adjusted content from other users in real time.

[0422] (Example 2)

[0423] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0424] Conventional systems using generative artificial intelligence have not adequately considered the user's emotional state when generating expressions, making it difficult to provide appropriate communication to the user. Furthermore, the instructions for generating expressions that respond to emotions are insufficient, making it impossible to provide responses that match the user's feelings.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0426] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for analyzing the user's emotional state; and means for giving instructions to the generative artificial intelligence based on the analysis results. This makes it possible to generate appropriate expressions according to the user's emotional state and provide better communication to the user.

[0427] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate expressions based on user input, and then translate, correct, and store them.

[0428] An "intermediary" is an entity that utilizes generative artificial intelligence to facilitate the smooth exchange of information between users and other systems or services.

[0429] A "user" is an entity that operates a system using generative artificial intelligence, inputs information, and receives the generated expression.

[0430] "Emotional state" refers to the type and intensity of emotions analyzed from the information entered by the user.

[0431] "Analysis" is the process of analyzing user input information to identify specific emotional states.

[0432] An "instruction" is a command or guideline given to a generative artificial intelligence system based on the analysis results, for generating a specific expression.

[0433] "Expression" refers to means of communication, such as text and speech, that generative artificial intelligence generates based on user input and emotional states.

[0434] A description of embodiments for carrying out this invention will be given.

[0435] The server provides a system that uses generative artificial intelligence to generate expressions that correspond to the user's emotional state. This system receives input from the user and uses sentiment analysis software to analyze that emotional state. Specific software examples include a "natural language understanding API" and a "sentiment analysis API." This software analyzes the user's input text and identifies their emotional state.

[0436] The server generates prompt statements and provides instructions to the generative artificial intelligence based on the analyzed emotional state. These prompt statements instruct the generative AI on what kind of expressions it should generate. For example, if the user is expressing anger, the server will generate a prompt statement such as, "If the user is angry, please generate expressions that will help them calm down."

[0437] Generative artificial intelligence generates appropriate expressions based on prompt text. Specifically, it uses a generative AI model (e.g., a natural language generation model) to generate text that corresponds to the user's emotional state. This generated expression is then sent to the user via the device to provide appropriate communication.

[0438] For example, if a user inputs "Why is it so slow!", the server uses emotion analysis software to identify the emotion as "anger." Then, it has a generative artificial intelligence system generate a calming message such as, "We apologize for the delay. We are doing our best to resolve the issue, so please wait a moment," and sends it to the user. In this way, it is possible to provide an appropriate response tailored to the user's emotional state.

[0439] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0440] Step 1:

[0441] The user enters text through the terminal. For example, they might type the message, "Why is it so slow!" This input data is then sent to the server.

[0442] Step 2:

[0443] The server passes the received text data to sentiment analysis software. Specifically, it uses natural language processing techniques to analyze the text and identify the emotional state. The input is the user's text data, and the output is an emotional state such as "anger."

[0444] Step 3:

[0445] The server generates prompt sentences to input into the generative AI model based on the analyzed emotional state. For example, it might create a prompt sentence such as, "If the user is angry, generate a calming expression." This prompt sentence becomes the input to the generative AI model.

[0446] Step 4:

[0447] The server sends a prompt to the generative AI model, which generates an expression that corresponds to the user's emotional state. The generative AI model then generates an appropriate response based on the prompt. The input is a prompt, and the output is an expression such as, "We apologize for the wait. We are doing our best to resolve the issue, so please wait a moment."

[0448] Step 5:

[0449] The server sends the generated expression to the terminal and displays it to the user. The user can then see the generated response on the terminal and receive appropriate communication that matches their emotions.

[0450] (Application Example 2)

[0451] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0452] In modern information and communication technology, there is a demand for appropriate communication that takes into account the emotional state of the user. However, conventional systems have struggled to accurately analyze the user's emotions and generate appropriate responses based on them. As a result, they have failed to alleviate the user's anxiety or anger, sometimes leading to decreased satisfaction.

[0453] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0454] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for utilizing generative artificial intelligence as an intermediary; means for providing expressions generated by generative artificial intelligence to the user; means for analyzing the user's emotional state; and means for generating an appropriate response based on the emotional state. This enables appropriate communication that is in line with the user's emotions.

[0455] "Generative artificial intelligence" is an artificial intelligence technology that can generate expressions based on user input, and then translate, correct, and store them.

[0456] A "mediator" is an entity in which generative artificial intelligence plays a role in facilitating the exchange of information between the user and the system.

[0457] "User" refers to an individual or group that uses the system and is the entity that receives the expressions provided by the generative artificial intelligence.

[0458] "Emotional state" refers to the psychological state or emotions that a user exhibits in a specific situation, and is the subject of analysis by the system.

[0459] A "response" is an expression generated by a generative artificial intelligence system based on the user's emotional state, and serves as a means of communication with the user.

[0460] The system for carrying out this invention operates in a network environment including a server and terminals. The server processes user input using generative artificial intelligence and generates an appropriate response. Specifically, the server uses a sentiment analysis API (e.g., Google Cloud Natural Language API) to analyze the emotional state from the user's input text. Based on this analysis, a generative artificial intelligence model (e.g., OpenAI's GPT model) generates a response that corresponds to the user's emotions.

[0461] The terminal provides an interface for user input and displays responses from the server. When a user enters text into the terminal, that data is sent to the server for sentiment analysis and response generation. The generated response is then sent back to the terminal and displayed to the user.

[0462] For example, if a user enters "I'm worried about the recent security breach," the server uses a sentiment analysis API to determine that the user is "anxious." Based on this emotional state, a generative artificial intelligence model generates a response such as, "Please rest assured that our team monitors the system 24 / 7 and we have implemented the latest security measures." An example of a prompt would be, "The user is feeling anxious. Generate a reassuring response."

[0463] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0464] Step 1:

[0465] The user enters text into the device. The entered text is sent to the server as data to analyze the user's emotional state.

[0466] Step 2:

[0467] The server sends the received text to a sentiment analysis API. The sentiment analysis API analyzes the content of the text and identifies the user's emotional state (e.g., anxiety, anger, joy, etc.). The analysis results are returned to the server.

[0468] Step 3:

[0469] The server receives the analysis results from the sentiment analysis API and creates a prompt message for the generative AI model. The prompt message contains instructions for generating an appropriate response based on the user's emotional state. For example, it might generate a prompt message such as, "The user is feeling anxious. Generate a reassuring response."

[0470] Step 4:

[0471] The server sends a prompt to the generative AI model, instructing it to generate a response that corresponds to the user's emotional state. The generative AI model generates an appropriate response based on the prompt and returns the result to the server.

[0472] Step 5:

[0473] The server sends the response received from the generated AI model to the terminal. The terminal displays this response to the user. The user can review the generated response and provide further input as needed.

[0474] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0475] "Example of form 1"

[0476] One embodiment of the present invention provides a system that combines an emotion engine. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI stores the generated and corrected expressions and provides them to the user.

[0477] "Example of form 2"

[0478] Another embodiment of the present invention provides a system that combines an emotion engine and a generative AI. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI provides the generated and corrected expression to the user. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0479] The following describes the processing flow for each example of the form.

[0480] "Example of form 1"

[0481] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[0482] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[0483] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[0484] Step 4: Store the expressions generated and corrected by the generative AI, and provide those expressions to the user.

[0485] "Example of form 2"

[0486] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[0487] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[0488] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[0489] Step 4: The generative AI provides the user with the generated and corrected expressions. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0490] (Example 1)

[0491] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0492] Conventional information processing systems struggled to accurately translate users' natural language input and generate expressions that took emotional states into account. Furthermore, they lacked the functionality to correct and store generated expressions, making it impossible to provide users with appropriate feedback. This resulted in a failure to generate expressions that accurately reflected user intent, leading to a decline in the quality of communication.

[0493] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0494] In this invention, the server includes means for receiving natural language input using an information processing device, means for generating expressions based on the received natural language input using a generative AI model, and means for recognizing emotional states from the received natural language input using an emotion analysis device. This makes it possible to generate, translate, correct, and store appropriate expressions that take into account the emotional state based on the user's natural language input.

[0495] An "information processing device" is a device that receives natural language input from a user and sends the data to a server.

[0496] A "generative AI model" is an artificial intelligence model that generates expressions based on received natural language input and performs translation and correction.

[0497] An "emotion analysis device" is a device that recognizes a user's emotional state from the natural language input it receives.

[0498] "Natural language input" refers to text data in human language that a user inputs through an information processing device.

[0499] "Representation" refers to translated or corrected text data generated by a generative AI model based on user input.

[0500] "Translation" is the process of converting text expressed in one language into another language.

[0501] "Correction" is the process of adjusting the content of a generated expression and modifying it to a more appropriate form.

[0502] "Storage" refers to the process of saving generated expressions in a database or similar system so that they can be referenced later.

[0503] The following system configurations are possible as embodiments for carrying out this invention.

[0504] The user uses a terminal to input text in natural language. For example, the user might type "I am angry." This input is sent from the terminal to the server.

[0505] The server receives natural language input from the user using an information processing device. The received data is input as a prompt to a generative AI model. The generative AI model generates a representation based on the input natural language. In this process, an emotion analyzer is used to recognize the user's emotional state from the input. For example, from the input "I am angry," the AI ​​recognizes that the user is in an angry emotional state.

[0506] The generative AI model translates the input expression, taking into account the recognized emotional state, and makes corrections as needed. For example, it can translate "I am angry" into the Japanese expression "I am angry" and then adjust the degree of emotion to "I am a little angry."

[0507] The generated expressions are stored in a database by the server, allowing them to be referenced later. Finally, the server sends the generated expressions to the terminal, providing them to the user. The user can then view the generated expressions through the terminal.

[0508] As a concrete example, a possible prompt sentence to input into a generative AI model is, "Translate and adjust the expression 'I am angry' to Japanese, considering the emotional state as recognized by the emotion engine." This prompt sentence allows the system to perform appropriate translation and emotion adjustment.

[0509] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0510] Step 1:

[0511] The user inputs text in natural language using a terminal. For example, the user inputs "I am angry". This input is sent from the terminal to the server. The input data is natural language text containing the user's emotional state.

[0512] Step 2:

[0513] The server uses an information processing device to receive the natural language input sent from the terminal. The received data is input as a prompt sentence into the generation AI model. The input here is the user's natural language text, and the output is the prompt sentence for the generation AI model.

[0514] Step 3:

[0515] The server uses an emotion analysis device to recognize the user's emotional state from the received natural language input. For example, from the input "I am angry", it is recognized that the user is in an angry emotional state. The input of this step is natural language text, and the output is the recognized emotional state.

[0516] Step 4:

[0517] The server uses a generation AI model to generate a new expression based on the input data while considering the recognized emotional state. Specifically, it translates "I am angry" into the Japanese expression "私は怒っています". The input of this step is the prompt sentence and the emotional state, and the output is the translated expression.

[0518] Step 5:

[0519] The server further corrects the generated expression and adjusts the degree of emotion. For example, it changes "私は怒っています" to "私は少し怒っています". The input of this step is the translated expression, and the output is the corrected expression.

[0520] Step 6:

[0521] The server stores the generated and corrected expressions in a database, making them available for later reference. The input for this step is the corrected expression, and the output is the data stored in the database.

[0522] Step 7:

[0523] The server sends the final generation result to the terminal and provides it to the user. The user can then verify the generated representation through the terminal. The input for this step is the representation obtained from the database, and the output is the representation provided to the user.

[0524] (Application Example 1)

[0525] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0526] In today's information society, users have access to a vast amount of information, but finding information that is relevant to their emotions and circumstances is difficult. Furthermore, the lack of emotionally responsive information makes it difficult to increase user satisfaction.

[0527] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0528] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for analyzing the user's emotional state and recommending information based on that emotion; and means for providing the information in a format suitable for the user's emotion. This makes it possible to provide appropriate information according to the user's emotions.

[0529] "Generative AI" is an artificial intelligence technology that generates new expressions based on user input and performs translation and correction.

[0530] Translation is the process of converting content expressed in one language into another language.

[0531] "Correction" is the process of correcting errors or inappropriate parts of a generated expression.

[0532] "Storage" is the process of saving generated expressions and information so that they can be referenced later.

[0533] A "mediator" is a generative AI that acts as an intermediary between the user and the information, playing a role in generating and providing that information.

[0534] "Emotional state" refers to the psychological state or emotions inferred from the information entered by the user.

[0535] "Methods of recommending information" refer to the process of selecting and presenting appropriate information to the user based on their emotional state.

[0536] "Means of providing information in an appropriate format" refers to the process of presenting information in the most optimal format according to the user's emotions and circumstances.

[0537] The system for carrying out this invention includes a program that combines a generative AI and an emotion analysis engine to provide information that responds to the user's emotions. The server receives input from the user and recognizes the user's emotional state using the emotion analysis engine. Specifically, it extracts emotions from the input text using an emotion analysis API (e.g., an emotion analysis API).

[0538] Next, the server uses a generative AI model (e.g., a generative AI model) to generate appropriate information based on the recognized emotions. This information is provided in a format suitable for the user's emotions. For example, if the user enters "I am stressed," the emotion analysis API recognizes "stress," and the generative AI model recommends relaxing music or meditation guides.

[0539] The device displays information provided by the server to the user. This allows the user to easily obtain information that matches their emotions.

[0540] As a concrete example, the following is an example of a prompt: "If the user is feeling stressed, recommend relaxing content." By inputting this prompt into a generative AI model, it becomes possible to provide information tailored to the user's emotions.

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0542] Step 1:

[0543] The user enters text containing emotions through their device. This input is sent to the server. The input data is text information that indicates the user's emotions.

[0544] Step 2:

[0545] The server sends the received text to a sentiment analysis API to analyze the user's emotional state. The input is the user's text, and the output is the emotional state recognized by the sentiment analysis API. Specifically, the API analyzes the text and identifies the type of emotion (e.g., joy, sadness, stress).

[0546] Step 3:

[0547] The server inputs a prompt message into the generative AI model based on the emotional state obtained from the emotion analysis API. The input is the emotional state, and the output is information or content based on that emotion. Specifically, the generative AI model receives the prompt message and generates information appropriate to the user's emotions.

[0548] Step 4:

[0549] The server sends information obtained from the generated AI model to the terminal. The input is the generated information, and the output is the content provided to the user. Specifically, the server converts the information into an appropriate format and sends it to the user's terminal for display.

[0550] Step 5:

[0551] The terminal displays information received from the server to the user. The input is information sent from the server, and the output is content that the user can visually confirm. Specifically, the terminal displays the information on the screen, allowing the user to view it.

[0552] (Example 2)

[0553] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0554] In modern communication, it is crucial to generate appropriate expressions that take into account the user's emotional state. However, conventional systems have struggled to accurately recognize user emotions and adjust expressions accordingly. As a result, they have been unable to provide appropriate feedback that responds to the user's emotions, leading to a decline in the quality of communication.

[0555] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0556] In this invention, the server includes means for receiving user input, means for recognizing the user's emotional state from the input, and means for generating expressions that take the emotional state into consideration. This makes it possible to generate appropriate expressions according to the user's emotional state and improve the quality of communication.

[0557] "Means for receiving user input" refers to a function that allows a server to acquire information entered by a user through a terminal.

[0558] "Means for recognizing a user's emotional state" refers to a function that analyzes user input information, extracts emotional information from it, and identifies the user's emotional state.

[0559] "Means for generating expressions" refers to a function that creates appropriate linguistic expressions based on the recognized emotional state of the user.

[0560] "Means of providing generated expressions to users" refers to functions for presenting generated linguistic expressions to users.

[0561] "Means for translating, correcting, and storing generated expressions" refers to functions for converting generated linguistic expressions into other languages, modifying them as necessary, and saving them for later reference.

[0562] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0563] A description of embodiments for carrying out this invention will be given.

[0564] This system uses a server, a terminal, and a generative AI model to generate expressions that take into account the user's emotional state. The user inputs text using the terminal, and this input is sent to the server. The server uses an emotion engine to recognize the user's emotional state from the input. The emotion engine utilizes natural language processing technology to analyze the input text and identify the user's emotions.

[0565] Next, the server uses generative AI to generate appropriate expressions based on the recognized emotional state. The generative AI translates the input language, adjusts the degree of emotion, and creates expressions that provide appropriate feedback to the user. The generated expressions are sent from the server to the terminal and provided to the user.

[0566] For example, if a user enters "I am angry," the emotion engine recognizes the emotion of anger. The generative AI takes this emotional state into consideration and generates the expression "I am a little angry." This expression adjusts the degree of emotion to help the user calm down.

[0567] An example of a prompt would be, "If a user provides input indicating anger, please explain how the generative AI generates a response."

[0568] In this way, the system enables appropriate communication tailored to the user's emotional state.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0570] Step 1:

[0571] The user uses a terminal to input text. For example, they might type the sentence "I am angry." This input is sent from the terminal to the server. The input data is in text format, and the server receives it.

[0572] Step 2:

[0573] The server passes the received text to the emotion engine. The emotion engine uses natural language processing techniques to recognize the user's emotional state from the input text. Specifically, it analyzes the text, extracts keywords and context related to the emotion, and identifies that the user is feeling angry. The output is the recognized emotional state.

[0574] Step 3:

[0575] The server uses a generative AI to generate expressions based on the emotional state obtained from the emotion engine. The generative AI translates the input English sentence into Japanese and further adjusts the degree of emotion. Specifically, it converts "I am angry" to "I am a little angry." The output is the adjusted Japanese expression.

[0576] Step 4:

[0577] The server sends the generated expression to the terminal. The terminal displays this expression to the user. The user reviews the generated expression through the terminal and receives feedback. This allows the user to obtain an appropriate expression that matches their emotional state.

[0578] (Application Example 2)

[0579] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0580] There is a need to achieve appropriate communication that takes into account the user's emotional state. In particular, in security services, a swift and appropriate response that responds to the user's emotions is necessary, but conventional systems have the challenge of not being able to respond while taking emotions into account.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0582] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for utilizing generative AI as an intermediary; means for providing expressions generated by generative AI to the user; means for analyzing the user's emotional state and generating an appropriate response based on that emotional state; and means for providing that response to the user. This enables appropriate communication that takes the user's emotional state into consideration.

[0583] A "generative AI" is an artificial intelligence system that generates expressions based on user input, and then translates, corrects, and stores them.

[0584] A "mediator" is a system that utilizes generative AI to facilitate communication between users and the system.

[0585] "Emotional state" refers to the psychological state analyzed from the user's input, and includes emotions such as anger and anxiety.

[0586] A "response" is an expression provided to the user, generated by a generative AI that takes the user's emotional state into consideration.

[0587] "Security services" are services designed to ensure the safety of users, and include emergency response and customer support.

[0588] The system for implementing this invention combines generative AI and an emotion analysis engine to achieve communication that takes into account the user's emotional state. The server receives input from the user and analyzes the user's emotional state using the emotion analysis engine. This analysis uses the Google Cloud Natural Language API. After the emotional state is identified, the server uses generative AI, specifically OpenAI's GPT model, to generate an appropriate response that corresponds to the user's emotions. The generated response is then provided to the user.

[0589] This system will be installed on devices such as smartphones and robots and used in customer support for security services. For example, if a user enters "The security alarm is malfunctioning," the server will detect the user's anxiety, and a generative AI will generate a response such as "Please rest assured. We will check it immediately."

[0590] Examples of prompt messages include the following:

[0591] User's emotional state: Anxiety

[0592] User input: Security alarm is malfunctioning.

[0593] Response generation prompt: Generate a response to reassure the user.

[0594] In this way, it becomes possible to communicate appropriately while taking into account the user's emotional state.

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0596] Step 1:

[0597] The user enters text through their device. The entered text is sent to the server as data to analyze the user's emotional state.

[0598] Step 2:

[0599] The server sends the received text to the Google Cloud Natural Language API for sentiment analysis. The input is the user's text, and the output is the emotional state (e.g., anxiety, anger). The server then passes this emotional state to the next processing step.

[0600] Step 3:

[0601] The server generates prompts that take the user's emotional state into account. These prompts are input to a generative AI model and contain instructions for generating responses that correspond to the user's emotions.

[0602] Step 4:

[0603] The server takes prompt text as input to a generative AI model (OpenAI's GPT model) and generates an appropriate response. The input is the prompt text, and the output is the response provided to the user.

[0604] Step 5:

[0605] The server sends the generated response to the terminal, providing it to the user. The user receives this response through the terminal and can gain a sense of reassurance.

[0606] (Other examples)

[0607] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0608] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0611] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0612] [Third Embodiment]

[0613] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0614] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0615] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0616] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0617] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0618] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0619] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0620] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0621] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0622] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0623] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0624] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0625] "Example of form 1"

[0626] One embodiment of the present invention is a system that translates, corrects, and stores expressions using generative AI. This system receives input from a user and uses generative AI to generate an expression based on that input. The generated expression is translated, corrected, and stored, and the results are provided to the user. For example, if a user inputs "I am angry" in English, the generative AI receives this input and translates it into the Japanese expression "I am angry." The generative AI can also correct this expression, adjusting the degree of emotion, for example, to "I am a little angry." Furthermore, the generative AI can store this expression for later reference.

[0627] "Example of form 2"

[0628] As another embodiment of the present invention, there is a system in which the generative AI generates an expression in consideration of the user's emotional state. In this system, the generative AI analyzes the user's emotional state and generates an expression based on the result. For example, when the user inputs an expression indicating anger, the generative AI can generate an expression that calms the user down considering the emotional state. In this way, the present invention realizes appropriate communication considering the user's emotional state.

[0629] The processing flow of each exemplary embodiment will be described below.

[0630] "Exemplary Embodiment 1"

[0631] Step 1: The system receives an input from the user. For example, the user inputs "I am angry" in English.

[0632] Step : The generative AI generates an expression based on the input from the user. In this example, "I am angry" is translated into a Japanese expression "私は怒っています".

[0633] Step 3: The generative AI corrects the generated expression. For example, the degree of emotion is adjusted such as changing "私は怒っています" to "私は少し怒っています".

[0634] Step 4: The generative AI stores the generated and corrected expression. This expression can be referred to later.

[0635] Step 5: The system provides the expression generated by the generative AI to the user.

[0636] "Exemplary Embodiment 2"

[0637] Step 1: The system receives an input from the user. This input indicates the user's emotional state.

[0638] Step 2: The generative AI analyzes the user's emotional state.

[0639] Step 3: The generative AI generates expressions based on the analysis results. For example, if the user provides input indicating anger, the generative AI will consider that emotional state and generate expressions that will help the user calm down.

[0640] Step 4: The system provides the user with an expression generated by a generative AI. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0641] (Example 1)

[0642] Next, we will describe Embodiment 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0643] In today's information society, users are required to communicate smoothly between different languages. However, conventional translation systems have difficulty accurately conveying emotional nuances and are insufficient in generating expressions that take into account the user's emotional state. Furthermore, there is a lack of systems that can efficiently store and refer to the generated expressions at a later date.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0645] In this invention, the server includes means for receiving input from a user, means for pre-processing said input, and means for generating expressions using a generative AI model. This enables the generation of natural expressions that take into account the user's emotional state, as well as translation, correction, and storage.

[0646] A "user" is an entity that provides input to a system and receives the generated representation.

[0647] "Input" refers to the text data and information that a user provides to the system.

[0648] "Preprocessing" is the process of shaping or transforming data so that the generative AI model can process the input appropriately.

[0649] A "generative AI model" is an algorithm or system that performs natural language processing and generates new expressions based on the input.

[0650] "Representation" refers to the format of text or information generated by a generative AI model.

[0651] Translation is the process of converting a generated expression into a different language.

[0652] "Correction" is the process of modifying the content of a generated expression and adjusting it to a more appropriate form.

[0653] "Storage" refers to the process of saving generated expressions to a database or similar system so that they can be referenced later.

[0654] A "server" is a computer system that receives input from users, generates expressions using a generative AI model, and then translates, corrects, and stores them.

[0655] One embodiment of this invention begins with a user inputting text into the system via a terminal. The user inputs an English sentence, for example, "I am angry." The terminal sends this input to the server.

[0656] The server preprocesses the received input data. Preprocessing involves formatting the data and removing unnecessary characters so that the generative AI model can process it appropriately. The preprocessed data is then input into the generative AI model. This generative AI model uses an algorithm for natural language processing, such as OpenAI's GPT-4.

[0657] The generative AI model generates new expressions based on the input. For example, for the input "I am angry", it generates a Japanese expression "私は怒っています". The generated expression is further translated and corrected as needed. In the correction process, the degree of emotion can be adjusted and corrected, for example, to "私は少し怒っています".

[0658] Finally, the server stores the generated expression in the database. This stored data is for the user to refer to later. The server sends the translated and corrected expression to the terminal, and the user can view the result through the terminal.

[0659] As a specific example, when the user inputs "I am angry", the system translates it to "私は怒っています" and further corrects it to "私は少し怒っています". This result is provided to the user and stored in the database.

[0660] An example of a prompt sentence is "Please translate the following English sentence into Japanese and adjust the degree of emotion: I am angry".

[0661] The flow of the specific process in Example 1 will be described using FIG. 11.

[0662] Step 1:

[0663] The user uses the terminal to input text into the system. For example, input an English sentence like "I am angry". The terminal sends this input to the server. The input is text data expressing the user's emotion.

[0664] Step 2:

[0665] The server preprocesses the input data received from the terminal. In preprocessing, the data is formatted and unnecessary characters are removed so that the generative AI model can process it appropriately. The input is raw data from the user, and the output is formatted text data.

[0666] Step 3:

[0667] The server inputs the preprocessed data into the generative AI model. This model performs natural language processing and generates new expressions based on the input. For example, for the input "I am angry", it generates the Japanese expression "私は怒っています". The input is formatted text data, and the output is the generated expression.

[0668] Step 4:

[0669] The server translates the generated expression. Using the generative AI model, the generated expression is converted into a different language. The input is the generated expression, and the output is the translated expression.

[0670] Step 5:

[0671] The server corrects the translated expression. Using the generative AI model, the expression is modified to adjust the degree of emotion. For example, "私は怒っています" is corrected to "私は少し怒っています". The input is the translated expression, and the output is the corrected expression.

[0672] Step 6:

[0673] The server stores the corrected expression in the database. The stored data is for the user to be able to refer to later. The input is the corrected expression, and the output is the data stored in the database.

[0674] Step 7:

[0675] The server sends the final expression to the terminal. The user can then view the translated and corrected results through the terminal. The input is the corrected expression, and the output is the result provided to the user.

[0676] (Application Example 1)

[0677] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0678] In today's information and communication environment, user-generated content is often multilingual and expresses diverse emotions. Therefore, it is necessary to appropriately translate this content, adjust the emotional intensity, and deliver it to other users. However, conventional systems struggle with real-time translation and emotional adjustment, hindering improvements in the user experience. Solving this problem is crucial.

[0679] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0680] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for translating user input in real time and adjusting the degree of emotion; and means for distributing the adjusted expressions to other users. This makes it possible to appropriately translate user-generated content into multiple languages, adjust the degree of emotion, and distribute it to other users in real time.

[0681] "Generative AI" is an artificial intelligence technology that generates expressions based on user input and performs translation, correction, and adjustment of emotional intensity.

[0682] "Translation" is the process of converting content expressed in one language into another language.

[0683] "Correction" is the process of correcting errors in generated expressions to make them accurate.

[0684] "Storage" refers to the process of recording generated expressions so that they can be referenced later.

[0685] "Real-time" refers to processing user input immediately and providing results instantly.

[0686] "Emotional intensity" is an indicator that shows the strength and type of emotion contained in the user's expression.

[0687] "Distribution" refers to the process of sending and sharing a generated expression with other users.

[0688] A "user" is an entity that uses the system to generate content and perform tasks such as translation and sentiment adjustment.

[0689] A "user" is the entity that receives the distributed content.

[0690] The system for implementing this invention has the functionality to translate, correct, and store user input using generative AI, and further adjust the degree of emotion before distributing it to other users. The server utilizes a generative AI model to receive user input and perform translation and emotion adjustment in real time. Specifically, the server receives user input as text data and sends prompts to the generative AI model. An example of a generative AI model used is OpenAI's GPT-3.

[0691] The server receives the translation and sentiment adjustment results returned from the generative AI model and stores them in a database. This stored data is referenced when distributing to other users. The terminal's role is to send the user's input to the server, receive the response from the server, and display it.

[0692] For example, if a user enters "I am very happy," the server sends the prompt "Translate and adjust the sentiment of the following text: 'I am very happy'" to the generative AI model. The generative AI model translates this to "I am very happy" and adjusts the degree of emotion to "I am happy." This result is stored by the server and distributed to other users.

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] The user enters text using a terminal. The entered text is sent from the terminal to the server. The input data is natural language text that includes the user's emotions and intentions.

[0696] Step 2:

[0697] The server generates a prompt message to send the received text to the AI ​​model. This prompt message instructs the model on how to process the input text. For example, it might be in the format, "Translate and adjust the sentiment of the following text: 'I am very happy'".

[0698] Step 3:

[0699] The server sends the generated prompt text to the generative AI model. The generative AI model translates the input text based on the prompt text and adjusts the degree of emotion. The input is the prompt text, and the output is the translated text and the adjusted emotion expression.

[0700] Step 4:

[0701] The server receives the translation and sentiment adjustment results returned by the generative AI model. The server stores these results in a database. The stored data is available for later reference and ready to be distributed to other users.

[0702] Step 5:

[0703] The server distributes stored translation and sentiment-adjusted results to other users. The terminal receives the response from the server and displays it to the user. This allows users to receive sentiment-adjusted content from other users in real time.

[0704] (Example 2)

[0705] Next, we will describe Example 2 of the morphological example. 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."

[0706] Conventional systems using generative artificial intelligence have not adequately considered the user's emotional state when generating expressions, making it difficult to provide appropriate communication to the user. Furthermore, the instructions for generating expressions that respond to emotions are insufficient, making it impossible to provide responses that match the user's feelings.

[0707] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0708] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for analyzing the user's emotional state; and means for giving instructions to the generative artificial intelligence based on the analysis results. This makes it possible to generate appropriate expressions according to the user's emotional state and provide better communication to the user.

[0709] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate expressions based on user input, and then translate, correct, and store them.

[0710] An "intermediary" is an entity that utilizes generative artificial intelligence to facilitate the smooth exchange of information between users and other systems or services.

[0711] A "user" is an entity that operates a system using generative artificial intelligence, inputs information, and receives the generated expression.

[0712] "Emotional state" refers to the type and intensity of emotions analyzed from the information entered by the user.

[0713] "Analysis" is the process of analyzing user input information to identify specific emotional states.

[0714] An "instruction" is a command or guideline given to a generative artificial intelligence system based on the analysis results, for generating a specific expression.

[0715] "Expression" refers to means of communication, such as text and speech, that generative artificial intelligence generates based on user input and emotional states.

[0716] A description of embodiments for carrying out this invention will be given.

[0717] The server provides a system that uses generative artificial intelligence to generate expressions that correspond to the user's emotional state. This system receives input from the user and uses sentiment analysis software to analyze that emotional state. Specific software examples include a "natural language understanding API" and a "sentiment analysis API." This software analyzes the user's input text and identifies their emotional state.

[0718] The server generates prompt statements and provides instructions to the generative artificial intelligence based on the analyzed emotional state. These prompt statements instruct the generative AI on what kind of expressions it should generate. For example, if the user is expressing anger, the server will generate a prompt statement such as, "If the user is angry, please generate expressions that will help them calm down."

[0719] Generative artificial intelligence generates appropriate expressions based on prompt text. Specifically, it uses a generative AI model (e.g., a natural language generation model) to generate text that corresponds to the user's emotional state. This generated expression is then sent to the user via the device to provide appropriate communication.

[0720] For example, if a user inputs "Why is it so slow!", the server uses emotion analysis software to identify the emotion as "anger." Then, it has a generative artificial intelligence system generate a calming message such as, "We apologize for the delay. We are doing our best to resolve the issue, so please wait a moment," and sends it to the user. In this way, it is possible to provide an appropriate response tailored to the user's emotional state.

[0721] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0722] Step 1:

[0723] The user enters text through the terminal. For example, they might type the message, "Why is it so slow!" This input data is then sent to the server.

[0724] Step 2:

[0725] The server passes the received text data to sentiment analysis software. Specifically, it uses natural language processing techniques to analyze the text and identify the emotional state. The input is the user's text data, and the output is an emotional state such as "anger."

[0726] Step 3:

[0727] The server generates prompt sentences to input into the generative AI model based on the analyzed emotional state. For example, it might create a prompt sentence such as, "If the user is angry, generate a calming expression." This prompt sentence becomes the input to the generative AI model.

[0728] Step 4:

[0729] The server sends a prompt to the generative AI model, which generates an expression that corresponds to the user's emotional state. The generative AI model then generates an appropriate response based on the prompt. The input is a prompt, and the output is an expression such as, "We apologize for the wait. We are doing our best to resolve the issue, so please wait a moment."

[0730] Step 5:

[0731] The server sends the generated expression to the terminal and displays it to the user. The user can then see the generated response on the terminal and receive appropriate communication that matches their emotions.

[0732] (Application Example 2)

[0733] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0734] In modern information and communication technology, there is a demand for appropriate communication that takes into account the emotional state of the user. However, conventional systems have struggled to accurately analyze the user's emotions and generate appropriate responses based on them. As a result, they have failed to alleviate the user's anxiety or anger, sometimes leading to decreased satisfaction.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0736] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for utilizing generative artificial intelligence as an intermediary; means for providing expressions generated by generative artificial intelligence to the user; means for analyzing the user's emotional state; and means for generating an appropriate response based on the emotional state. This enables appropriate communication that is in line with the user's emotions.

[0737] "Generative artificial intelligence" is an artificial intelligence technology that can generate expressions based on user input, and then translate, correct, and store them.

[0738] A "mediator" is an entity in which generative artificial intelligence plays a role in facilitating the exchange of information between the user and the system.

[0739] "User" refers to an individual or group that uses the system and is the entity that receives the expressions provided by the generative artificial intelligence.

[0740] "Emotional state" refers to the psychological state or emotions that a user exhibits in a specific situation, and is the subject of analysis by the system.

[0741] A "response" is an expression generated by a generative artificial intelligence system based on the user's emotional state, and serves as a means of communication with the user.

[0742] The system for carrying out this invention operates in a network environment including a server and terminals. The server processes user input using generative artificial intelligence and generates an appropriate response. Specifically, the server uses a sentiment analysis API (e.g., Google Cloud Natural Language API) to analyze the emotional state from the user's input text. Based on this analysis, a generative artificial intelligence model (e.g., OpenAI's GPT model) generates a response that corresponds to the user's emotions.

[0743] The terminal provides an interface for user input and displays responses from the server. When a user enters text into the terminal, that data is sent to the server for sentiment analysis and response generation. The generated response is then sent back to the terminal and displayed to the user.

[0744] For example, if a user enters "I'm worried about the recent security breach," the server uses a sentiment analysis API to determine that the user is "anxious." Based on this emotional state, a generative artificial intelligence model generates a response such as, "Please rest assured that our team monitors the system 24 / 7 and we have implemented the latest security measures." An example of a prompt would be, "The user is feeling anxious. Generate a reassuring response."

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The user enters text into the device. The entered text is sent to the server as data to analyze the user's emotional state.

[0748] Step 2:

[0749] The server sends the received text to a sentiment analysis API. The sentiment analysis API analyzes the content of the text and identifies the user's emotional state (e.g., anxiety, anger, joy, etc.). The analysis results are returned to the server.

[0750] Step 3:

[0751] The server receives the analysis results from the sentiment analysis API and creates a prompt message for the generative AI model. The prompt message contains instructions for generating an appropriate response based on the user's emotional state. For example, it might generate a prompt message such as, "The user is feeling anxious. Generate a reassuring response."

[0752] Step 4:

[0753] The server sends a prompt to the generative AI model, instructing it to generate a response that corresponds to the user's emotional state. The generative AI model generates an appropriate response based on the prompt and returns the result to the server.

[0754] Step 5:

[0755] The server sends the response received from the generated AI model to the terminal. The terminal displays this response to the user. The user can review the generated response and provide further input as needed.

[0756] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0757] "Example of form 1"

[0758] One embodiment of the present invention provides a system that combines an emotion engine. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI stores the generated and corrected expressions and provides them to the user.

[0759] "Example of form 2"

[0760] Another embodiment of the present invention provides a system that combines an emotion engine and a generative AI. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI provides the generated and corrected expression to the user. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0761] The following describes the processing flow for each example of the form.

[0762] "Example of form 1"

[0763] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[0764] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[0765] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[0766] Step 4: Store the expressions generated and corrected by the generative AI, and provide those expressions to the user.

[0767] "Example of form 2"

[0768] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[0769] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[0770] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[0771] Step 4: The generative AI provides the user with the generated and corrected expressions. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0772] (Example 1)

[0773] Next, we will describe Embodiment 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0774] Conventional information processing systems struggled to accurately translate users' natural language input and generate expressions that took emotional states into account. Furthermore, they lacked the functionality to correct and store generated expressions, making it impossible to provide users with appropriate feedback. This resulted in a failure to generate expressions that accurately reflected user intent, leading to a decline in the quality of communication.

[0775] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0776] In this invention, the server includes means for receiving natural language input using an information processing device, means for generating expressions based on the received natural language input using a generative AI model, and means for recognizing emotional states from the received natural language input using an emotion analysis device. This makes it possible to generate, translate, correct, and store appropriate expressions that take into account the emotional state based on the user's natural language input.

[0777] An "information processing device" is a device that receives natural language input from a user and sends the data to a server.

[0778] A "generative AI model" is an artificial intelligence model that generates expressions based on received natural language input and performs translation and correction.

[0779] An "emotion analysis device" is a device that recognizes a user's emotional state from the natural language input it receives.

[0780] "Natural language input" refers to text data in human language that a user inputs through an information processing device.

[0781] "Representation" refers to translated or corrected text data generated by a generative AI model based on user input.

[0782] "Translation" is the process of converting text expressed in one language into another language.

[0783] "Correction" is the process of adjusting the content of a generated expression and modifying it to a more appropriate form.

[0784] "Storage" refers to the process of saving generated expressions in a database or similar system so that they can be referenced later.

[0785] The following system configurations are possible as embodiments for carrying out this invention.

[0786] The user uses a terminal to input text in natural language. For example, the user might type "I am angry." This input is sent from the terminal to the server.

[0787] The server receives natural language input from the user using an information processing device. The received data is input as a prompt to a generative AI model. The generative AI model generates a representation based on the input natural language. In this process, an emotion analyzer is used to recognize the user's emotional state from the input. For example, from the input "I am angry," the AI ​​recognizes that the user is in an angry emotional state.

[0788] The generative AI model translates the input expression, taking into account the recognized emotional state, and makes corrections as needed. For example, it can translate "I am angry" into the Japanese expression "I am angry" and then adjust the degree of emotion to "I am a little angry."

[0789] The generated expressions are stored in a database by the server, allowing them to be referenced later. Finally, the server sends the generated expressions to the terminal, providing them to the user. The user can then view the generated expressions through the terminal.

[0790] As a concrete example, a possible prompt sentence to input into a generative AI model is, "Translate and adjust the expression 'I am angry' to Japanese, considering the emotional state as recognized by the emotion engine." This prompt sentence allows the system to perform appropriate translation and emotion adjustment.

[0791] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0792] Step 1:

[0793] The user uses a terminal to input text in natural language. For example, they might type "I am angry." This input is sent from the terminal to the server. The input data is natural language text that includes the user's emotional state.

[0794] Step 2:

[0795] The server uses an information processing device to receive natural language input sent from the terminal. The received data is input to the generative AI model as a prompt. Here, the input is the user's natural language text, and the output is a prompt to the generative AI model.

[0796] Step 3:

[0797] The server uses an emotion analysis device to recognize the user's emotional state from the received natural language input. For example, from an input of "I am angry", it recognizes that the user is in an angry emotional state. The input for this step is natural language text, and the output is the recognized emotional state.

[0798] Step 4:

[0799] The server uses a generative AI model to generate a new expression based on the input data while considering the recognized emotional state. Specifically, it translates "I am angry" into the Japanese expression "私は怒っています". The input for this step is the prompt sentence and the emotional state, and the output is the translated expression.

[0800] Step 5:

[0801] The server further corrects the generated expression and adjusts the degree of emotion. For example, it changes "私は怒っています" to "私は少し怒っています". The input for this step is the translated expression, and the output is the corrected expression.

[0802] Step 6:

[0803] The server stores the generated and corrected expressions in the database. This enables later reference. The input for this step is the corrected expression, and the output is the data stored in the database.

[0804] [[ID=2,8]]Step 7:

[0805] The server sends the final generated result to the terminal and provides it to the user. The user can view the generated expression through the terminal. The input for this step is the expression obtained from the database, and the output is the expression provided to the user.

[0806] (Application Example 1)

[0807] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0808] In today's information society, users have access to a vast amount of information, but finding information that is relevant to their emotions and circumstances is difficult. Furthermore, the lack of emotionally responsive information makes it difficult to increase user satisfaction.

[0809] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0810] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for analyzing the user's emotional state and recommending information based on that emotion; and means for providing the information in a format suitable for the user's emotion. This makes it possible to provide appropriate information according to the user's emotions.

[0811] "Generative AI" is an artificial intelligence technology that generates new expressions based on user input and performs translation and correction.

[0812] Translation is the process of converting content expressed in one language into another language.

[0813] "Correction" is the process of correcting errors or inappropriate parts of a generated expression.

[0814] "Storage" is the process of saving generated expressions and information so that they can be referenced later.

[0815] A "mediator" is a generative AI that acts as an intermediary between the user and the information, playing a role in generating and providing that information.

[0816] "Emotional state" refers to the psychological state or emotions inferred from the information entered by the user.

[0817] "Methods of recommending information" refer to the process of selecting and presenting appropriate information to the user based on their emotional state.

[0818] "Means of providing information in an appropriate format" refers to the process of presenting information in the most optimal format according to the user's emotions and circumstances.

[0819] The system for carrying out this invention includes a program that combines a generative AI and an emotion analysis engine to provide information that responds to the user's emotions. The server receives input from the user and recognizes the user's emotional state using the emotion analysis engine. Specifically, it extracts emotions from the input text using an emotion analysis API (e.g., an emotion analysis API).

[0820] Next, the server uses a generative AI model (e.g., a generative AI model) to generate appropriate information based on the recognized emotions. This information is provided in a format suitable for the user's emotions. For example, if the user enters "I am stressed," the emotion analysis API recognizes "stress," and the generative AI model recommends relaxing music or meditation guides.

[0821] The device displays information provided by the server to the user. This allows the user to easily obtain information that matches their emotions.

[0822] As a concrete example, the following is an example of a prompt: "If the user is feeling stressed, recommend relaxing content." By inputting this prompt into a generative AI model, it becomes possible to provide information tailored to the user's emotions.

[0823] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0824] Step 1:

[0825] The user enters text containing emotions through their device. This input is sent to the server. The input data is text information that indicates the user's emotions.

[0826] Step 2:

[0827] The server sends the received text to a sentiment analysis API to analyze the user's emotional state. The input is the user's text, and the output is the emotional state recognized by the sentiment analysis API. Specifically, the API analyzes the text and identifies the type of emotion (e.g., joy, sadness, stress).

[0828] Step 3:

[0829] The server inputs a prompt message into the generative AI model based on the emotional state obtained from the emotion analysis API. The input is the emotional state, and the output is information or content based on that emotion. Specifically, the generative AI model receives the prompt message and generates information appropriate to the user's emotions.

[0830] Step 4:

[0831] The server sends information obtained from the generated AI model to the terminal. The input is the generated information, and the output is the content provided to the user. Specifically, the server converts the information into an appropriate format and sends it to the user's terminal for display.

[0832] Step 5:

[0833] The terminal displays information received from the server to the user. The input is information sent from the server, and the output is content that the user can visually confirm. Specifically, the terminal displays the information on the screen, allowing the user to view it.

[0834] (Example 2)

[0835] Next, we will describe Example 2 of the morphological example. 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."

[0836] In modern communication, it is crucial to generate appropriate expressions that take into account the user's emotional state. However, conventional systems have struggled to accurately recognize user emotions and adjust expressions accordingly. As a result, they have been unable to provide appropriate feedback that responds to the user's emotions, leading to a decline in the quality of communication.

[0837] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0838] In this invention, the server includes means for receiving user input, means for recognizing the user's emotional state from the input, and means for generating expressions that take the emotional state into consideration. This makes it possible to generate appropriate expressions according to the user's emotional state and improve the quality of communication.

[0839] "Means for receiving user input" refers to a function that allows a server to acquire information entered by a user through a terminal.

[0840] "Means for recognizing a user's emotional state" refers to a function that analyzes user input information, extracts emotional information from it, and identifies the user's emotional state.

[0841] "Means for generating expressions" refers to a function that creates appropriate linguistic expressions based on the recognized emotional state of the user.

[0842] "Means of providing generated expressions to users" refers to functions for presenting generated linguistic expressions to users.

[0843] "Means for translating, correcting, and storing generated expressions" refers to functions for converting generated linguistic expressions into other languages, modifying them as necessary, and saving them for later reference.

[0844] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0845] A description of embodiments for carrying out this invention will be given.

[0846] This system uses a server, a terminal, and a generative AI model to generate expressions that take into account the user's emotional state. The user inputs text using the terminal, and this input is sent to the server. The server uses an emotion engine to recognize the user's emotional state from the input. The emotion engine utilizes natural language processing technology to analyze the input text and identify the user's emotions.

[0847] Next, the server uses generative AI to generate appropriate expressions based on the recognized emotional state. The generative AI translates the input language, adjusts the degree of emotion, and creates expressions that provide appropriate feedback to the user. The generated expressions are sent from the server to the terminal and provided to the user.

[0848] For example, if a user enters "I am angry," the emotion engine recognizes the emotion of anger. The generative AI takes this emotional state into consideration and generates the expression "I am a little angry." This expression adjusts the degree of emotion to help the user calm down.

[0849] An example of a prompt would be, "If a user provides input indicating anger, please explain how the generative AI generates a response."

[0850] In this way, the system enables appropriate communication tailored to the user's emotional state.

[0851] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0852] Step 1:

[0853] The user uses a terminal to input text. For example, they might type the sentence "I am angry." This input is sent from the terminal to the server. The input data is in text format, and the server receives it.

[0854] Step 2:

[0855] The server passes the received text to the emotion engine. The emotion engine uses natural language processing techniques to recognize the user's emotional state from the input text. Specifically, it analyzes the text, extracts keywords and context related to the emotion, and identifies that the user is feeling angry. The output is the recognized emotional state.

[0856] Step 3:

[0857] The server uses a generative AI to generate expressions based on the emotional state obtained from the emotion engine. The generative AI translates the input English sentence into Japanese and further adjusts the degree of emotion. Specifically, it converts "I am angry" to "I am a little angry." The output is the adjusted Japanese expression.

[0858] Step 4:

[0859] The server sends the generated expression to the terminal. The terminal displays this expression to the user. The user reviews the generated expression through the terminal and receives feedback. This allows the user to obtain an appropriate expression that matches their emotional state.

[0860] (Application Example 2)

[0861] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0862] There is a need to achieve appropriate communication that takes into account the user's emotional state. In particular, in security services, a swift and appropriate response that responds to the user's emotions is necessary, but conventional systems have the challenge of not being able to respond while taking emotions into account.

[0863] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0864] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for utilizing generative AI as an intermediary; means for providing expressions generated by generative AI to the user; means for analyzing the user's emotional state and generating an appropriate response based on that emotional state; and means for providing that response to the user. This enables appropriate communication that takes the user's emotional state into consideration.

[0865] A "generative AI" is an artificial intelligence system that generates expressions based on user input, and then translates, corrects, and stores them.

[0866] A "mediator" is a system that utilizes generative AI to facilitate communication between users and the system.

[0867] "Emotional state" refers to the psychological state analyzed from the user's input, and includes emotions such as anger and anxiety.

[0868] A "response" is an expression provided to the user, generated by a generative AI that takes the user's emotional state into consideration.

[0869] "Security services" are services designed to ensure the safety of users, and include emergency response and customer support.

[0870] The system for implementing this invention combines generative AI and an emotion analysis engine to achieve communication that takes into account the user's emotional state. The server receives input from the user and analyzes the user's emotional state using the emotion analysis engine. This analysis uses the Google Cloud Natural Language API. After the emotional state is identified, the server uses generative AI, specifically OpenAI's GPT model, to generate an appropriate response that corresponds to the user's emotions. The generated response is then provided to the user.

[0871] This system will be installed on devices such as smartphones and robots and used in customer support for security services. For example, if a user enters "The security alarm is malfunctioning," the server will detect the user's anxiety, and a generative AI will generate a response such as "Please rest assured. We will check it immediately."

[0872] Examples of prompt messages include the following:

[0873] User's emotional state: Anxiety

[0874] User input: Security alarm is malfunctioning.

[0875] Response generation prompt: Generate a response to reassure the user.

[0876] In this way, it becomes possible to communicate appropriately while taking into account the user's emotional state.

[0877] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0878] Step 1:

[0879] The user enters text through their device. The entered text is sent to the server as data to analyze the user's emotional state.

[0880] Step 2:

[0881] The server sends the received text to the Google Cloud Natural Language API for sentiment analysis. The input is the user's text, and the output is the emotional state (e.g., anxiety, anger). The server then passes this emotional state to the next processing step.

[0882] Step 3:

[0883] The server generates prompts that take the user's emotional state into account. These prompts are input to a generative AI model and contain instructions for generating responses that correspond to the user's emotions.

[0884] Step 4:

[0885] The server takes prompt text as input to a generative AI model (OpenAI's GPT model) and generates an appropriate response. The input is the prompt text, and the output is the response provided to the user.

[0886] Step 5:

[0887] The server sends the generated response to the terminal, providing it to the user. The user receives this response through the terminal and can gain a sense of reassurance.

[0888] (Other examples)

[0889] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0890] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0891] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0892] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0893] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0894] [Fourth Embodiment]

[0895] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0896] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0897] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0898] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0899] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0900] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0901] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0902] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0903] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0904] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0905] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0906] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0907] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0908] "Example of form 1"

[0909] One embodiment of the present invention is a system that translates, corrects, and stores expressions using generative AI. This system receives input from a user and uses generative AI to generate an expression based on that input. The generated expression is translated, corrected, and stored, and the results are provided to the user. For example, if a user inputs "I am angry" in English, the generative AI receives this input and translates it into the Japanese expression "I am angry." The generative AI can also correct this expression, adjusting the degree of emotion, for example, to "I am a little angry." Furthermore, the generative AI can store this expression for later reference.

[0910] "Form Example 2"

[0911] Also, as another embodiment of the present invention, there is a system in which the generative AI generates an expression in consideration of the user's emotional state. In this system, the generative AI analyzes the user's emotional state and generates an expression based on the result. For example, when the user inputs an expression indicating anger, the generative AI can generate an expression that calms the user considering that emotional state. In this way, the present invention realizes appropriate communication considering the user's emotional state.

[0912] The processing flow of each form example will be described below.

[0913] "Form Example 1"

[0914] Step 1: The system receives an input from the user. For example, the user inputs "I am angry" in English.

[0915] Step 2: The generative AI generates an expression based on the input from the user. In this example, it translates "I am angry" into a Japanese expression "私は怒っています".

[0916] Step 3: The generative AI corrects the generated expression. For example, it adjusts the degree of emotion like changing "私は怒っています" to "私は少し怒っています".

[0917] Step 4: The generative AI stores the generated and corrected expression. This expression can be referred to later.

[0918] Step 5: The system provides the expression generated by the generative AI to the user.

[0919] "Form Example 2"

[0920] Step 1: The system receives an input from the user. This input indicates the user's emotional state.

[0921] Step 2: Generative AI analyzes the user's emotional state.

[0922] Step 3: The generative AI generates expressions based on the analysis results. For example, if the user provides input indicating anger, the generative AI will consider that emotional state and generate expressions that will help the user calm down.

[0923] Step 4: The system provides the user with an expression generated by a generative AI. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[0924] (Example 1)

[0925] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0926] In today's information society, users are required to communicate smoothly between different languages. However, conventional translation systems have difficulty accurately conveying emotional nuances and are insufficient in generating expressions that take into account the user's emotional state. Furthermore, there is a lack of systems that can efficiently store and refer to the generated expressions at a later date.

[0927] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0928] In this invention, the server includes means for receiving input from a user, means for pre-processing said input, and means for generating expressions using a generative AI model. This enables the generation of natural expressions that take into account the user's emotional state, as well as translation, correction, and storage.

[0929] A "user" is an entity that provides input to a system and receives the generated representation.

[0930] "Input" refers to the text data and information that a user provides to the system.

[0931] "Preprocessing" is the process of shaping or transforming data so that the generative AI model can process the input appropriately.

[0932] A "generative AI model" is an algorithm or system that performs natural language processing and generates new expressions based on the input.

[0933] "Representation" refers to the format of text or information generated by a generative AI model.

[0934] Translation is the process of converting a generated expression into a different language.

[0935] "Correction" is the process of modifying the content of a generated expression and adjusting it to a more appropriate form.

[0936] "Storage" refers to the process of saving generated expressions to a database or similar system so that they can be referenced later.

[0937] A "server" is a computer system that receives input from users, generates expressions using a generative AI model, and then translates, corrects, and stores them.

[0938] One embodiment of this invention begins with a user inputting text into the system via a terminal. The user inputs an English sentence, for example, "I am angry." The terminal sends this input to the server.

[0939] The server preprocesses the received input data. Preprocessing involves formatting the data and removing unnecessary characters so that the generative AI model can process it appropriately. The preprocessed data is then input into the generative AI model. This generative AI model uses an algorithm for natural language processing, such as OpenAI's GPT-4.

[0940] The generation AI model generates new expressions based on the input. For example, for the input "I am angry", it generates the Japanese expression "私は怒っています". The generated expression is further translated and corrected as necessary. In the correction process, the degree of emotion can be adjusted, for example, it is corrected to "私は少し怒っています".

[0941] Finally, the server stores the generated expression in the database. This stored data is for the user to refer to later. The server sends the translated and corrected expression to the terminal, and the user can confirm the result through the terminal.

[0942] As a specific example, when the user inputs "I am angry", the system translates it as "私は怒っています" and further corrects it to "私は少し怒っています". This result is provided to the user and stored in the database.

[0943] An example of the prompt text is "Please translate the following English sentence into Japanese and adjust the degree of emotion: I am angry".

[0944] The flow of the specific process in Example 1 will be described using FIG.

[0945] Step 1:

[0946] The user uses the terminal to input text into the system. For example, the user inputs an English sentence such as "I am angry". The terminal sends this input to the server. The input is text data expressing the user's emotion.

[0947] Step 2:

[0948] The server preprocesses the input data received from the terminal. In the preprocessing, data formatting and removal of unnecessary characters are performed so that the generative AI model can process it appropriately. The input is raw data from the user, and the output is formatted text data.

[0949] Step 3:

[0950] The server inputs the preprocessed data into the generative AI model. This model performs natural language processing and generates new expressions based on the input. For example, for the input "I am angry", it generates a Japanese expression "私は怒っています". The input is formatted text data, and the output is the generated expression.

[0951] Step 4:

[0952] The server translates the generated expression. Using the generative AI model, the generated expression is converted into a different language. The input is the generated expression, and the output is the translated expression.

[0953] Step 5:

[0954] The server corrects the translated expression. Using the generative AI model, the expression is modified to adjust the degree of emotion. For example, "私は怒っています" is corrected to "私は少し怒っています". The input is the translated expression, and the output is the corrected expression.

[0955] Step 6:

[0956] The server stores the corrected expression in the database. The stored data is for the user to be able to refer to later. The input is the corrected expression, and the output is the data stored in the database.

[0957] Step 7:

[0958] The server sends the final expression to the terminal. The user can then view the translated and corrected results through the terminal. The input is the corrected expression, and the output is the result provided to the user.

[0959] (Application Example 1)

[0960] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0961] In today's information and communication environment, user-generated content is often multilingual and expresses diverse emotions. Therefore, it is necessary to appropriately translate this content, adjust the emotional intensity, and deliver it to other users. However, conventional systems struggle with real-time translation and emotional adjustment, hindering improvements in the user experience. Solving this problem is crucial.

[0962] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0963] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for translating user input in real time and adjusting the degree of emotion; and means for distributing the adjusted expressions to other users. This makes it possible to appropriately translate user-generated content into multiple languages, adjust the degree of emotion, and distribute it to other users in real time.

[0964] "Generative AI" is an artificial intelligence technology that generates expressions based on user input and performs translation, correction, and adjustment of emotional intensity.

[0965] "Translation" is the process of converting content expressed in one language into another language.

[0966] "Correction" is the process of correcting errors in generated expressions to make them accurate.

[0967] "Storage" refers to the process of recording generated expressions so that they can be referenced later.

[0968] "Real-time" refers to processing user input immediately and providing results instantly.

[0969] "Emotional intensity" is an indicator that shows the strength and type of emotion contained in the user's expression.

[0970] "Distribution" refers to the process of sending and sharing a generated expression with other users.

[0971] A "user" is an entity that uses the system to generate content and perform tasks such as translation and sentiment adjustment.

[0972] A "user" is the entity that receives the distributed content.

[0973] The system for implementing this invention has the functionality to translate, correct, and store user input using generative AI, and further adjust the degree of emotion before distributing it to other users. The server utilizes a generative AI model to receive user input and perform translation and emotion adjustment in real time. Specifically, the server receives user input as text data and sends prompts to the generative AI model. An example of a generative AI model used is OpenAI's GPT-3.

[0974] The server receives the translation and sentiment adjustment results returned from the generative AI model and stores them in a database. This stored data is referenced when distributing to other users. The terminal's role is to send the user's input to the server, receive the response from the server, and display it.

[0975] For example, if a user enters "I am very happy," the server sends the prompt "Translate and adjust the sentiment of the following text: 'I am very happy'" to the generative AI model. The generative AI model translates this to "I am very happy" and adjusts the degree of emotion to "I am happy." This result is stored by the server and distributed to other users.

[0976] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0977] Step 1:

[0978] The user enters text using a terminal. The entered text is sent from the terminal to the server. The input data is natural language text that includes the user's emotions and intentions.

[0979] Step 2:

[0980] The server generates a prompt message to send the received text to the AI ​​model. This prompt message instructs the model on how to process the input text. For example, it might be in the format, "Translate and adjust the sentiment of the following text: 'I am very happy'".

[0981] Step 3:

[0982] The server sends the generated prompt text to the generative AI model. The generative AI model translates the input text based on the prompt text and adjusts the degree of emotion. The input is the prompt text, and the output is the translated text and the adjusted emotion expression.

[0983] Step 4:

[0984] The server receives the translation and sentiment adjustment results returned by the generative AI model. The server stores these results in a database. The stored data is available for later reference and ready to be distributed to other users.

[0985] Step 5:

[0986] The server distributes stored translation and sentiment-adjusted results to other users. The terminal receives the response from the server and displays it to the user. This allows users to receive sentiment-adjusted content from other users in real time.

[0987] (Example 2)

[0988] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0989] Conventional systems using generative artificial intelligence have not adequately considered the user's emotional state when generating expressions, making it difficult to provide appropriate communication to the user. Furthermore, the instructions for generating expressions that respond to emotions are insufficient, making it impossible to provide responses that match the user's feelings.

[0990] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0991] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for analyzing the user's emotional state; and means for giving instructions to the generative artificial intelligence based on the analysis results. This makes it possible to generate appropriate expressions according to the user's emotional state and provide better communication to the user.

[0992] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate expressions based on user input, and then translate, correct, and store them.

[0993] An "intermediary" is an entity that utilizes generative artificial intelligence to facilitate the smooth exchange of information between users and other systems or services.

[0994] A "user" is an entity that operates a system using generative artificial intelligence, inputs information, and receives the generated expression.

[0995] "Emotional state" refers to the type and intensity of emotions analyzed from the information entered by the user.

[0996] "Analysis" is the process of analyzing user input information to identify specific emotional states.

[0997] An "instruction" is a command or guideline given to a generative artificial intelligence system based on the analysis results, for generating a specific expression.

[0998] "Expression" refers to means of communication, such as text and speech, that generative artificial intelligence generates based on user input and emotional states.

[0999] A description of embodiments for carrying out this invention will be given.

[1000] The server provides a system that uses generative artificial intelligence to generate expressions that correspond to the user's emotional state. This system receives input from the user and uses sentiment analysis software to analyze that emotional state. Specific software examples include a "natural language understanding API" and a "sentiment analysis API." This software analyzes the user's input text and identifies their emotional state.

[1001] The server generates prompt statements and provides instructions to the generative artificial intelligence based on the analyzed emotional state. These prompt statements instruct the generative AI on what kind of expressions it should generate. For example, if the user is expressing anger, the server will generate a prompt statement such as, "If the user is angry, please generate expressions that will help them calm down."

[1002] Generative artificial intelligence generates appropriate expressions based on prompt text. Specifically, it uses a generative AI model (e.g., a natural language generation model) to generate text that corresponds to the user's emotional state. This generated expression is then sent to the user via the device to provide appropriate communication.

[1003] For example, if a user inputs "Why is it so slow!", the server uses emotion analysis software to identify the emotion as "anger." Then, it has a generative artificial intelligence system generate a calming message such as, "We apologize for the delay. We are doing our best to resolve the issue, so please wait a moment," and sends it to the user. In this way, it is possible to provide an appropriate response tailored to the user's emotional state.

[1004] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1005] Step 1:

[1006] The user enters text through the terminal. For example, they might type the message, "Why is it so slow!" This input data is then sent to the server.

[1007] Step 2:

[1008] The server passes the received text data to sentiment analysis software. Specifically, it uses natural language processing techniques to analyze the text and identify the emotional state. The input is the user's text data, and the output is an emotional state such as "anger."

[1009] Step 3:

[1010] The server generates prompt sentences to input into the generative AI model based on the analyzed emotional state. For example, it might create a prompt sentence such as, "If the user is angry, generate a calming expression." This prompt sentence becomes the input to the generative AI model.

[1011] Step 4:

[1012] The server sends a prompt to the generative AI model, which generates an expression that corresponds to the user's emotional state. The generative AI model then generates an appropriate response based on the prompt. The input is a prompt, and the output is an expression such as, "We apologize for the wait. We are doing our best to resolve the issue, so please wait a moment."

[1013] Step 5:

[1014] The server sends the generated expression to the terminal and displays it to the user. The user can then see the generated response on the terminal and receive appropriate communication that matches their emotions.

[1015] (Application Example 2)

[1016] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1017] In modern information and communication technology, there is a demand for appropriate communication that takes into account the emotional state of the user. However, conventional systems have struggled to accurately analyze the user's emotions and generate appropriate responses based on them. As a result, they have failed to alleviate the user's anxiety or anger, sometimes leading to decreased satisfaction.

[1018] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1019] In this invention, the server includes means for translating, correcting, and storing expressions using generative artificial intelligence; means for utilizing generative artificial intelligence as an intermediary; means for providing expressions generated by generative artificial intelligence to the user; means for analyzing the user's emotional state; and means for generating an appropriate response based on the emotional state. This enables appropriate communication that is in line with the user's emotions.

[1020] "Generative artificial intelligence" is an artificial intelligence technology that can generate expressions based on user input, and then translate, correct, and store them.

[1021] A "mediator" is an entity in which generative artificial intelligence plays a role in facilitating the exchange of information between the user and the system.

[1022] "User" refers to an individual or group that uses the system and is the entity that receives the expressions provided by the generative artificial intelligence.

[1023] "Emotional state" refers to the psychological state or emotions that a user exhibits in a specific situation, and is the subject of analysis by the system.

[1024] A "response" is an expression generated by a generative artificial intelligence system based on the user's emotional state, and serves as a means of communication with the user.

[1025] The system for carrying out this invention operates in a network environment including a server and terminals. The server processes user input using generative artificial intelligence and generates an appropriate response. Specifically, the server uses a sentiment analysis API (e.g., Google Cloud Natural Language API) to analyze the emotional state from the user's input text. Based on this analysis, a generative artificial intelligence model (e.g., OpenAI's GPT model) generates a response that corresponds to the user's emotions.

[1026] The terminal provides an interface for user input and displays responses from the server. When a user enters text into the terminal, that data is sent to the server for sentiment analysis and response generation. The generated response is then sent back to the terminal and displayed to the user.

[1027] For example, if a user enters "I'm worried about the recent security breach," the server uses a sentiment analysis API to determine that the user is "anxious." Based on this emotional state, a generative artificial intelligence model generates a response such as, "Please rest assured that our team monitors the system 24 / 7 and we have implemented the latest security measures." An example of a prompt would be, "The user is feeling anxious. Generate a reassuring response."

[1028] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1029] Step 1:

[1030] The user enters text into the device. The entered text is sent to the server as data to analyze the user's emotional state.

[1031] Step 2:

[1032] The server sends the received text to a sentiment analysis API. The sentiment analysis API analyzes the content of the text and identifies the user's emotional state (e.g., anxiety, anger, joy, etc.). The analysis results are returned to the server.

[1033] Step 3:

[1034] The server receives the analysis results from the sentiment analysis API and creates a prompt message for the generative AI model. The prompt message contains instructions for generating an appropriate response based on the user's emotional state. For example, it might generate a prompt message such as, "The user is feeling anxious. Generate a reassuring response."

[1035] Step 4:

[1036] The server sends a prompt to the generative AI model, instructing it to generate a response that corresponds to the user's emotional state. The generative AI model generates an appropriate response based on the prompt and returns the result to the server.

[1037] Step 5:

[1038] The server sends the response received from the generated AI model to the terminal. The terminal displays this response to the user. The user can review the generated response and provide further input as needed.

[1039] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1040] "Example of form 1"

[1041] One embodiment of the present invention provides a system that combines an emotion engine. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI stores the generated and corrected expressions and provides them to the user.

[1042] "Example of form 2"

[1043] Another embodiment of the present invention provides a system that combines an emotion engine and a generative AI. This system receives input from a user, and the emotion engine recognizes the user's emotional state from that input. For example, if a user inputs the English phrase "I am angry," the emotion engine recognizes from that input that the user is in an angry emotional state. Next, the generative AI generates an expression considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry." Finally, the generative AI provides the generated and corrected expression to the user. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[1044] The following describes the processing flow for each example of the form.

[1045] "Example of form 1"

[1046] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[1047] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[1048] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[1049] Step 4: Store the expressions generated and corrected by the generative AI, and provide those expressions to the user.

[1050] "Example of form 2"

[1051] Step 1: Receive input from the user. For example, if the user enters the English phrase "I am angry," the system will receive that input.

[1052] Step 2: The emotion engine recognizes the user's emotional state. In this example, the emotion engine recognizes that the user is angry based on the input "I am angry".

[1053] Step 3: The generative AI generates expressions considering the user's emotional state recognized by the emotion engine. In this example, "I am angry" is translated into the Japanese expression "I am angry," and then the degree of emotion is adjusted to generate the expression "I am a little angry."

[1054] Step 4: The generative AI provides the user with the generated and corrected expressions. In this way, the present invention realizes appropriate communication that takes into account the user's emotional state.

[1055] (Example 1)

[1056] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1057] Conventional information processing systems struggled to accurately translate users' natural language input and generate expressions that took emotional states into account. Furthermore, they lacked the functionality to correct and store generated expressions, making it impossible to provide users with appropriate feedback. This resulted in a failure to generate expressions that accurately reflected user intent, leading to a decline in the quality of communication.

[1058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1059] In this invention, the server includes means for receiving natural language input using an information processing device, means for generating expressions based on the received natural language input using a generative AI model, and means for recognizing emotional states from the received natural language input using an emotion analysis device. This makes it possible to generate, translate, correct, and store appropriate expressions that take into account the emotional state based on the user's natural language input.

[1060] An "information processing device" is a device that receives natural language input from a user and sends the data to a server.

[1061] A "generative AI model" is an artificial intelligence model that generates expressions based on received natural language input and performs translation and correction.

[1062] An "emotion analysis device" is a device that recognizes a user's emotional state from the natural language input it receives.

[1063] "Natural language input" refers to text data in human language that a user inputs through an information processing device.

[1064] "Representation" refers to translated or corrected text data generated by a generative AI model based on user input.

[1065] "Translation" is the process of converting text expressed in one language into another language.

[1066] "Correction" is the process of adjusting the content of a generated expression and modifying it to a more appropriate form.

[1067] "Storage" refers to the process of saving generated expressions in a database or similar system so that they can be referenced later.

[1068] The following system configurations are possible as embodiments for carrying out this invention.

[1069] The user uses a terminal to input text in natural language. For example, the user might type "I am angry." This input is sent from the terminal to the server.

[1070] The server receives natural language input from the user using an information processing device. The received data is input as a prompt to a generative AI model. The generative AI model generates a representation based on the input natural language. In this process, an emotion analyzer is used to recognize the user's emotional state from the input. For example, from the input "I am angry," the AI ​​recognizes that the user is in an angry emotional state.

[1071] The generative AI model translates the input expression, taking into account the recognized emotional state, and makes corrections as needed. For example, it can translate "I am angry" into the Japanese expression "I am angry" and then adjust the degree of emotion to "I am a little angry."

[1072] The generated expressions are stored in a database by the server, allowing them to be referenced later. Finally, the server sends the generated expressions to the terminal, providing them to the user. The user can then view the generated expressions through the terminal.

[1073] As a concrete example, a possible prompt sentence to input into a generative AI model is, "Translate and adjust the expression 'I am angry' to Japanese, considering the emotional state as recognized by the emotion engine." This prompt sentence allows the system to perform appropriate translation and emotion adjustment.

[1074] The flow of the specific processing in Example 1 will be explained using Figure 15.

[1075] Step 1:

[1076] The user uses a terminal to input text in natural language. For example, they might type "I am angry." This input is sent from the terminal to the server. The input data is natural language text that includes the user's emotional state.

[1077] Step 2:

[1078] The server uses an information processing device to receive natural language input sent from the terminal. The received data is input to the generative AI model as a prompt. Here, the input is the user's natural language text, and the output is a prompt to the generative AI model.

[1079] Step 3:

[1080] The server uses an emotion analysis device to recognize the user's emotional state from the received natural language input. For example, from the input "I am angry", it recognizes that the user is in an angry emotional state. The input for this step is natural language text, and the output is the recognized emotional state.

[1081] Step 4:

[1082] The server uses a generative AI model to generate a new expression based on the input data while considering the recognized emotional state. Specifically, it translates "I am angry" into the Japanese expression "私は怒っています". The input for this step is the prompt sentence and the emotional state, and the output is the translated expression.

[1083] Step 5:

[1084] The server further corrects the generated expression and adjusts the degree of emotion. For example, it changes "私は怒っています" to "私は少し怒っています". The input for this step is the translated expression, and the output is the corrected expression.

[1085] Step 6:

[1086] The server stores the generated and corrected expression in the database. This enables it to be referenced later. The input for this step is the corrected expression, and the output is the data stored in the database. [[ID=2,6]]

[1087] Step 7:

[1088] The server sends the final generated result to the terminal and provides it to the user. The user can view the generated expression through the terminal. The input for this step is the expression obtained from the database, and the output is the expression provided to the user.

[1089] (Application Example 1)

[1090] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1091] In today's information society, users have access to a vast amount of information, but finding information that is relevant to their emotions and circumstances is difficult. Furthermore, the lack of emotionally responsive information makes it difficult to increase user satisfaction.

[1092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1093] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for analyzing the user's emotional state and recommending information based on that emotion; and means for providing the information in a format suitable for the user's emotion. This makes it possible to provide appropriate information according to the user's emotions.

[1094] "Generative AI" is an artificial intelligence technology that generates new expressions based on user input and performs translation and correction.

[1095] Translation is the process of converting content expressed in one language into another language.

[1096] "Correction" is the process of correcting errors or inappropriate parts of a generated expression.

[1097] "Storage" is the process of saving generated expressions and information so that they can be referenced later.

[1098] A "mediator" is a generative AI that acts as an intermediary between the user and the information, playing a role in generating and providing that information.

[1099] "Emotional state" refers to the psychological state or emotions inferred from the information entered by the user.

[1100] "Methods of recommending information" refer to the process of selecting and presenting appropriate information to the user based on their emotional state.

[1101] "Means of providing information in an appropriate format" refers to the process of presenting information in the most optimal format according to the user's emotions and circumstances.

[1102] The system for carrying out this invention includes a program that combines a generative AI and an emotion analysis engine to provide information that responds to the user's emotions. The server receives input from the user and recognizes the user's emotional state using the emotion analysis engine. Specifically, it extracts emotions from the input text using an emotion analysis API (e.g., an emotion analysis API).

[1103] Next, the server uses a generative AI model (e.g., a generative AI model) to generate appropriate information based on the recognized emotions. This information is provided in a format suitable for the user's emotions. For example, if the user enters "I am stressed," the emotion analysis API recognizes "stress," and the generative AI model recommends relaxing music or meditation guides.

[1104] The device displays information provided by the server to the user. This allows the user to easily obtain information that matches their emotions.

[1105] As a concrete example, the following is an example of a prompt: "If the user is feeling stressed, recommend relaxing content." By inputting this prompt into a generative AI model, it becomes possible to provide information tailored to the user's emotions.

[1106] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[1107] Step 1:

[1108] The user enters text containing emotions through their device. This input is sent to the server. The input data is text information that indicates the user's emotions.

[1109] Step 2:

[1110] The server sends the received text to a sentiment analysis API to analyze the user's emotional state. The input is the user's text, and the output is the emotional state recognized by the sentiment analysis API. Specifically, the API analyzes the text and identifies the type of emotion (e.g., joy, sadness, stress).

[1111] Step 3:

[1112] The server inputs a prompt message into the generative AI model based on the emotional state obtained from the emotion analysis API. The input is the emotional state, and the output is information or content based on that emotion. Specifically, the generative AI model receives the prompt message and generates information appropriate to the user's emotions.

[1113] Step 4:

[1114] The server sends information obtained from the generated AI model to the terminal. The input is the generated information, and the output is the content provided to the user. Specifically, the server converts the information into an appropriate format and sends it to the user's terminal for display.

[1115] Step 5:

[1116] The terminal displays information received from the server to the user. The input is information sent from the server, and the output is content that the user can visually confirm. Specifically, the terminal displays the information on the screen, allowing the user to view it.

[1117] (Example 2)

[1118] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1119] In modern communication, it is crucial to generate appropriate expressions that take into account the user's emotional state. However, conventional systems have struggled to accurately recognize user emotions and adjust expressions accordingly. As a result, they have been unable to provide appropriate feedback that responds to the user's emotions, leading to a decline in the quality of communication.

[1120] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1121] In this invention, the server includes means for receiving user input, means for recognizing the user's emotional state from the input, and means for generating expressions that take the emotional state into consideration. This makes it possible to generate appropriate expressions according to the user's emotional state and improve the quality of communication.

[1122] "Means for receiving user input" refers to a function that allows a server to acquire information entered by a user through a terminal.

[1123] "Means for recognizing a user's emotional state" refers to a function that analyzes user input information, extracts emotional information from it, and identifies the user's emotional state.

[1124] "Means for generating expressions" refers to a function that creates appropriate linguistic expressions based on the recognized emotional state of the user.

[1125] "Means of providing generated expressions to users" refers to functions for presenting generated linguistic expressions to users.

[1126] "Means for translating, correcting, and storing generated expressions" refers to functions for converting generated linguistic expressions into other languages, modifying them as necessary, and saving them for later reference.

[1127] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[1128] A description of embodiments for carrying out this invention will be given.

[1129] This system uses a server, a terminal, and a generative AI model to generate expressions that take into account the user's emotional state. The user inputs text using the terminal, and this input is sent to the server. The server uses an emotion engine to recognize the user's emotional state from the input. The emotion engine utilizes natural language processing technology to analyze the input text and identify the user's emotions.

[1130] Next, the server uses generative AI to generate appropriate expressions based on the recognized emotional state. The generative AI translates the input language, adjusts the degree of emotion, and creates expressions that provide appropriate feedback to the user. The generated expressions are sent from the server to the terminal and provided to the user.

[1131] For example, if a user enters "I am angry," the emotion engine recognizes the emotion of anger. The generative AI takes this emotional state into consideration and generates the expression "I am a little angry." This expression adjusts the degree of emotion to help the user calm down.

[1132] An example of a prompt would be, "If a user provides input indicating anger, please explain how the generative AI generates a response."

[1133] In this way, the system enables appropriate communication tailored to the user's emotional state.

[1134] The flow of the specific processing in Example 2 will be explained using Figure 17.

[1135] Step 1:

[1136] The user uses a terminal to input text. For example, they might type the sentence "I am angry." This input is sent from the terminal to the server. The input data is in text format, and the server receives it.

[1137] Step 2:

[1138] The server passes the received text to the emotion engine. The emotion engine uses natural language processing techniques to recognize the user's emotional state from the input text. Specifically, it analyzes the text, extracts keywords and context related to the emotion, and identifies that the user is feeling angry. The output is the recognized emotional state.

[1139] Step 3:

[1140] The server uses a generative AI to generate expressions based on the emotional state obtained from the emotion engine. The generative AI translates the input English sentence into Japanese and further adjusts the degree of emotion. Specifically, it converts "I am angry" to "I am a little angry." The output is the adjusted Japanese expression.

[1141] Step 4:

[1142] The server sends the generated expression to the terminal. The terminal displays this expression to the user. The user reviews the generated expression through the terminal and receives feedback. This allows the user to obtain an appropriate expression that matches their emotional state.

[1143] (Application Example 2)

[1144] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1145] There is a need to achieve appropriate communication that takes into account the user's emotional state. In particular, in security services, a swift and appropriate response that responds to the user's emotions is necessary, but conventional systems have the challenge of not being able to respond while taking emotions into account.

[1146] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1147] In this invention, the server includes means for translating, correcting, and storing expressions using generative AI; means for utilizing generative AI as an intermediary; means for providing expressions generated by generative AI to the user; means for analyzing the user's emotional state and generating an appropriate response based on that emotional state; and means for providing that response to the user. This enables appropriate communication that takes the user's emotional state into consideration.

[1148] A "generative AI" is an artificial intelligence system that generates expressions based on user input, and then translates, corrects, and stores them.

[1149] A "mediator" is a system that utilizes generative AI to facilitate communication between users and the system.

[1150] "Emotional state" refers to the psychological state analyzed from the user's input, and includes emotions such as anger and anxiety.

[1151] A "response" is an expression provided to the user, generated by a generative AI that takes the user's emotional state into consideration.

[1152] "Security services" are services designed to ensure the safety of users, and include emergency response and customer support.

[1153] The system for implementing this invention combines generative AI and an emotion analysis engine to achieve communication that takes into account the user's emotional state. The server receives input from the user and analyzes the user's emotional state using the emotion analysis engine. This analysis uses the Google Cloud Natural Language API. After the emotional state is identified, the server uses generative AI, specifically OpenAI's GPT model, to generate an appropriate response that corresponds to the user's emotions. The generated response is then provided to the user.

[1154] This system will be installed on devices such as smartphones and robots and used in customer support for security services. For example, if a user enters "The security alarm is malfunctioning," the server will detect the user's anxiety, and a generative AI will generate a response such as "Please rest assured. We will check it immediately."

[1155] Examples of prompt messages include the following:

[1156] User's emotional state: Anxiety

[1157] User input: Security alarm is malfunctioning.

[1158] Response generation prompt: Generate a response to reassure the user.

[1159] In this way, it becomes possible to communicate appropriately while taking into account the user's emotional state.

[1160] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[1161] Step 1:

[1162] The user enters text through their device. The entered text is sent to the server as data to analyze the user's emotional state.

[1163] Step 2:

[1164] The server sends the received text to the Google Cloud Natural Language API for sentiment analysis. The input is the user's text, and the output is the emotional state (e.g., anxiety, anger). The server then passes this emotional state to the next processing step.

[1165] Step 3:

[1166] The server generates prompts that take the user's emotional state into account. These prompts are input to a generative AI model and contain instructions for generating responses that correspond to the user's emotions.

[1167] Step 4:

[1168] The server takes prompt text as input to a generative AI model (OpenAI's GPT model) and generates an appropriate response. The input is the prompt text, and the output is the response provided to the user.

[1169] Step 5:

[1170] The server sends the generated response to the terminal, providing it to the user. The user receives this response through the terminal and can gain a sense of reassurance.

[1171] (Other examples)

[1172] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1174] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1175] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1176] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1178] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[1185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1186] 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.

[1187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1194] The following is further disclosed regarding the embodiments described above.

[1195] (Claim 1)

[1196] A system including means for translating, correcting, and storing expressions using generative AI, means for utilizing the generative AI as an intermediary, and means for providing the expressions generated by the generative AI to a user.

[1197] (Claim 2)

[1198] The system according to claim 1, further comprising means for the generative AI to generate expressions based on user input, and for translating, correcting, and storing the expressions.

[1199] (Claim 3)

[1200] The system according to claim 1, further comprising means for the generative AI to generate expressions considering the user's emotional state, and for translating, correcting, and storing the expressions.

[1201] (Claim 4)

[1202] The system according to claim 1, further comprising means for the generative AI to generate expressions considering the user's emotional state recognized by the emotion engine, and for translating, correcting, and storing the expressions.

[1203] (Claim 5)

[1204] The system according to claim 4, further comprising means for an emotion engine to recognize an emotional state based on user input and provide the emotional state to a generative AI.

[1205] (Claim 6)

[1206] The system according to claim 5, further comprising means for the generative AI to generate an expression considering the user's emotional state recognized by the emotion engine and to provide the expression to the user.

[1207] "Example 1"

[1208] (Claim 1)

[1209] A means of receiving input from the user,

[1210] Means for preprocessing the input,

[1211] A means of generating representations using a generative AI model,

[1212] Means for translating the generated expression,

[1213] Means for correcting the translated expression,

[1214] Means for storing the corrected expression,

[1215] Means for providing the stored representation to the user,

[1216] A system that includes this.

[1217] (Claim 2)

[1218] The system according to claim 1, further comprising a generative AI model that generates expressions based on user input, and means for translating, correcting, and storing the expressions.

[1219] (Claim 3)

[1220] The system according to claim 1, further comprising means for a generative AI model to generate expressions taking into account the user's emotional state, and for translating, correcting, and storing the expressions.

[1221] "Application Example 1"

[1222] (Claim 1)

[1223] A means for translating, correcting, and storing expressions using generative AI,

[1224] A means of utilizing the said generation AI as an intermediary,

[1225] A means for providing the user with the expression generated by the generation AI,

[1226] A means of translating user input in real time and adjusting the degree of emotion,

[1227] A means for distributing the adjusted expression to other users,

[1228] A system that includes this.

[1229] (Claim 2)

[1230] The system according to claim 1, further comprising means for the generative AI to generate expressions based on user input, and for translating, correcting, and storing the expressions.

[1231] (Claim 3)

[1232] The system according to claim 1, wherein the generative AI generates expressions considering the user's emotional state, translates, corrects, and stores the expressions, and further distributes the expressions to other users.

[1233] Example 2

[1234] (Claim 1)

[1235] A means for translating, correcting, and storing expressions using generative artificial intelligence,

[1236] A means of utilizing the said generative artificial intelligence as an intermediary,

[1237] A means for providing users with expressions generated by the said generative artificial intelligence,

[1238] A means of analyzing the emotional state of users,

[1239] A means for giving instructions to a generative artificial intelligence based on the analysis results,

[1240] A means for generating an expression corresponding to the user's emotional state based on the said instruction,

[1241] A means for transmitting the generated expression to the user,

[1242] A system that includes this.

[1243] (Claim 2)

[1244] The system according to claim 1, further comprising means for the generative artificial intelligence to generate expressions based on input from a user, and for translating, correcting, and storing the expressions.

[1245] (Claim 3)

[1246] The system according to claim 1, further comprising means for generating expressions while taking into account the emotional state of the user, and for translating, correcting, and storing the expressions.

[1247] "Application Example 2"

[1248] (Claim 1)

[1249] A means for translating, correcting, and storing expressions using generative artificial intelligence,

[1250] A means of utilizing the said generative artificial intelligence as an intermediary,

[1251] A means for providing users with expressions generated by the said generative artificial intelligence,

[1252] A means of analyzing the emotional state of users,

[1253] Means for generating an appropriate response based on the emotional state,

[1254] A system that includes this.

[1255] (Claim 2)

[1256] The system according to claim 1, further comprising means for the generative artificial intelligence to generate expressions based on input from a user, and for translating, correcting, and storing the expressions.

[1257] (Claim 3)

[1258] The system according to claim 1, further comprising means for generating expressions while taking into account the emotional state of the user, and for translating, correcting, and storing the expressions.

[1259] "Example 1 of combining an emotion engine"

[1260] (Claim 1)

[1261] A means for receiving natural language input using an information processing device,

[1262] A means for generating representations based on received natural language input using a generative AI model,

[1263] A means for recognizing an emotional state from received natural language input using an emotion analysis device,

[1264] A means of translating, correcting, and storing expressions while considering recognized emotional states using a generative AI model,

[1265] A means for transmitting the generated representation to an information processing device and providing it to the user,

[1266] A system that includes this.

[1267] (Claim 2)

[1268] The system according to claim 1, further comprising a generative AI model that generates expressions based on natural language input from a user, and means for translating, correcting, and storing the expressions.

[1269] (Claim 3)

[1270] The system according to claim 1, further comprising means for generating an AI model that generates an expression considering the user's emotional state recognized by an emotion analysis device, and for translating, correcting, and storing the expression.

[1271] "Application example 1 of combining emotional engines"

[1272] (Claim 1)

[1273] A means for translating, correcting, and storing expressions using generative AI,

[1274] A means of utilizing the said generation AI as an intermediary,

[1275] A means for providing the user with the expression generated by the generation AI,

[1276] A means of analyzing the user's emotional state and recommending information based on that emotion,

[1277] A means of providing the information in a way that is appropriate to the user's emotions,

[1278] A system that includes this.

[1279] (Claim 2)

[1280] The system according to claim 1, further comprising means for the generative AI to generate expressions based on user input, and for translating, correcting, and storing the expressions.

[1281] (Claim 3)

[1282] The system according to claim 1, further comprising means for the generative AI to generate expressions considering the user's emotional state, and for translating, correcting, and storing the expressions.

[1283] "Example 2 of combining an emotion engine"

[1284] (Claim 1)

[1285] A means of receiving user input,

[1286] A means for recognizing the user's emotional state from the input,

[1287] Means for generating expressions that take the emotional state into consideration,

[1288] Means for providing the generated expression to the user,

[1289] Means for translating, correcting, and storing the generated expression,

[1290] A system that includes this.

[1291] (Claim 2)

[1292] The system according to claim 1, further comprising means for adjusting the generated expression according to the user's emotional state.

[1293] (Claim 3)

[1294] The system according to claim 1, wherein the means for recognizing the user's emotional state further includes means for using natural language processing technology.

[1295] "Application example 2 when combining with an emotional engine"

[1296] (Claim 1)

[1297] A means for translating, correcting, and storing expressions using generative AI,

[1298] A means of utilizing the said generation AI as an intermediary,

[1299] A means for providing the user with the expression generated by the generation AI,

[1300] A means for analyzing the user's emotional state and generating an appropriate response based on that emotional state,

[1301] Means for providing the response to the user,

[1302] A system that includes this.

[1303] (Claim 2)

[1304] The system according to claim 1, further comprising means for the generative AI to generate expressions based on user input, and for translating, correcting, and storing the expressions.

[1305] (Claim 3)

[1306] The system according to claim 1, further comprising means for the generative AI to generate expressions considering the user's emotional state, and for translating, correcting, and storing the expressions. [Explanation of Symbols]

[1307] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Equipped with a processor, The aforementioned processor, Receive input from the user, From the input, recognize the user's emotional state. The input is preprocessed, Based on the pre-processed input, a prompt statement is generated to instruct the generating AI model to generate a text representation. The prompt text is input into the AI ​​model to generate text, Translate the generated text, Based on the recognized emotional state, the translated text is modified to reduce the degree of emotion in the translated text. The corrected text is stored in the database. The saved text is provided to the user. system.

2. The system according to claim 1, wherein the processor generates a prompt sentence for instructing the generating AI model to generate text in response to an input prompt using a neural network.

3. The system according to claim 1, wherein the processor generates a prompt statement taking into account the user's emotional state and instructs the system to generate text corresponding to the emotion.

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