System

Abstract

A system is provided.SOLUTION: A personal assistant system comprising: means for receiving a natural language input from a user; means for converting the received natural language input into a text by speech recognition; means for analyzing the text to determine an intention of the user; means for analyzing an emotional state of the user from the text; means for generating an AI response based on the intention and the emotional state of the user; means for transmitting the generated AI response to a terminal of the user; and means for displaying the response on the terminal of the user or reproducing the response by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Loneliness and stress remain serious problems in modern society. In particular, people need emotional support while physical interactions are declining. The present invention aims to solve the problem of conventional personal assistants being unable to fully understand users' emotions and intentions, and to provide a system that can provide emotional support to users. [Means for solving the problem]

[0005] The present invention is a personal assistant system that includes a means for accepting natural language input from a user and converting it into text through speech recognition, a natural language processing means for analyzing the text and determining the user's intention, an emotion analysis means for analyzing the user's emotional state from the text, and an AI generation means for generating a response based on the user's intention and emotional state. The system sends the generated response to the user's terminal and displays or plays back the response aloud on the terminal, allowing the user to receive emotional support.

[0006] "Natural language input" refers to understandable language data provided by a user in the form of speech or text.

[0007] "Speech recognition" refers to the technology of analyzing voice data and converting it into text data.

[0008] "Text" refers to data consisting of characters generated by voice recognition or direct input.

[0009] "Natural language processing" refers to technology for analyzing text data and understanding its intent and meaning.

[0010] "Sentiment analysis" refers to the technology of extracting emotional elements from text and assessing the user's emotional state.

[0011] "Generative AI" refers to artificial intelligence techniques that generate natural responses based on given input data.

[0012] A "response" refers to a reply message generated in response to an input from a user.

[0013] "Terminal" refers to an electronic device used by a user that accepts voice or text input and displays or plays responses. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[0036] System program processing

[0037] Request reception

[0038] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[0039] Voice Recognition

[0040] The server uses a speech recognition system to convert the received voice data into text data. In this case, the server generates the text "I've been tired at work lately" from the voice.

[0041] Natural Language Processing

[0042] The server passes the transferred text to a natural language processing engine to analyze the user's intent. In this case, the NLP model analyzes the intent as "I feel tired and stressed."

[0043] Emotion analysis

[0044] The server evaluates the user's emotional state from this text and extracts emotional elements from the text, in this case identifying the emotions of "fatigue" and "stress."

[0045] Response Generation

[0046] The server uses generative AI to generate an appropriate response based on the user's intent and emotional state, generating a mentor message like, "Maybe you need a little break. Let's take a deep breath and relax."

[0047] Response Send

[0048] The server generates a response and sends it to the user's device. In this case, the response is sent back to the smartphone.

[0049] Response Display

[0050] The user's device (smartphone) receives the response and displays it on the screen as text or uses speech synthesis to communicate it to the user. The user can confirm the message by looking at it on the screen or by listening to it.

[0051] Specific examples

[0052] Example 1: When a user is stressed

[0053] (situation)

[0054] The user says to their smartphone, "I've been feeling tired at work lately."

[0055] (process)

[0056] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0057] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[0058] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[0059] 4. Emotion analysis: The server analyzes the emotions "fatigue" and "stress."

[0060] 5. Response generation: The server uses a generative AI to generate a response such as, "Maybe you need a little break. Shall we take a deep breath and take a moment to relax?"

[0061] 6. Send response: The server sends the response to the smartphone.

[0062] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0063] Example 2: User seeking advice

[0064] (situation)

[0065] A user says, "I want to start a new hobby. What should I do?"

[0066] (process)

[0067] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0068] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[0069] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[0070] 4. Sentiment analysis: The server analyzes the text for the emotional elements of "curiosity" and "expectation."

[0071] 5. Response generation: The server uses a generation AI to generate a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[0072] 6. Send response: The server sends the response to the smartphone.

[0073] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0074] In this way, the personal assistant system of the present invention generates an appropriate response to a user's natural language input and is used to reduce the user's sense of loneliness and stress.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[0078] Step 2:

[0079] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[0080] Step 3:

[0081] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[0082] Step 4:

[0083] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[0084] Step 5:

[0085] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this case, the emotions "fatigue" and "stress" are analyzed.

[0086] Step 6:

[0087] The server uses generative AI to generate a response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[0088] Step 7:

[0089] The server sends the generated response to the user's device (smartphone). Again, the generated text response is transferred to the smartphone via the network.

[0090] Step 8:

[0091] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[0092] Step 9:

[0093] The user sees a message displayed on the device or hears a spoken response. For example, the user reads or hears the message, "You might need a little break. Would you like to take a moment to take a deep breath and relax?"

[0094] In this way, through each of the above steps, the LifeMentor system stays close to the user's heart and provides appropriate responses.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] In modern society, users often feel lonely and stressed, and appropriate support is needed to address these issues. Conventional personal assistant systems face the challenge of being unable to fully understand a user's emotions and intentions and provide appropriate responses. Therefore, a system that can accurately analyze a user's emotions and intentions and generate appropriate responses based on those analysis is needed.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generative AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for transmitting received speech data to a speech recognition service, means for transmitting text data acquired from the speech recognition service to a natural language processing engine, means for transmitting intention information acquired from the natural language processing engine to an emotion analysis engine, means for generating a prompt sentence based on the emotion information acquired from the emotion analysis engine and inputting it to a generative AI model, and means for transmitting response data acquired from the generative AI model to the user's terminal. This makes it possible to accurately analyze the user's emotions and intentions and provide an appropriate response based on the analysis.

[0100] "Natural language input" refers to natural language, such as the words spoken or written by a user.

[0101] "Speech recognition" refers to the technology of analyzing speech and converting it into text data.

[0102] "Text data" refers to character data converted by speech recognition.

[0103] "Natural language processing" refers to the technology of analyzing text data to understand its meaning and intent.

[0104] "Emotion analysis" refers to the technology of identifying a user's emotions and psychological state from text data.

[0105] "Generative AI" refers to artificial intelligence that generates responses based on the user's intentions and emotional state.

[0106] A "prompt sentence" refers to an instruction sentence to be input into the generation AI.

[0107] "Terminal" refers to a device that a user directly operates (e.g., a smartphone or tablet).

[0108] "Speech Recognition Service" refers generally to any external cloud-based or on-premise service that provides speech recognition.

[0109] A "natural language processing engine" refers to software or a system in general that analyzes text data and understands the user's intent.

[0110] "Sentiment analysis engine" refers to software or a system in general that analyzes text data to identify a user's emotions.

[0111] "Generative AI model" refers to any artificial intelligence model that generates a response based on an input prompt.

[0112] "Response Data" refers to the text data of the response generated by the generative AI model.

[0113] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress. By combining generative AI and an emotion engine, we provide a system that generates responses that are close to the user's heart.

[0114] Specific system configuration

[0115] Hardware and Software

[0116] This system operates using the following hardware and software.

[0117] User device: smartphone, tablet, or computer

[0118] Server: Cloud-based or on-premise server

[0119] Speech Recognition Service: An external cloud service for converting voice input into text (e.g., Google® Speech-to-Text API)

[0120] Natural language processing engine: Software for analyzing text data (e.g., IBM Watson® NLP)

[0121] Sentiment analysis engine: Software for identifying emotions (e.g., Microsoft® Azure® Cognitive Services Text Analytics)

[0122] Generative AI model: artificial intelligence for generating responses (e.g., OpenAI® GPT-4®)

[0123] System operation explanation

[0124] Request reception

[0125] When a user speaks natural language into a smartphone, such as "I'm tired from work these days," the device captures the speech and transmits it to a server as voice data. The device is equipped with an appropriate microphone and communication interface to accept voice input.

[0126] Voice Recognition

[0127] The server sends the received voice data to a speech recognition service and converts the voice data into text data. For example, by using the Google Speech-to-Text API, speech can be converted into text with high accuracy.

[0128] Natural Language Processing

[0129] The server then sends the converted text data to a natural language processing engine to analyze the user's intent. Here, the IBM Watson NLP engine is used to identify the user's specific intent (e.g., "I feel tired and stressed") from the text data.

[0130] Emotion analysis

[0131] The server sends the parsed intent information to a sentiment analysis engine, which uses Microsoft Azure Cognitive Services Text Analytics to identify emotional elements (e.g., "fatigue" and "stress") contained within the text.

[0132] Response Generation

[0133] The server generates a prompt based on the identified intent and emotional information and inputs it into the generative AI model. An example of a prompt is, "The user is tired from work. Please generate a response recommending a break." OpenAI GPT-4 is used to generate a response, and an appropriate response is obtained, for example, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[0134] Response Send

[0135] The server sends the generated response data to the user's terminal, which receives the response and notifies the user.

[0136] Response Display

[0137] The device will display the received response as text on the screen or play it aloud using a speech synthesis function, allowing the user to confirm the response visually or audibly.

[0138] Specific examples

[0139] Example 1: When a user is stressed

[0140] User: "I've been feeling tired at work lately," says the smartphone.

[0141] Server: Receives the voice data and sends it to the voice recognition service.

[0142] Server: Obtain the text data "I've been tired at work lately."

[0143] Server: Sends text data to a natural language processing engine and analyzes intent.

[0144] Server: Sends intent information to the emotion analysis engine to identify emotions of "fatigue" and "stress."

[0145] Server: Generates a prompt sentence, "The user is tired from work. Please generate a response recommending a break." and inputs it into the generative AI model.

[0146] Server: The response obtained from GPT-4, "Maybe you need a break. Would you like to take a deep breath and relax?", is sent to the user's device.

[0147] Terminal: Display or play audible responses received.

[0148] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and is used to reduce the user's feelings of loneliness and stress.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Processing Steps

[0151] Step 1: Request acceptance

[0152] Subject: User, Device

[0153] Action: The user speaks into the smartphone.

[0154] Input: User speech (e.g., "I've been feeling tired at work lately.")

[0155] Specific operation: The device uses a microphone to capture audio data, which is then sent to the server.

[0156] Output: The audio data is sent to the server.

[0157] Step 2: Voice Recognition

[0158] Subject: Server

[0159] How it works: The server sends the voice data to the voice recognition service.

[0160] Input: Audio data

[0161] Specific operation: The server sends the audio data to the Google Speech-to-Text API, which converts the audio into text data.

[0162] Output: Text data from the speech recognition service (e.g., "I've been feeling tired at work lately.")

[0163] Step 3: Natural Language Processing

[0164] Subject: Server

[0165] How it works: The server sends text data to a natural language processing engine.

[0166] Input: Text data

[0167] Specific operation: The server sends text data to the IBM Watson NLP engine to analyze the user's intent. The NLP engine analyzes the text and identifies meaning and intent.

[0168] Output: Intent information from the NLP engine (e.g., "I feel tired and stressed")

[0169] Step 4: Sentiment Analysis

[0170] Subject: Server

[0171] How it works: The server sends the intent information to the sentiment analysis engine.

[0172] Input: Intention information

[0173] Specific operation: The server sends the intent information to Microsoft Azure Cognitive Services Text Analytics, which analyzes the emotion. The emotion analysis engine identifies "fatigue" and "stress" from the text.

[0174] Output: Emotion information from the emotion analysis engine (e.g., "fatigue" and "stress")

[0175] Step 5: Response Generation

[0176] Subject: Server

[0177] How it works: The server generates prompt sentences based on emotional information and inputs them into the generative AI model.

[0178] Input: Emotion information

[0179] Specific operation: The server generates a prompt saying, "The user is tired from work. Please generate a response recommending a break." and sends it to OpenAI's GPT-4. The generative AI model generates a response based on the prompt.

[0180] Output: Response data from the generative AI model (e.g., "Maybe you need a break. Shall we take a moment to take a deep breath and relax?")

[0181] Step 6: Send response

[0182] Subject: Server

[0183] Action: The server generates a response and sends it to the user's device.

[0184] Input: Response data

[0185] Specific operation: The server sends response data to the smartphone.

[0186] Output: Delivery of response data to smartphone

[0187] Step 7: Display the response

[0188] Subject: Terminal

[0189] Action: The device displays or plays audibly the response it receives.

[0190] Input: Response data

[0191] What it does: Your smartphone will display the response on the screen as text, or play it aloud using its speech synthesis feature.

[0192] Output: The user confirms the response visually or audibly.

[0193] Thus, each processing step provides a detailed description of specific operations, showing how an appropriate response is generated based on the user's natural language input.

[0194] (Application example 1)

[0195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0196] Conventional personal assistant systems generate responses based on natural language input from users, but lack the ability to provide specific advice and product suggestions to reduce users' feelings of loneliness and stress. In particular, there is a need for systems that can provide advice and help users reduce stress when shopping in virtual stores.

[0197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0198] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, response generation means using a generative model for generating a response based on the user's intention and emotional state, means for providing the user with product suggestions and stress relief advice based on the generated response, means for transmitting the response to the user's terminal, and means for displaying or playing the response by audio on the user's terminal, thereby reducing the user's sense of loneliness and stress and improving the shopping experience in the virtual store.

[0199] "User" refers to an individual or end user who uses this system to communicate in natural language.

[0200] "Natural language input" refers to input that is given by a user in language and is expressed in voice or text format.

[0201] "Speech recognition" is a technology that converts voice data into text data.

[0202] "Text" refers to textual information generated by speech recognition.

[0203] "Natural language processing" is a technology for analyzing text data and understanding its intent and meaning.

[0204] "Emotion analysis" refers to a technology that identifies a user's emotional state from text data and extracts its emotional components.

[0205] "Generative model" refers to artificial intelligence for generating appropriate responses based on a user's intent and emotional state.

[0206] "Response generation means" refers to a function that uses a generative model to generate natural conversations and suggestions based on the user's intentions and emotional state.

[0207] "Terminal" refers to a device used by a user, including a smartphone, a head-mounted display (HMD), or other device.

[0208] "Response display" refers to the function of outputting the generated response to the user's terminal as text or voice.

[0209] "Product suggestions" refers to the function of recommending appropriate products and services based on the user's intentions and emotions.

[0210] "Stress relief advice" refers to a function that provides specific advice to reduce the stress and fatigue that the user is feeling.

[0211] "Generative AI means" refers to technology for generating responses using generative models.

[0212] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress and improves their shopping experience in virtual stores. The system combines generative AI and an emotion analysis engine to provide users with appropriate product suggestions and advice on how to relieve stress.

[0213] System configuration

[0214] Hardware

[0215] Terminal: A smart device used by a user (e.g., a smartphone, a head-mounted display (HMD)).

[0216] Microphone: Used to capture the user's voice input.

[0217] software

[0218] Speech recognition system: Uses the speech_recognition library to convert voice data into text data.

[0219] Natural Language Processing (NLP) engine: Performs natural language processing and analyzes user intent.

[0220] Sentiment analysis engine: Analyzes emotional elements from text data.

[0221] Generative Models: Use the transformers library to utilize generative AI (GPT-2) models.

[0222] Response generation means: Generates an appropriate response for the user.

[0223] System program processing

[0224] This system operates in the following steps:

[0225] 1. Speech Recognition:

[0226] The server receives user voice input through a microphone, and the voice data is converted to text data using the speech_recognition library.

[0227] 2. Natural Language Processing:

[0228] The server uses a natural language processing engine to analyze the received text data and determine the user's intent.

[0229] 3. Emotion analysis:

[0230] The server uses an emotion analysis engine to analyze the user's emotional state from the text data.

[0231] 4. Response Generation:

[0232] The server uses the generative model to generate appropriate responses based on the user's intentions and emotional state, such as product suggestions and stress-relief advice.

[0233] 5. Response transmission and display:

[0234] The server sends the generated response to the user's terminal, which displays or plays the response audibly.

[0235] Specific examples

[0236] Example 1: When a user is stressed

[0237] Situation: A user says into their smartphone, "I've been feeling tired at work lately."

[0238] process:

[0239] The speech recognition system converts the speech into text: "I've been feeling tired at work lately."

[0240] The natural language processing engine determines that the person is feeling tired and stressed.

[0241] The emotion analysis engine analyzes emotions such as "fatigue" and "stress."

[0242] The generative model generates a response like, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[0243] The server sends this response to the smartphone, which displays the response.

[0244] Example 2: User asks for new hobbies

[0245] Situation: The user speaks to the HMD, "I want to start a new hobby. What should I do?"

[0246] process:

[0247] The speech recognition system converts the speech into text: "I want to start a new hobby. What should I do?"

[0248] The natural language processing engine determines that the person is looking for suggestions for a new hobby.

[0249] The emotion analysis engine analyzes emotions such as "curiosity" and "expectation."

[0250] The generative model generates a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[0251] The server sends this response to the HMD, which displays or plays the response audibly.

[0252] Prompt Sentence Examples

[0253] "Recommend food suggestions based on the user's recent mood."

[0254] "What are some of your most popular items and services these days?"

[0255] As a result, users can receive support to reduce feelings of loneliness and stress while shopping at virtual stores and in their daily lives, and to lead richer lives.

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1: Accepting voice input

[0258] The user speaks in natural language into the device (smartphone or HMD), which then captures the user's voice through a microphone. The input is the user's voice data, and the output is the captured voice data.

[0259] Step 2: Voice Recognition

[0260] The server uses a speech recognition system to convert the captured audio data into text data. Specifically, it uses the speech_recognition library. Audio data is the input and text data is the output.

[0261] Step 3: Natural Language Processing

[0262] The server passes the text data to a natural language processing engine, which analyzes the user's intent. For example, the NLP engine determines intent such as "I feel tired and stressed" or "I'm looking for a new hobby." The input is text data obtained by speech recognition, and the output is data about the user's intent.

[0263] Step 4: Sentiment Analysis

[0264] The server uses an emotion analysis engine to identify the user's emotional state from the text data. For example, emotions such as "fatigue" or "stress" are extracted. The input is text data obtained by natural language processing, and the output is data about the emotional state.

[0265] Step 5: Response Generation

[0266] The server uses a generative model to generate a prompt based on the user's intention and emotional state, and creates a response based on that. Specifically, it uses a generative AI model (GPT-2) using the transformers library. The input is a prompt based on the user's intention and emotional state, and the output is the generated response text.

[0267] Step 6: Send response

[0268] The server sends the generated response text to the user's terminal. The input is the generated response text, and the output is the response text sent to the user's terminal.

[0269] Step 7: Display the response

[0270] The user's device displays the received response text on the screen or plays it aloud using speech synthesis. Specific operations include displaying the text on the screen or playing it aloud using a speech synthesis system. The input is the transmitted response text, and the output is the response that is communicated to the user visually or audibly.

[0271] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0272] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[0273] System program processing

[0274] Request reception

[0275] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[0276] Voice Recognition

[0277] The server uses a speech recognition system to convert the received voice data into text data. In this case, the text "I've been tired at work lately" is generated from the voice.

[0278] Natural Language Processing

[0279] The server passes the transferred text data to a natural language processing (NLP) engine to determine the user's intent. In this case, the NLP model parses the intent as "I feel tired and stressed."

[0280] Emotion analysis

[0281] The server evaluates the user's emotional state from this text and uses an emotion engine to extract emotional elements from the text. In this case, the emotions "fatigue" and "stress" are analyzed. The emotion engine does this in real time, adaptively updating its emotion assessment based on changes in the frequency and content of user input.

[0282] Response Generation

[0283] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. The generative AI might generate a text response like, "Maybe you need a break. Let's take a deep breath and relax." If the emotion engine has learned the user's long-term emotional tendencies, the response can be further personalized.

[0284] Response Send

[0285] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[0286] Response Display

[0287] The user's device (smartphone) receives a response that is displayed as text on the screen or played aloud using a speech synthesis system. The user can see the message displayed on the screen or listen to it. The emotion engine may also visually display the user's emotional state.

[0288] Specific examples

[0289] Example 1: When a user is stressed

[0290] (situation)

[0291] The user says to their smartphone, "I've been feeling tired at work lately."

[0292] (process)

[0293] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0294] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[0295] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[0296] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "fatigue" and "stress."

[0297] 5. Response generation: The server uses generative AI to generate a response such as, "Maybe you need a break. Let's take a deep breath and relax." It also generates a personalized response based on the user's long-term emotional trends.

[0298] 6. Send response: The server sends the response to the smartphone.

[0299] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0300] Example 2: User seeking advice

[0301] (situation)

[0302] A user says, "I want to start a new hobby. What should I do?"

[0303] (process)

[0304] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0305] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[0306] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[0307] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "curiosity" and "expectation."

[0308] 5. Response generation: The server uses generative AI to generate a response such as, "Gardening and programming are popular hobbies these days. If you're interested, why not give them a try?" It also incorporates the user's emotional tendencies to provide more personalized advice.

[0309] 6. Send response: The server sends the response to the smartphone.

[0310] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0311] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[0312] The processing flow will be explained below.

[0313] Step 1:

[0314] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[0315] Step 2:

[0316] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[0317] Step 3:

[0318] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[0319] Step 4:

[0320] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[0321] Step 5:

[0322] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this example, the emotions "fatigue" and "stress" are analyzed. The sentiment analysis engine evaluates the emotion that matches the content of the text in real time.

[0323] Step 6:

[0324] The server uses the generative AI to generate an appropriate response based on the emotional data extracted by the emotion analysis engine. The generative AI generates a text response such as, "Maybe you need a little break. Shall we take a deep breath and relax?"

[0325] Step 7:

[0326] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[0327] Step 8:

[0328] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[0329] Step 9:

[0330] The user sees a message displayed on the device or hears a spoken response, such as "You might need a little break. Would you like to take a moment to breathe and relax?"

[0331] Step 10:

[0332] The server accepts new input data from the user and learns the user's long-term emotional trends. The emotion analysis engine uses the user's continuous data to evaluate emotional transitions and analyze long-term trends, which allows the next response to be more personalized.

[0333] In this way, a "Life Mentor" system can receive user input, analyze emotions, and generate appropriate responses based on those inputs, providing support tailored to individual situations, such as helping people relieve stress in their daily lives or suggesting new hobbies.

[0334] Example 2

[0335] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0336] In modern society, feelings of loneliness and stress experienced by users have become serious issues. Current personal assistant systems are unable to adequately address these emotional issues. This is due to a lack of technology to accurately grasp a user's emotional state and generate appropriate responses. Another problem is that it is difficult to provide personalized responses that incorporate a user's long-term emotional trends. As a result, users are unable to receive support that is in tune with their own emotions, and it is difficult to alleviate feelings of loneliness and stress.

[0337] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for visually displaying the user's emotional state, means for adaptively updating an emotion evaluation in accordance with changes in the user's input frequency and content, and means for personalizing the generated response by learning the user's long-term emotional tendency. This makes it possible to accurately grasp the user's emotional state and provide an appropriate and personalized response.

[0338] "User" refers to a person who uses the system to input natural language.

[0339] "Natural language input" refers to linguistic input that a user makes to a system using voice or text.

[0340] "Speech recognition" is a technology that converts voice data into text data.

[0341] "Text" refers to character string data generated based on natural language input.

[0342] "Analysis" refers to the process of understanding the content of text and determining the user's intent and sentiment.

[0343] "Natural language processing" is a technology that analyzes text data to determine the user's intent.

[0344] "Sentiment analysis" is a technique for assessing a user's emotional state from text and extracting specific emotional elements.

[0345] "Generative AI" refers to artificial intelligence that generates responses based on the user's intent and emotional state.

[0346] "Response" refers to the reply or message that the generating AI provides to the user.

[0347] "Terminal" refers to devices such as smartphones and personal computers used by users.

[0348] "Display" refers to displaying the generated response as text on the terminal screen.

[0349] "Voice playback" refers to playing back the generated response as voice using speech synthesis technology.

[0350] "Visual display" refers to a display means for visually representing a user's emotional state.

[0351] "Adaptive" refers to dynamically responding to changes in the frequency and content of user input.

[0352] "Personalization" refers to learning a user's long-term emotional tendencies and responding to them individually.

[0353] The present invention relates to a personal assistant system for reducing a user's feelings of loneliness and stress. This system accepts natural language input and generates a response that is in tune with the user's feelings by combining a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[0354] Hardware and Software Overview

[0355] server:

[0356] The servers have central processing units (CPUs) that perform speech recognition, natural language processing, sentiment analysis, and generative AI functions. The servers are deployed on high-performance cloud infrastructure, such as Google Cloud Platform or Amazon Web Services.

[0357] Device:

[0358] The device refers to the smartphone or computer used by the user, which is equipped with audio capture and network connectivity functions.

[0359] Program processing

[0360] Request reception

[0361] When a user says to their smartphone, "I've been feeling tired at work lately," the smartphone sends the voice data to the server.

[0362] Voice Recognition

[0363] The server uses Google Cloud Speech-to-Text to convert the received voice data into text data, generating the text "I've been tired at work lately" from the voice.

[0364] Natural Language Processing

[0365] The server passes the generated text data to a natural language processing engine (such as OpenAI's GPT-3 (registered trademark)) to determine the user's intent. In this case, the NLP model analyzes the intent as "feeling tired and stressed."

[0366] Emotion analysis

[0367] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotional elements in the text, extracting the emotions of "fatigue" and "stress."

[0368] Response Generation

[0369] The server uses a generative AI (such as OpenAI's ChatGPT®) to generate an appropriate response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a little break. Shall we take a moment to take a deep breath and relax?"

[0370] Response Send

[0371] The server generates a response and sends it to the user's device (smartphone). The response is then transferred to the smartphone via the network.

[0372] Response Display

[0373] The user's device (smartphone) receives a response, which is then displayed as text on the screen or played aloud using a speech synthesis system such as AWS Polly. The user can then see the message displayed on the screen or listen to it. Additionally, an emotion engine visually displays the user's emotional state.

[0374] Specific examples

[0375] Example 1: When a user is stressed

[0376] (situation)

[0377] The user says to their smartphone, "I've been feeling tired at work lately."

[0378] (Processing) Based on this statement, the server converts the speech into text data, performs sentiment analysis, and generates and sends an appropriate response.

[0379] Example 2: User seeking advice

[0380] (situation)

[0381] A user says, "I want to start a new hobby. What should I do?"

[0382] (Processing) Based on this utterance, the server converts the speech into text data, analyzes emotions, and generates a response suggesting recommended hobbies.

[0383] Prompt Sentence Examples

[0384] Stress prompts

[0385] “If a user expresses that they are tired, what words of encouragement would you offer them?”

[0386] New Hobby Prompt

[0387] "When a user is looking for a new hobby, how can we suggest trending hobbies?"

[0388] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[0389] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0390] Step 1:

[0391] The user speaks into the input device (smartphone) saying, "I've been feeling tired at work lately." Voice input is performed, and the smartphone captures this voice data. This captured voice data becomes the input data used in the next processing step.

[0392] Step 2:

[0393] The device (smartphone) sends the captured audio data to the server. The device uses a network connection to transfer this audio data to the server in real time. The server receives this audio data and uses it in the next processing step.

[0394] Step 3:

[0395] The server uses a speech recognition system to convert the received voice data into text data. Specifically, Google Cloud Speech-to-Text is used to convert the voice data into text such as "I've been feeling tired at work lately." The input is voice data, and the output is text data.

[0396] Step 4:

[0397] The server passes the generated text data to a natural language processing (NLP) engine to determine the user's intent. Specifically, OpenAI's GPT-3 is used to analyze the intent of "feeling tired and stressed." The input is text data, and the output is data indicating the user's intent.

[0398] Step 5:

[0399] The server uses an emotion engine to analyze the emotional elements in the text data. It uses IBM Watson Tone Analyzer to extract the emotions of "fatigue" and "stress." The input is text data, and the output is data indicating the emotional state.

[0400] Step 6:

[0401] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. It uses OpenAI's ChatGPT to generate a text response such as, "Maybe you need a break. Let's take a deep breath and relax." The input is the user's intent and emotional data, and the output is a text response.

[0402] Step 7:

[0403] The server generates a text response and sends it to the user's device (smartphone). The server then transfers the text response to the smartphone via the network. The input is the text response, and the output is data sent to the device.

[0404] Step 8:

[0405] The terminal receives the text response sent from the server. The terminal receives this data via the network and uses it in the next processing step. The input is the text response data from the server.

[0406] Step 9:

[0407] The device displays or plays audible the text response it receives. Specifically, it uses AWS Polly to synthesize speech and plays a message to the user audibly or displays it as text on the screen. The input is the text response data, and the output is visual or auditory feedback to the user.

[0408] This series of processes makes it possible to generate and provide an appropriate response to the emotions and intentions expressed by the user in natural language.

[0409] (Application example 2)

[0410] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0411] Conventional personal assistant systems often only generate responses by analyzing the user's natural language input and emotional state, and have the problem of not being able to suggest products or services that correspond to the user's specific requests and emotions. For this reason, there has been a demand for systems that not only reduce the sense of loneliness and stress felt by users, but also improve the user experience by suggesting specific actions.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0413] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, and means for suggesting products and services to the user based on the response. This makes it possible to provide a response that is in tune with the user's emotions while improving the user experience by suggesting specific products and services.

[0414] "Natural language input" refers to input made by a user in natural language, in the form of voice or text.

[0415] "Speech recognition" is a technology for converting voice data into text data, and is a technology for acquiring user utterances as text information.

[0416] "Text" refers to character information generated by speech recognition, and is a string of characters that expresses the content of a user's speech.

[0417] "Natural language processing" is a technology for analyzing natural language text to understand its meaning and intent, and is a means of accurately determining the user's intent.

[0418] "Sentiment analysis" is a technique for assessing and extracting a user's emotional state from text, and is a means of understanding a user's psychological state.

[0419] "Generative AI" is a technology that uses artificial intelligence to generate responses based on a user's intent and emotional state, and is a means of creating natural conversations.

[0420] "User terminal" refers to a device such as a smartphone, smart glasses, or head-mounted display on which responses can be received and displayed.

[0421] "Response display" refers to displaying the generated response on the user's terminal, allowing the user to visually confirm the response content.

[0422] "Audio playback" refers to the use of speech synthesis technology to transmit the generated response to the user audibly, allowing the user to confirm the response content through their hearing.

[0423] "Product and service suggestions" refers to introducing specific products and services that correspond to the user's emotions and intentions based on the generated responses, and suggests actions to satisfy the user's needs.

[0424] System Configuration

[0425] The present invention relates to a personal assistant system for reducing a user's sense of loneliness and stress, and is implemented by combining the following hardware and software:

[0426] Hardware used

[0427] Smartphones (iPhone (registered trademark), ANDROID (registered trademark), etc.)

[0428] Smart glasses (general name)

[0429] Head-mounted display (general name)

[0430] Server (cloud-based: AWS, Google Cloud Platform, etc.)

[0431] Software used

[0432] Speech recognition systems (Google Speech-to-Text, IBM Watson, etc.)

[0433] Natural language processing engine (GPT-4, BERT, etc.)

[0434] Sentiment analysis engine (Microsoft Azure Emotion API, etc.)

[0435] Generative AI models (OpenAI GPT-4, etc.)

[0436] Program processing and data processing

[0437] 1. Request acceptance

[0438] When a user says something like "I'm tired from work, so I want to eat something delicious" via a smartphone or smart glasses, the voice data is sent to the server, where it is converted into text data using a voice recognition system.

[0439] 2. Voice Recognition

[0440] The server uses a speech recognition system (such as Google Speech-to-Text) to convert the received voice data into text data, which generates the text "I'm tired from work, so I want to eat something delicious."

[0441] 3. Natural Language Processing

[0442] The server passes this text data to a natural language processing (NLP) engine to analyze the user's intent. In this case, the NLP engine (such as GPT-4) determines the intent as "I'm feeling tired and want to eat something delicious."

[0443] 4. Emotion analysis

[0444] The server uses a sentiment analysis engine to assess and extract the user's emotional state (fatigue and anticipation) from the text.

[0445] 5. Response Generation

[0446] The server uses a generative AI model to generate appropriate responses and meal suggestions based on the user's intent and emotional state, such as, "You seem tired today. In that case, I'd recommend a warm soup or a nutritious salad. What do you think?"

[0447] 6. Sending a Response

[0448] The generated response is sent from the server to the user's terminal.

[0449] 7. Response Display

[0450] The user's device (smartphone or smart glasses) will display the received response on the screen, allowing the user to visually confirm the response and select and order a meal suggestion if necessary.

[0451] Specific examples

[0452] Example 1: Suggestions for when you're tired

[0453] (situation)

[0454] The user says to their smartphone, "I'm tired from work, so I want to eat something delicious."

[0455] (process)

[0456] 1. Request acceptance

[0457] 2. Speech recognition: "I'm tired from work and want to eat something delicious."

[0458] 3. Natural Language Processing: "I'm feeling tired and want something tasty to eat."

[0459] 4. Emotion analysis: Analyzing "fatigue" and "expectation"

[0460] 5. Response Generation: "I'd love a warm soup or a hearty salad. What do you think?"

[0461] 6. Sending a Response

[0462] 7. Response display: Responses and meal suggestions are displayed on the user's smartphone or smart glasses.

[0463] Prompt Sentence Examples

[0464] Suggestions for when you're tired

[0465] User request: "I'm tired from work, so I want to eat something delicious."

[0466] Response-generating prompt:

[0467] "What kind of food would you recommend for users when they're tired? Also, add some encouraging messages to help users relax."

[0468] Example response of the generative AI model:

[0469] "You must be tired today. In that case, I recommend a warm soup or a nutritious salad. Take a little rest and relax your body and mind."

[0470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0471] Step 1: Request acceptance

[0472] The user speaks into their smartphone or smart glasses, "I'm tired from work, I want to eat something delicious." The voice data is sent to the server via a URL. This is the input. The server receives this voice data and prepares for the next step.

[0473] Step 2: Voice Recognition

[0474] The server uses a speech recognition system (e.g., Google Speech-to-Text) to convert the received voice data into text data. The input of this step is the voice data, and the output is the text "I'm tired from work, so I want to eat something delicious." The speech recognition system analyzes the voice waveform and generates a corresponding string of characters.

[0475] Step 3: Natural Language Processing

[0476] The server passes the generated text data to a natural language processing engine (e.g., GPT-4). The input of this step is text data, and the output is the intent, "I feel tired and want to eat something delicious." The natural language processing engine analyzes words and understands the context to determine the user's intent.

[0477] Step 4: Sentiment Analysis

[0478] The server passes the text data to a sentiment analysis engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The input for this step is the text data, and the output is the emotional states of "fatigue" and "expectation." The sentiment analysis engine identifies emotional keywords in the text and evaluates the emotion.

[0479] Step 5: Response Generation

[0480] The server uses a generative AI model to generate a response based on the user's intention and emotional state. The input for this step is the user's intention and emotional state, and the output is a response message such as, "You seem tired today, don't you? In that case, I recommend a warm soup or a nutritious salad. What do you think?" The generative AI model generates a sentence that continues the conversation in a natural way based on the prompt.

[0481] Step 6: Send response

[0482] The generated response message is sent from the server to the user's device (smartphone or smart glasses). The input of this step is the generated response message, and the output is the completion of message transmission. The server sends the message to the device via network communication.

[0483] Step 7: Display the response

[0484] The user's device (smartphone or smart glasses) displays the received response on its screen and, if necessary, plays it aloud using speech synthesis technology. The input for this step is the received response message, and the output is in a format that the user can confirm visually or audibly. The device's display and speaker are used to notify the user of the response content.

[0485] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0486] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0487] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0488] [Second embodiment]

[0489] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0490] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0491] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0492] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0493] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0494] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0495] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0496] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0497] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0498] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0499] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0500] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0501] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[0502] System program processing

[0503] Request reception

[0504] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[0505] Voice Recognition

[0506] The server uses a speech recognition system to convert the received voice data into text data. In this case, the server generates the text "I've been tired at work lately" from the voice.

[0507] Natural Language Processing

[0508] The server passes the transferred text to a natural language processing engine to analyze the user's intent. In this case, the NLP model analyzes the intent as "I feel tired and stressed."

[0509] Emotion analysis

[0510] The server evaluates the user's emotional state from this text and extracts emotional elements from the text, in this case identifying the emotions of "fatigue" and "stress."

[0511] Response Generation

[0512] The server uses generative AI to generate an appropriate response based on the user's intent and emotional state, generating a mentor message like, "Maybe you need a little break. Let's take a deep breath and relax."

[0513] Response Send

[0514] The server generates a response and sends it to the user's device. In this case, the response is sent back to the smartphone.

[0515] Response Display

[0516] The user's device (smartphone) receives the response and displays it on the screen as text or uses speech synthesis to communicate it to the user. The user can confirm the message by looking at it on the screen or by listening to it.

[0517] Specific examples

[0518] Example 1: When a user is stressed

[0519] (situation)

[0520] The user says to their smartphone, "I've been feeling tired at work lately."

[0521] (process)

[0522] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0523] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[0524] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[0525] 4. Emotion analysis: The server analyzes the emotions "fatigue" and "stress."

[0526] 5. Response generation: The server uses a generative AI to generate a response such as, "Maybe you need a little break. Shall we take a deep breath and take a moment to relax?"

[0527] 6. Send response: The server sends the response to the smartphone.

[0528] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0529] Example 2: User seeking advice

[0530] (situation)

[0531] A user says, "I want to start a new hobby. What should I do?"

[0532] (process)

[0533] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0534] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[0535] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[0536] 4. Sentiment analysis: The server analyzes the text for the emotional elements of "curiosity" and "expectation."

[0537] 5. Response generation: The server uses a generation AI to generate a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[0538] 6. Send response: The server sends the response to the smartphone.

[0539] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0540] In this way, the personal assistant system of the present invention generates an appropriate response to a user's natural language input and is used to reduce the user's sense of loneliness and stress.

[0541] The processing flow will be explained below.

[0542] Step 1:

[0543] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[0544] Step 2:

[0545] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[0546] Step 3:

[0547] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[0548] Step 4:

[0549] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[0550] Step 5:

[0551] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this case, the emotions "fatigue" and "stress" are analyzed.

[0552] Step 6:

[0553] The server uses generative AI to generate a response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[0554] Step 7:

[0555] The server sends the generated response to the user's device (smartphone). Again, the generated text response is transferred to the smartphone via the network.

[0556] Step 8:

[0557] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[0558] Step 9:

[0559] The user sees a message displayed on the device or hears a spoken response. For example, the user reads or hears the message, "You might need a little break. Would you like to take a moment to take a deep breath and relax?"

[0560] In this way, through each of the above steps, the LifeMentor system stays close to the user's heart and provides appropriate responses.

[0561] Example 1

[0562] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0563] In modern society, users often feel lonely and stressed, and appropriate support is needed to address these issues. Conventional personal assistant systems face the challenge of being unable to fully understand a user's emotions and intentions and provide appropriate responses. Therefore, a system that can accurately analyze a user's emotions and intentions and generate appropriate responses based on those analysis is needed.

[0564] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0565] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generative AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for transmitting received speech data to a speech recognition service, means for transmitting text data acquired from the speech recognition service to a natural language processing engine, means for transmitting intention information acquired from the natural language processing engine to an emotion analysis engine, means for generating a prompt sentence based on the emotion information acquired from the emotion analysis engine and inputting it to a generative AI model, and means for transmitting response data acquired from the generative AI model to the user's terminal. This makes it possible to accurately analyze the user's emotions and intentions and provide an appropriate response based on the analysis.

[0566] "Natural language input" refers to natural language, such as the words spoken or written by a user.

[0567] "Speech recognition" refers to the technology of analyzing speech and converting it into text data.

[0568] "Text data" refers to character data converted by speech recognition.

[0569] "Natural language processing" refers to the technology of analyzing text data to understand its meaning and intent.

[0570] "Emotion analysis" refers to the technology of identifying a user's emotions and psychological state from text data.

[0571] "Generative AI" refers to artificial intelligence that generates responses based on the user's intentions and emotional state.

[0572] A "prompt sentence" refers to an instruction sentence to be input into the generation AI.

[0573] "Terminal" refers to a device that a user directly operates (e.g., a smartphone or tablet).

[0574] "Speech Recognition Service" refers generally to any external cloud-based or on-premise service that provides speech recognition.

[0575] A "natural language processing engine" refers to software or a system in general that analyzes text data and understands the user's intent.

[0576] "Sentiment analysis engine" refers to software or a system in general that analyzes text data to identify a user's emotions.

[0577] "Generative AI model" refers to any artificial intelligence model that generates a response based on an input prompt.

[0578] "Response Data" refers to the text data of the response generated by the generative AI model.

[0579] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress. By combining generative AI and an emotion engine, we provide a system that generates responses that are close to the user's heart.

[0580] Specific system configuration

[0581] Hardware and Software

[0582] This system operates using the following hardware and software.

[0583] User device: smartphone, tablet, or computer

[0584] Server: Cloud-based or on-premise server

[0585] Speech Recognition Service: An external cloud service for converting voice input into text (e.g., Google Speech-to-Text API)

[0586] Natural language processing engine: Software for analyzing text data (e.g., IBM Watson NLP)

[0587] Sentiment analysis engine: Software for identifying emotions (e.g., Microsoft Azure Cognitive Services Text Analytics)

[0588] Generative AI model: artificial intelligence for generating responses (e.g., OpenAI GPT-4)

[0589] System operation explanation

[0590] Request reception

[0591] When a user speaks natural language into a smartphone, such as "I'm tired from work these days," the device captures the speech and transmits it to a server as voice data. The device is equipped with an appropriate microphone and communication interface to accept voice input.

[0592] Voice Recognition

[0593] The server sends the received voice data to a speech recognition service and converts the voice data into text data. For example, by using the Google Speech-to-Text API, speech can be converted into text with high accuracy.

[0594] Natural Language Processing

[0595] The server then sends the converted text data to a natural language processing engine to analyze the user's intent. Here, the IBM Watson NLP engine is used to identify the user's specific intent (e.g., "I feel tired and stressed") from the text data.

[0596] Emotion analysis

[0597] The server sends the parsed intent information to a sentiment analysis engine, which uses Microsoft Azure Cognitive Services Text Analytics to identify emotional elements (e.g., "fatigue" and "stress") contained within the text.

[0598] Response Generation

[0599] The server generates a prompt based on the identified intent and emotional information and inputs it into the generative AI model. An example of a prompt is, "The user is tired from work. Please generate a response recommending a break." OpenAI GPT-4 is used to generate a response, and an appropriate response is obtained, for example, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[0600] Response Send

[0601] The server sends the generated response data to the user's terminal, which receives the response and notifies the user.

[0602] Response Display

[0603] The device will display the received response as text on the screen or play it aloud using a speech synthesis function, allowing the user to confirm the response visually or audibly.

[0604] Specific examples

[0605] Example 1: When a user is stressed

[0606] User: "I've been feeling tired at work lately," says the smartphone.

[0607] Server: Receives the voice data and sends it to the voice recognition service.

[0608] Server: Obtain the text data "I've been tired at work lately."

[0609] Server: Sends text data to a natural language processing engine and analyzes intent.

[0610] Server: Sends intent information to the emotion analysis engine to identify emotions of "fatigue" and "stress."

[0611] Server: Generates a prompt sentence, "The user is tired from work. Please generate a response recommending a break." and inputs it into the generative AI model.

[0612] Server: The response obtained from GPT-4, "Maybe you need a break. Would you like to take a deep breath and relax?", is sent to the user's device.

[0613] Terminal: Display or play audible responses received.

[0614] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and is used to reduce the user's feelings of loneliness and stress.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Processing Steps

[0617] Step 1: Request acceptance

[0618] Subject: User, Device

[0619] Action: The user speaks into the smartphone.

[0620] Input: User speech (e.g., "I've been feeling tired at work lately.")

[0621] Specific operation: The device uses a microphone to capture audio data, which is then sent to the server.

[0622] Output: The audio data is sent to the server.

[0623] Step 2: Voice Recognition

[0624] Subject: Server

[0625] How it works: The server sends the voice data to the voice recognition service.

[0626] Input: Audio data

[0627] Specific operation: The server sends the audio data to the Google Speech-to-Text API, which converts the audio into text data.

[0628] Output: Text data from the speech recognition service (e.g., "I've been feeling tired at work lately.")

[0629] Step 3: Natural Language Processing

[0630] Subject: Server

[0631] How it works: The server sends text data to a natural language processing engine.

[0632] Input: Text data

[0633] Specific operation: The server sends text data to the IBM Watson NLP engine to analyze the user's intent. The NLP engine analyzes the text and identifies meaning and intent.

[0634] Output: Intent information from the NLP engine (e.g., "I feel tired and stressed")

[0635] Step 4: Sentiment Analysis

[0636] Subject: Server

[0637] How it works: The server sends the intent information to the sentiment analysis engine.

[0638] Input: Intention information

[0639] Specific operation: The server sends the intent information to Microsoft Azure Cognitive Services Text Analytics, which analyzes the emotion. The emotion analysis engine identifies "fatigue" and "stress" from the text.

[0640] Output: Emotion information from the emotion analysis engine (e.g., "fatigue" and "stress")

[0641] Step 5: Response Generation

[0642] Subject: Server

[0643] How it works: The server generates prompt sentences based on emotional information and inputs them into the generative AI model.

[0644] Input: Emotion information

[0645] Specific operation: The server generates a prompt saying, "The user is tired from work. Please generate a response recommending a break." and sends it to OpenAI's GPT-4. The generative AI model generates a response based on the prompt.

[0646] Output: Response data from the generative AI model (e.g., "Maybe you need a break. Shall we take a moment to take a deep breath and relax?")

[0647] Step 6: Send response

[0648] Subject: Server

[0649] Action: The server generates a response and sends it to the user's device.

[0650] Input: Response data

[0651] Specific operation: The server sends response data to the smartphone.

[0652] Output: Delivery of response data to smartphone

[0653] Step 7: Display the response

[0654] Subject: Terminal

[0655] Action: The device displays or plays audibly the response it receives.

[0656] Input: Response data

[0657] What it does: Your smartphone will display the response on the screen as text, or play it aloud using its speech synthesis feature.

[0658] Output: The user confirms the response visually or audibly.

[0659] Thus, each processing step provides a detailed description of specific operations, showing how an appropriate response is generated based on the user's natural language input.

[0660] (Application example 1)

[0661] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0662] Conventional personal assistant systems generate responses based on natural language input from users, but lack the ability to provide specific advice and product suggestions to reduce users' feelings of loneliness and stress. In particular, there is a need for systems that can provide advice and help users reduce stress when shopping in virtual stores.

[0663] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0664] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, response generation means using a generative model for generating a response based on the user's intention and emotional state, means for providing the user with product suggestions and stress relief advice based on the generated response, means for transmitting the response to the user's terminal, and means for displaying or playing the response by audio on the user's terminal, thereby reducing the user's sense of loneliness and stress and improving the shopping experience in the virtual store.

[0665] "User" refers to an individual or end user who uses this system to communicate in natural language.

[0666] "Natural language input" refers to input that is given by a user in language and is expressed in voice or text format.

[0667] "Speech recognition" is a technology that converts voice data into text data.

[0668] "Text" refers to textual information generated by speech recognition.

[0669] "Natural language processing" is a technology for analyzing text data and understanding its intent and meaning.

[0670] "Emotion analysis" refers to a technology that identifies a user's emotional state from text data and extracts its emotional components.

[0671] "Generative model" refers to artificial intelligence for generating appropriate responses based on a user's intent and emotional state.

[0672] "Response generation means" refers to a function that uses a generative model to generate natural conversations and suggestions based on the user's intentions and emotional state.

[0673] "Terminal" refers to a device used by a user, including a smartphone, a head-mounted display (HMD), or other device.

[0674] "Response display" refers to the function of outputting the generated response to the user's terminal as text or voice.

[0675] "Product suggestions" refers to the function of recommending appropriate products and services based on the user's intentions and emotions.

[0676] "Stress relief advice" refers to a function that provides specific advice to reduce the stress and fatigue that the user is feeling.

[0677] "Generative AI means" refers to technology for generating responses using generative models.

[0678] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress and improves their shopping experience in virtual stores. The system combines generative AI and an emotion analysis engine to provide users with appropriate product suggestions and advice on how to relieve stress.

[0679] System configuration

[0680] Hardware

[0681] Terminal: A smart device used by a user (e.g., a smartphone, a head-mounted display (HMD)).

[0682] Microphone: Used to capture the user's voice input.

[0683] software

[0684] Speech recognition system: Uses the speech_recognition library to convert voice data into text data.

[0685] Natural Language Processing (NLP) engine: Performs natural language processing and analyzes user intent.

[0686] Sentiment analysis engine: Analyzes emotional elements from text data.

[0687] Generative Models: Use the transformers library to utilize generative AI (GPT-2) models.

[0688] Response generation means: Generates an appropriate response for the user.

[0689] System program processing

[0690] This system operates in the following steps:

[0691] 1. Speech Recognition:

[0692] The server receives user voice input through a microphone, and the voice data is converted to text data using the speech_recognition library.

[0693] 2. Natural Language Processing:

[0694] The server uses a natural language processing engine to analyze the received text data and determine the user's intent.

[0695] 3. Emotion analysis:

[0696] The server uses an emotion analysis engine to analyze the user's emotional state from the text data.

[0697] 4. Response Generation:

[0698] The server uses the generative model to generate appropriate responses based on the user's intentions and emotional state, such as product suggestions and stress-relief advice.

[0699] 5. Response transmission and display:

[0700] The server sends the generated response to the user's terminal, which displays or plays the response audibly.

[0701] Specific examples

[0702] Example 1: When a user is stressed

[0703] Situation: A user says into their smartphone, "I've been feeling tired at work lately."

[0704] process:

[0705] The speech recognition system converts the speech into text: "I've been feeling tired at work lately."

[0706] The natural language processing engine determines that the person is feeling tired and stressed.

[0707] The emotion analysis engine analyzes emotions such as "fatigue" and "stress."

[0708] The generative model generates a response like, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[0709] The server sends this response to the smartphone, which displays the response.

[0710] Example 2: User asks for new hobbies

[0711] Situation: The user speaks to the HMD, "I want to start a new hobby. What should I do?"

[0712] process:

[0713] The speech recognition system converts the speech into text: "I want to start a new hobby. What should I do?"

[0714] The natural language processing engine determines that the person is looking for suggestions for a new hobby.

[0715] The emotion analysis engine analyzes emotions such as "curiosity" and "expectation."

[0716] The generative model generates a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[0717] The server sends this response to the HMD, which displays or plays the response audibly.

[0718] Prompt Sentence Examples

[0719] "Recommend food suggestions based on the user's recent mood."

[0720] "What are some of your most popular items and services these days?"

[0721] As a result, users can receive support to reduce feelings of loneliness and stress while shopping at virtual stores and in their daily lives, and to lead richer lives.

[0722] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0723] Step 1: Accepting voice input

[0724] The user speaks in natural language into the device (smartphone or HMD), which then captures the user's voice through a microphone. The input is the user's voice data, and the output is the captured voice data.

[0725] Step 2: Voice Recognition

[0726] The server uses a speech recognition system to convert the captured audio data into text data. Specifically, it uses the speech_recognition library. Audio data is the input and text data is the output.

[0727] Step 3: Natural Language Processing

[0728] The server passes the text data to a natural language processing engine, which analyzes the user's intent. For example, the NLP engine determines intent such as "I feel tired and stressed" or "I'm looking for a new hobby." The input is text data obtained by speech recognition, and the output is data about the user's intent.

[0729] Step 4: Sentiment Analysis

[0730] The server uses an emotion analysis engine to identify the user's emotional state from the text data. For example, emotions such as "fatigue" or "stress" are extracted. The input is text data obtained by natural language processing, and the output is data about the emotional state.

[0731] Step 5: Response Generation

[0732] The server uses a generative model to generate a prompt based on the user's intention and emotional state, and creates a response based on that. Specifically, it uses a generative AI model (GPT-2) using the transformers library. The input is a prompt based on the user's intention and emotional state, and the output is the generated response text.

[0733] Step 6: Send response

[0734] The server sends the generated response text to the user's terminal. The input is the generated response text, and the output is the response text sent to the user's terminal.

[0735] Step 7: Display the response

[0736] The user's device displays the received response text on the screen or plays it aloud using speech synthesis. Specific operations include displaying the text on the screen or playing it aloud using a speech synthesis system. The input is the transmitted response text, and the output is the response that is communicated to the user visually or audibly.

[0737] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0738] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[0739] System program processing

[0740] Request reception

[0741] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[0742] Voice Recognition

[0743] The server uses a speech recognition system to convert the received voice data into text data. In this case, the text "I've been tired at work lately" is generated from the voice.

[0744] Natural Language Processing

[0745] The server passes the transferred text data to a natural language processing (NLP) engine to determine the user's intent. In this case, the NLP model parses the intent as "I feel tired and stressed."

[0746] Emotion analysis

[0747] The server evaluates the user's emotional state from this text and uses an emotion engine to extract emotional elements from the text. In this case, the emotions "fatigue" and "stress" are analyzed. The emotion engine does this in real time, adaptively updating its emotion assessment based on changes in the frequency and content of user input.

[0748] Response Generation

[0749] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. The generative AI might generate a text response like, "Maybe you need a break. Let's take a deep breath and relax." If the emotion engine has learned the user's long-term emotional tendencies, the response can be further personalized.

[0750] Response Send

[0751] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[0752] Response Display

[0753] The user's device (smartphone) receives a response that is displayed as text on the screen or played aloud using a speech synthesis system. The user can see the message displayed on the screen or listen to it. The emotion engine may also visually display the user's emotional state.

[0754] Specific examples

[0755] Example 1: When a user is stressed

[0756] (situation)

[0757] The user says to their smartphone, "I've been feeling tired at work lately."

[0758] (process)

[0759] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0760] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[0761] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[0762] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "fatigue" and "stress."

[0763] 5. Response generation: The server uses generative AI to generate a response such as, "Maybe you need a break. Let's take a deep breath and relax." It also generates a personalized response based on the user's long-term emotional trends.

[0764] 6. Send response: The server sends the response to the smartphone.

[0765] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0766] Example 2: User seeking advice

[0767] (situation)

[0768] A user says, "I want to start a new hobby. What should I do?"

[0769] (process)

[0770] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0771] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[0772] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[0773] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "curiosity" and "expectation."

[0774] 5. Response generation: The server uses generative AI to generate a response such as, "Gardening and programming are popular hobbies these days. If you're interested, why not give them a try?" It also incorporates the user's emotional tendencies to provide more personalized advice.

[0775] 6. Send response: The server sends the response to the smartphone.

[0776] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0777] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[0778] The processing flow will be explained below.

[0779] Step 1:

[0780] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[0781] Step 2:

[0782] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[0783] Step 3:

[0784] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[0785] Step 4:

[0786] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[0787] Step 5:

[0788] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this example, the emotions "fatigue" and "stress" are analyzed. The sentiment analysis engine evaluates the emotion that matches the content of the text in real time.

[0789] Step 6:

[0790] The server uses the generative AI to generate an appropriate response based on the emotional data extracted by the emotion analysis engine. The generative AI generates a text response such as, "Maybe you need a little break. Shall we take a deep breath and relax?"

[0791] Step 7:

[0792] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[0793] Step 8:

[0794] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[0795] Step 9:

[0796] The user sees a message displayed on the device or hears a spoken response, such as "You might need a little break. Would you like to take a moment to breathe and relax?"

[0797] Step 10:

[0798] The server accepts new input data from the user and learns the user's long-term emotional trends. The emotion analysis engine uses the user's continuous data to evaluate emotional transitions and analyze long-term trends, which allows the next response to be more personalized.

[0799] In this way, a "Life Mentor" system can receive user input, analyze emotions, and generate appropriate responses based on those inputs, providing support tailored to individual situations, such as helping people relieve stress in their daily lives or suggesting new hobbies.

[0800] Example 2

[0801] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0802] In modern society, feelings of loneliness and stress experienced by users have become serious issues. Current personal assistant systems are unable to adequately address these emotional issues. This is due to a lack of technology to accurately grasp a user's emotional state and generate appropriate responses. Another problem is that it is difficult to provide personalized responses that incorporate a user's long-term emotional trends. As a result, users are unable to receive support that is in tune with their own emotions, and it is difficult to alleviate feelings of loneliness and stress.

[0803] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for visually displaying the user's emotional state, means for adaptively updating an emotion evaluation in accordance with changes in the user's input frequency and content, and means for personalizing the generated response by learning the user's long-term emotional tendency. This makes it possible to accurately grasp the user's emotional state and provide an appropriate and personalized response.

[0804] "User" refers to a person who uses the system to input natural language.

[0805] "Natural language input" refers to linguistic input that a user makes to a system using voice or text.

[0806] "Speech recognition" is a technology that converts voice data into text data.

[0807] "Text" refers to character string data generated based on natural language input.

[0808] "Analysis" refers to the process of understanding the content of text and determining the user's intent and sentiment.

[0809] "Natural language processing" is a technology that analyzes text data to determine the user's intent.

[0810] "Sentiment analysis" is a technique for assessing a user's emotional state from text and extracting specific emotional elements.

[0811] "Generative AI" refers to artificial intelligence that generates responses based on the user's intent and emotional state.

[0812] "Response" refers to the reply or message that the generating AI provides to the user.

[0813] "Terminal" refers to devices such as smartphones and personal computers used by users.

[0814] "Display" refers to displaying the generated response as text on the terminal screen.

[0815] "Voice playback" refers to playing back the generated response as voice using speech synthesis technology.

[0816] "Visual display" refers to a display means for visually representing a user's emotional state.

[0817] "Adaptive" refers to dynamically responding to changes in the frequency and content of user input.

[0818] "Personalization" refers to learning a user's long-term emotional tendencies and responding to them individually.

[0819] The present invention relates to a personal assistant system for reducing a user's feelings of loneliness and stress. This system accepts natural language input and generates a response that is in tune with the user's feelings by combining a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[0820] Hardware and Software Overview

[0821] server:

[0822] The servers have central processing units (CPUs) that perform speech recognition, natural language processing, sentiment analysis, and generative AI functions. The servers are deployed on high-performance cloud infrastructure, such as Google Cloud Platform or Amazon Web Services.

[0823] Device:

[0824] The device refers to the smartphone or computer used by the user, which is equipped with audio capture and network connectivity functions.

[0825] Program processing

[0826] Request reception

[0827] When a user says to their smartphone, "I've been feeling tired at work lately," the smartphone sends the voice data to the server.

[0828] Voice Recognition

[0829] The server uses Google Cloud Speech-to-Text to convert the received voice data into text data, generating the text "I've been tired at work lately" from the voice.

[0830] Natural Language Processing

[0831] The server passes the generated text data to a natural language processing engine (such as OpenAI's GPT-3) to determine the user's intent. In this case, the NLP model analyzes the intent as "feeling tired and stressed."

[0832] Emotion analysis

[0833] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotional elements in the text, extracting the emotions of "fatigue" and "stress."

[0834] Response Generation

[0835] The server uses a generative AI (such as OpenAI's ChatGPT) to generate an appropriate response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a break. Shall we take a moment to take a deep breath and relax?"

[0836] Response Send

[0837] The server generates a response and sends it to the user's device (smartphone). The response is then transferred to the smartphone via the network.

[0838] Response Display

[0839] The user's device (smartphone) receives a response, which is then displayed as text on the screen or played aloud using a speech synthesis system such as AWS Polly. The user can then see the message displayed on the screen or listen to it. In addition, an emotion engine visually displays the user's emotional state.

[0840] Specific examples

[0841] Example 1: When a user is stressed

[0842] (situation)

[0843] The user says to their smartphone, "I've been feeling tired at work lately."

[0844] (Processing) Based on this statement, the server converts the speech into text data, performs sentiment analysis, and generates and sends an appropriate response.

[0845] Example 2: User seeking advice

[0846] (situation)

[0847] A user says, "I want to start a new hobby. What should I do?"

[0848] (Processing) Based on this utterance, the server converts the speech into text data, analyzes emotions, and generates a response suggesting recommended hobbies.

[0849] Prompt Sentence Examples

[0850] Stress prompts

[0851] “If a user expresses that they are tired, what words of encouragement would you offer them?”

[0852] New Hobby Prompt

[0853] "When a user is looking for a new hobby, how can we suggest trending hobbies?"

[0854] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[0855] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0856] Step 1:

[0857] The user speaks into the input device (smartphone) saying, "I've been feeling tired at work lately." Voice input is performed, and the smartphone captures this voice data. This captured voice data becomes the input data used in the next processing step.

[0858] Step 2:

[0859] The device (smartphone) sends the captured audio data to the server. The device uses a network connection to transfer this audio data to the server in real time. The server receives this audio data and uses it in the next processing step.

[0860] Step 3:

[0861] The server uses a speech recognition system to convert the received voice data into text data. Specifically, Google Cloud Speech-to-Text is used to convert the voice data into text such as "I've been feeling tired at work lately." The input is voice data, and the output is text data.

[0862] Step 4:

[0863] The server passes the generated text data to a natural language processing (NLP) engine to determine the user's intent. Specifically, OpenAI's GPT-3 is used to analyze the intent of "feeling tired and stressed." The input is text data, and the output is data indicating the user's intent.

[0864] Step 5:

[0865] The server uses an emotion engine to analyze the emotional elements in the text data. It uses IBM Watson Tone Analyzer to extract the emotions of "fatigue" and "stress." The input is text data, and the output is data indicating the emotional state.

[0866] Step 6:

[0867] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. It uses OpenAI's ChatGPT to generate a text response such as, "Maybe you need a break. Let's take a deep breath and relax." The input is the user's intent and emotional data, and the output is a text response.

[0868] Step 7:

[0869] The server generates a text response and sends it to the user's device (smartphone). The server then transfers the text response to the smartphone via the network. The input is the text response, and the output is data sent to the device.

[0870] Step 8:

[0871] The terminal receives the text response sent from the server. The terminal receives this data via the network and uses it in the next processing step. The input is the text response data from the server.

[0872] Step 9:

[0873] The device displays or plays audible the text response it receives. Specifically, it uses AWS Polly to synthesize speech and plays a message to the user audibly or displays it as text on the screen. The input is the text response data, and the output is visual or auditory feedback to the user.

[0874] This series of processes makes it possible to generate and provide an appropriate response to the emotions and intentions expressed by the user in natural language.

[0875] (Application example 2)

[0876] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0877] Conventional personal assistant systems often only generate responses by analyzing the user's natural language input and emotional state, and have the problem of not being able to suggest products or services that correspond to the user's specific requests and emotions. For this reason, there has been a demand for systems that not only reduce the sense of loneliness and stress felt by users, but also improve the user experience by suggesting specific actions.

[0878] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0879] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, and means for suggesting products and services to the user based on the response. This makes it possible to provide a response that is in tune with the user's emotions while improving the user experience by suggesting specific products and services.

[0880] "Natural language input" refers to input made by a user in natural language, in the form of voice or text.

[0881] "Speech recognition" is a technology for converting voice data into text data, and is a technology for acquiring user utterances as text information.

[0882] "Text" refers to character information generated by speech recognition, and is a string of characters that expresses the content of a user's speech.

[0883] "Natural language processing" is a technology for analyzing natural language text to understand its meaning and intent, and is a means of accurately determining the user's intent.

[0884] "Sentiment analysis" is a technique for assessing and extracting a user's emotional state from text, and is a means of understanding a user's psychological state.

[0885] "Generative AI" is a technology that uses artificial intelligence to generate responses based on a user's intent and emotional state, and is a means of creating natural conversations.

[0886] "User terminal" refers to a device such as a smartphone, smart glasses, or head-mounted display on which responses can be received and displayed.

[0887] "Response display" refers to displaying the generated response on the user's terminal, allowing the user to visually confirm the response content.

[0888] "Audio playback" refers to the use of speech synthesis technology to transmit the generated response to the user audibly, allowing the user to confirm the response content through their hearing.

[0889] "Product and service suggestions" refers to introducing specific products and services that correspond to the user's emotions and intentions based on the generated responses, and suggests actions to satisfy the user's needs.

[0890] System Configuration

[0891] The present invention relates to a personal assistant system for reducing a user's sense of loneliness and stress, and is implemented by combining the following hardware and software:

[0892] Hardware used

[0893] Smartphone (iPhone, Android, etc.)

[0894] Smart glasses (general name)

[0895] Head-mounted display (general name)

[0896] Server (cloud-based: AWS, Google Cloud Platform, etc.)

[0897] Software used

[0898] Speech recognition systems (Google Speech-to-Text, IBM Watson, etc.)

[0899] Natural language processing engine (GPT-4, BERT, etc.)

[0900] Sentiment analysis engine (Microsoft Azure Emotion API, etc.)

[0901] Generative AI models (OpenAI GPT-4, etc.)

[0902] Program processing and data processing

[0903] 1. Request acceptance

[0904] When a user says something like "I'm tired from work, so I want to eat something delicious" via a smartphone or smart glasses, the voice data is sent to the server, where it is converted into text data using a voice recognition system.

[0905] 2. Voice Recognition

[0906] The server uses a speech recognition system (such as Google Speech-to-Text) to convert the received voice data into text data, which generates the text "I'm tired from work, so I want to eat something delicious."

[0907] 3. Natural Language Processing

[0908] The server passes this text data to a natural language processing (NLP) engine to analyze the user's intent. In this case, the NLP engine (such as GPT-4) determines the intent as "I'm feeling tired and want to eat something delicious."

[0909] 4. Emotion analysis

[0910] The server uses a sentiment analysis engine to assess and extract the user's emotional state (fatigue and anticipation) from the text.

[0911] 5. Response Generation

[0912] The server uses a generative AI model to generate appropriate responses and meal suggestions based on the user's intent and emotional state, such as, "You seem tired today. In that case, I'd recommend a warm soup or a nutritious salad. What do you think?"

[0913] 6. Sending a Response

[0914] The generated response is sent from the server to the user's terminal.

[0915] 7. Response Display

[0916] The user's device (smartphone or smart glasses) will display the received response on the screen, allowing the user to visually confirm the response and select and order a meal suggestion if necessary.

[0917] Specific examples

[0918] Example 1: Suggestions for when you're tired

[0919] (situation)

[0920] The user says to their smartphone, "I'm tired from work, so I want to eat something delicious."

[0921] (process)

[0922] 1. Request acceptance

[0923] 2. Speech recognition: "I'm tired from work and want to eat something delicious."

[0924] 3. Natural Language Processing: "I'm feeling tired and want something tasty to eat."

[0925] 4. Emotion analysis: Analyzing "fatigue" and "expectation"

[0926] 5. Response Generation: "I'd love a warm soup or a hearty salad. What do you think?"

[0927] 6. Sending a Response

[0928] 7. Response display: Responses and meal suggestions are displayed on the user's smartphone or smart glasses.

[0929] Prompt Sentence Examples

[0930] Suggestions for when you're tired

[0931] User request: "I'm tired from work, so I want to eat something delicious."

[0932] Response-generating prompt:

[0933] "What kind of food would you recommend for users when they're tired? Also, add some encouraging messages to help users relax."

[0934] Example response of the generative AI model:

[0935] "You must be tired today. In that case, I recommend a warm soup or a nutritious salad. Take a little rest and relax your body and mind."

[0936] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0937] Step 1: Request acceptance

[0938] The user speaks into their smartphone or smart glasses, "I'm tired from work, I want to eat something delicious." The voice data is sent to the server via a URL. This is the input. The server receives this voice data and prepares for the next step.

[0939] Step 2: Voice Recognition

[0940] The server uses a speech recognition system (e.g., Google Speech-to-Text) to convert the received voice data into text data. The input of this step is the voice data, and the output is the text "I'm tired from work, so I want to eat something delicious." The speech recognition system analyzes the voice waveform and generates a corresponding string of characters.

[0941] Step 3: Natural Language Processing

[0942] The server passes the generated text data to a natural language processing engine (e.g., GPT-4). The input of this step is text data, and the output is the intent, "I feel tired and want to eat something delicious." The natural language processing engine analyzes words and understands the context to determine the user's intent.

[0943] Step 4: Sentiment Analysis

[0944] The server passes the text data to a sentiment analysis engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The input for this step is the text data, and the output is the emotional states of "fatigue" and "expectation." The sentiment analysis engine identifies emotional keywords in the text and evaluates the emotion.

[0945] Step 5: Response Generation

[0946] The server uses a generative AI model to generate a response based on the user's intention and emotional state. The input for this step is the user's intention and emotional state, and the output is a response message such as, "You seem tired today, don't you? In that case, I recommend a warm soup or a nutritious salad. What do you think?" The generative AI model generates a sentence that continues the conversation in a natural way based on the prompt.

[0947] Step 6: Send response

[0948] The generated response message is sent from the server to the user's device (smartphone or smart glasses). The input of this step is the generated response message, and the output is the completion of message transmission. The server sends the message to the device via network communication.

[0949] Step 7: Display the response

[0950] The user's device (smartphone or smart glasses) displays the received response on its screen and, if necessary, plays it aloud using speech synthesis technology. The input for this step is the received response message, and the output is in a format that the user can confirm visually or audibly. The device's display and speaker are used to notify the user of the response content.

[0951] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0952] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0953] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0954] [Third embodiment]

[0955] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0956] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0957] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0958] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0959] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0960] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0961] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0962] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0963] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0964] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0965] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0966] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0967] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[0968] System program processing

[0969] Request reception

[0970] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[0971] Voice Recognition

[0972] The server uses a speech recognition system to convert the received voice data into text data. In this case, the server generates the text "I've been tired at work lately" from the voice.

[0973] Natural Language Processing

[0974] The server passes the transferred text to a natural language processing engine to analyze the user's intent. In this case, the NLP model analyzes the intent as "I feel tired and stressed."

[0975] Emotion analysis

[0976] The server evaluates the user's emotional state from this text and extracts emotional elements from the text, in this case identifying the emotions of "fatigue" and "stress."

[0977] Response Generation

[0978] The server uses generative AI to generate an appropriate response based on the user's intent and emotional state, generating a mentor message like, "Maybe you need a little break. Let's take a deep breath and relax."

[0979] Response Send

[0980] The server generates a response and sends it to the user's device. In this case, the response is sent back to the smartphone.

[0981] Response Display

[0982] The user's device (smartphone) receives the response and displays it on the screen as text or uses speech synthesis to communicate it to the user. The user can confirm the message by looking at it on the screen or by listening to it.

[0983] Specific examples

[0984] Example 1: When a user is stressed

[0985] (situation)

[0986] The user says to their smartphone, "I've been feeling tired at work lately."

[0987] (process)

[0988] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[0989] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[0990] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[0991] 4. Emotion analysis: The server analyzes the emotions "fatigue" and "stress."

[0992] 5. Response generation: The server uses a generative AI to generate a response such as, "Maybe you need a little break. Shall we take a deep breath and take a moment to relax?"

[0993] 6. Send response: The server sends the response to the smartphone.

[0994] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[0995] Example 2: User seeking advice

[0996] (situation)

[0997] A user says, "I want to start a new hobby. What should I do?"

[0998] (process)

[0999] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[1000] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[1001] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[1002] 4. Sentiment analysis: The server analyzes the text for the emotional elements of "curiosity" and "expectation."

[1003] 5. Response generation: The server uses a generation AI to generate a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[1004] 6. Send response: The server sends the response to the smartphone.

[1005] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[1006] In this way, the personal assistant system of the present invention generates an appropriate response to a user's natural language input and is used to reduce the user's sense of loneliness and stress.

[1007] The processing flow will be explained below.

[1008] Step 1:

[1009] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[1010] Step 2:

[1011] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[1012] Step 3:

[1013] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[1014] Step 4:

[1015] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[1016] Step 5:

[1017] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this case, the emotions "fatigue" and "stress" are analyzed.

[1018] Step 6:

[1019] The server uses generative AI to generate a response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[1020] Step 7:

[1021] The server sends the generated response to the user's device (smartphone). Again, the generated text response is transferred to the smartphone via the network.

[1022] Step 8:

[1023] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[1024] Step 9:

[1025] The user sees a message displayed on the device or hears a spoken response. For example, the user reads or hears the message, "You might need a little break. Would you like to take a moment to take a deep breath and relax?"

[1026] In this way, through each of the above steps, the LifeMentor system stays close to the user's heart and provides appropriate responses.

[1027] Example 1

[1028] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1029] In modern society, users often feel lonely and stressed, and appropriate support is needed to address these issues. Conventional personal assistant systems face the challenge of being unable to fully understand a user's emotions and intentions and provide appropriate responses. Therefore, a system that can accurately analyze a user's emotions and intentions and generate appropriate responses based on those analysis is needed.

[1030] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1031] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generative AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for transmitting received speech data to a speech recognition service, means for transmitting text data acquired from the speech recognition service to a natural language processing engine, means for transmitting intention information acquired from the natural language processing engine to an emotion analysis engine, means for generating a prompt sentence based on the emotion information acquired from the emotion analysis engine and inputting it to a generative AI model, and means for transmitting response data acquired from the generative AI model to the user's terminal. This makes it possible to accurately analyze the user's emotions and intentions and provide an appropriate response based on the analysis.

[1032] "Natural language input" refers to natural language, such as the words spoken or written by a user.

[1033] "Speech recognition" refers to the technology of analyzing speech and converting it into text data.

[1034] "Text data" refers to character data converted by speech recognition.

[1035] "Natural language processing" refers to the technology of analyzing text data to understand its meaning and intent.

[1036] "Emotion analysis" refers to the technology of identifying a user's emotions and psychological state from text data.

[1037] "Generative AI" refers to artificial intelligence that generates responses based on the user's intentions and emotional state.

[1038] A "prompt sentence" refers to an instruction sentence to be input into the generation AI.

[1039] "Terminal" refers to a device that a user directly operates (e.g., a smartphone or tablet).

[1040] "Speech Recognition Service" refers generally to any external cloud-based or on-premise service that provides speech recognition.

[1041] A "natural language processing engine" refers to software or a system in general that analyzes text data and understands the user's intent.

[1042] "Sentiment analysis engine" refers to software or a system in general that analyzes text data to identify a user's emotions.

[1043] "Generative AI model" refers to any artificial intelligence model that generates a response based on an input prompt.

[1044] "Response Data" refers to the text data of the response generated by the generative AI model.

[1045] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress. By combining generative AI and an emotion engine, we provide a system that generates responses that are close to the user's heart.

[1046] Specific system configuration

[1047] Hardware and Software

[1048] This system operates using the following hardware and software.

[1049] User device: smartphone, tablet, or computer

[1050] Server: Cloud-based or on-premise server

[1051] Speech Recognition Service: An external cloud service for converting voice input into text (e.g., Google Speech-to-Text API)

[1052] Natural language processing engine: Software for analyzing text data (e.g., IBM Watson NLP)

[1053] Sentiment analysis engine: Software for identifying emotions (e.g., Microsoft Azure Cognitive Services Text Analytics)

[1054] Generative AI model: artificial intelligence for generating responses (e.g., OpenAI GPT-4)

[1055] System operation explanation

[1056] Request reception

[1057] When a user speaks natural language into a smartphone, such as "I'm tired from work these days," the device captures the speech and transmits it to a server as voice data. The device is equipped with an appropriate microphone and communication interface to accept voice input.

[1058] Voice Recognition

[1059] The server sends the received voice data to a speech recognition service and converts the voice data into text data. For example, by using the Google Speech-to-Text API, speech can be converted into text with high accuracy.

[1060] Natural Language Processing

[1061] The server then sends the converted text data to a natural language processing engine to analyze the user's intent. Here, the IBM Watson NLP engine is used to identify the user's specific intent (e.g., "I feel tired and stressed") from the text data.

[1062] Emotion analysis

[1063] The server sends the parsed intent information to a sentiment analysis engine, which uses Microsoft Azure Cognitive Services Text Analytics to identify emotional elements (e.g., "fatigue" and "stress") contained within the text.

[1064] Response Generation

[1065] The server generates a prompt based on the identified intent and emotional information and inputs it into the generative AI model. An example of a prompt is, "The user is tired from work. Please generate a response recommending a break." OpenAI GPT-4 is used to generate a response, and an appropriate response is obtained, for example, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[1066] Response Send

[1067] The server sends the generated response data to the user's terminal, which receives the response and notifies the user.

[1068] Response Display

[1069] The device will display the received response as text on the screen or play it aloud using a speech synthesis function, allowing the user to confirm the response visually or audibly.

[1070] Specific examples

[1071] Example 1: When a user is stressed

[1072] User: "I've been feeling tired at work lately," says the smartphone.

[1073] Server: Receives the voice data and sends it to the voice recognition service.

[1074] Server: Obtain the text data "I've been tired at work lately."

[1075] Server: Sends text data to a natural language processing engine and analyzes intent.

[1076] Server: Sends intent information to the emotion analysis engine to identify emotions of "fatigue" and "stress."

[1077] Server: Generates a prompt sentence, "The user is tired from work. Please generate a response recommending a break." and inputs it into the generative AI model.

[1078] Server: The response obtained from GPT-4, "Maybe you need a break. Would you like to take a deep breath and relax?", is sent to the user's device.

[1079] Terminal: Display or play audible responses received.

[1080] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and is used to reduce the user's feelings of loneliness and stress.

[1081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1082] Processing Steps

[1083] Step 1: Request acceptance

[1084] Subject: User, Device

[1085] Action: The user speaks into the smartphone.

[1086] Input: User speech (e.g., "I've been feeling tired at work lately.")

[1087] Specific operation: The device uses a microphone to capture audio data, which is then sent to the server.

[1088] Output: The audio data is sent to the server.

[1089] Step 2: Voice Recognition

[1090] Subject: Server

[1091] How it works: The server sends the voice data to the voice recognition service.

[1092] Input: Audio data

[1093] Specific operation: The server sends the audio data to the Google Speech-to-Text API, which converts the audio into text data.

[1094] Output: Text data from the speech recognition service (e.g., "I've been feeling tired at work lately.")

[1095] Step 3: Natural Language Processing

[1096] Subject: Server

[1097] How it works: The server sends text data to a natural language processing engine.

[1098] Input: Text data

[1099] Specific operation: The server sends text data to the IBM Watson NLP engine to analyze the user's intent. The NLP engine analyzes the text and identifies meaning and intent.

[1100] Output: Intent information from the NLP engine (e.g., "I feel tired and stressed")

[1101] Step 4: Sentiment Analysis

[1102] Subject: Server

[1103] How it works: The server sends the intent information to the sentiment analysis engine.

[1104] Input: Intention information

[1105] Specific operation: The server sends the intent information to Microsoft Azure Cognitive Services Text Analytics, which analyzes the emotion. The emotion analysis engine identifies "fatigue" and "stress" from the text.

[1106] Output: Emotion information from the emotion analysis engine (e.g., "fatigue" and "stress")

[1107] Step 5: Response Generation

[1108] Subject: Server

[1109] How it works: The server generates prompt sentences based on emotional information and inputs them into the generative AI model.

[1110] Input: Emotion information

[1111] Specific operation: The server generates a prompt saying, "The user is tired from work. Please generate a response recommending a break." and sends it to OpenAI's GPT-4. The generative AI model generates a response based on the prompt.

[1112] Output: Response data from the generative AI model (e.g., "Maybe you need a break. Shall we take a moment to take a deep breath and relax?")

[1113] Step 6: Send response

[1114] Subject: Server

[1115] Action: The server generates a response and sends it to the user's device.

[1116] Input: Response data

[1117] Specific operation: The server sends response data to the smartphone.

[1118] Output: Delivery of response data to smartphone

[1119] Step 7: Display the response

[1120] Subject: Terminal

[1121] Action: The device displays or plays audibly the response it receives.

[1122] Input: Response data

[1123] What it does: Your smartphone will display the response on the screen as text, or play it aloud using its speech synthesis feature.

[1124] Output: The user confirms the response visually or audibly.

[1125] Thus, each processing step provides a detailed description of specific operations, showing how an appropriate response is generated based on the user's natural language input.

[1126] (Application example 1)

[1127] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1128] Conventional personal assistant systems generate responses based on natural language input from users, but lack the ability to provide specific advice and product suggestions to reduce users' feelings of loneliness and stress. In particular, there is a need for systems that can provide advice and help users reduce stress when shopping in virtual stores.

[1129] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1130] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, response generation means using a generative model for generating a response based on the user's intention and emotional state, means for providing the user with product suggestions and stress relief advice based on the generated response, means for transmitting the response to the user's terminal, and means for displaying or playing the response by audio on the user's terminal, thereby reducing the user's sense of loneliness and stress and improving the shopping experience in the virtual store.

[1131] "User" refers to an individual or end user who uses this system to communicate in natural language.

[1132] "Natural language input" refers to input that is given by a user in language and is expressed in voice or text format.

[1133] "Speech recognition" is a technology that converts voice data into text data.

[1134] "Text" refers to textual information generated by speech recognition.

[1135] "Natural language processing" is a technology for analyzing text data and understanding its intent and meaning.

[1136] "Emotion analysis" refers to a technology that identifies a user's emotional state from text data and extracts its emotional components.

[1137] "Generative model" refers to artificial intelligence for generating appropriate responses based on a user's intent and emotional state.

[1138] "Response generation means" refers to a function that uses a generative model to generate natural conversations and suggestions based on the user's intentions and emotional state.

[1139] "Terminal" refers to a device used by a user, including a smartphone, a head-mounted display (HMD), or other device.

[1140] "Response display" refers to the function of outputting the generated response to the user's terminal as text or voice.

[1141] "Product suggestions" refers to the function of recommending appropriate products and services based on the user's intentions and emotions.

[1142] "Stress relief advice" refers to a function that provides specific advice to reduce the stress and fatigue that the user is feeling.

[1143] "Generative AI means" refers to technology for generating responses using generative models.

[1144] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress and improves their shopping experience in virtual stores. The system combines generative AI and an emotion analysis engine to provide users with appropriate product suggestions and advice on how to relieve stress.

[1145] System configuration

[1146] Hardware

[1147] Terminal: A smart device used by a user (e.g., a smartphone, a head-mounted display (HMD)).

[1148] Microphone: Used to capture the user's voice input.

[1149] software

[1150] Speech recognition system: Uses the speech_recognition library to convert voice data into text data.

[1151] Natural Language Processing (NLP) engine: Performs natural language processing and analyzes user intent.

[1152] Sentiment analysis engine: Analyzes emotional elements from text data.

[1153] Generative Models: Use the transformers library to utilize generative AI (GPT-2) models.

[1154] Response generation means: Generates an appropriate response for the user.

[1155] System program processing

[1156] This system operates in the following steps:

[1157] 1. Speech Recognition:

[1158] The server receives user voice input through a microphone, and the voice data is converted to text data using the speech_recognition library.

[1159] 2. Natural Language Processing:

[1160] The server uses a natural language processing engine to analyze the received text data and determine the user's intent.

[1161] 3. Emotion analysis:

[1162] The server uses an emotion analysis engine to analyze the user's emotional state from the text data.

[1163] 4. Response Generation:

[1164] The server uses the generative model to generate appropriate responses based on the user's intentions and emotional state, such as product suggestions and stress-relief advice.

[1165] 5. Response transmission and display:

[1166] The server sends the generated response to the user's terminal, which displays or plays the response audibly.

[1167] Specific examples

[1168] Example 1: When a user is stressed

[1169] Situation: A user says into their smartphone, "I've been feeling tired at work lately."

[1170] process:

[1171] The speech recognition system converts the speech into text: "I've been feeling tired at work lately."

[1172] The natural language processing engine determines that the person is feeling tired and stressed.

[1173] The emotion analysis engine analyzes emotions such as "fatigue" and "stress."

[1174] The generative model generates a response like, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[1175] The server sends this response to the smartphone, which displays the response.

[1176] Example 2: User asks for new hobbies

[1177] Situation: The user speaks to the HMD, "I want to start a new hobby. What should I do?"

[1178] process:

[1179] The speech recognition system converts the speech into text: "I want to start a new hobby. What should I do?"

[1180] The natural language processing engine determines that the person is looking for suggestions for a new hobby.

[1181] The emotion analysis engine analyzes emotions such as "curiosity" and "expectation."

[1182] The generative model generates a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[1183] The server sends this response to the HMD, which displays or plays the response audibly.

[1184] Prompt Sentence Examples

[1185] "Recommend food suggestions based on the user's recent mood."

[1186] "What are some of your most popular items and services these days?"

[1187] As a result, users can receive support to reduce feelings of loneliness and stress while shopping at virtual stores and in their daily lives, and to lead richer lives.

[1188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1189] Step 1: Accepting voice input

[1190] The user speaks in natural language into the device (smartphone or HMD), which then captures the user's voice through a microphone. The input is the user's voice data, and the output is the captured voice data.

[1191] Step 2: Voice Recognition

[1192] The server uses a speech recognition system to convert the captured audio data into text data. Specifically, it uses the speech_recognition library. Audio data is the input and text data is the output.

[1193] Step 3: Natural Language Processing

[1194] The server passes the text data to a natural language processing engine, which analyzes the user's intent. For example, the NLP engine determines intent such as "I feel tired and stressed" or "I'm looking for a new hobby." The input is text data obtained by speech recognition, and the output is data about the user's intent.

[1195] Step 4: Sentiment Analysis

[1196] The server uses an emotion analysis engine to identify the user's emotional state from the text data. For example, emotions such as "fatigue" or "stress" are extracted. The input is text data obtained by natural language processing, and the output is data about the emotional state.

[1197] Step 5: Response Generation

[1198] The server uses a generative model to generate a prompt based on the user's intention and emotional state, and creates a response based on that. Specifically, it uses a generative AI model (GPT-2) using the transformers library. The input is a prompt based on the user's intention and emotional state, and the output is the generated response text.

[1199] Step 6: Send response

[1200] The server sends the generated response text to the user's terminal. The input is the generated response text, and the output is the response text sent to the user's terminal.

[1201] Step 7: Display the response

[1202] The user's device displays the received response text on the screen or plays it aloud using speech synthesis. Specific operations include displaying the text on the screen or playing it aloud using a speech synthesis system. The input is the transmitted response text, and the output is the response that is communicated to the user visually or audibly.

[1203] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1204] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[1205] System program processing

[1206] Request reception

[1207] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[1208] Voice Recognition

[1209] The server uses a speech recognition system to convert the received voice data into text data. In this case, the text "I've been tired at work lately" is generated from the voice.

[1210] Natural Language Processing

[1211] The server passes the transferred text data to a natural language processing (NLP) engine to determine the user's intent. In this case, the NLP model parses the intent as "I feel tired and stressed."

[1212] Emotion analysis

[1213] The server evaluates the user's emotional state from this text and uses an emotion engine to extract emotional elements from the text. In this case, the emotions "fatigue" and "stress" are analyzed. The emotion engine does this in real time, adaptively updating its emotion assessment based on changes in the frequency and content of user input.

[1214] Response Generation

[1215] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. The generative AI might generate a text response like, "Maybe you need a break. Let's take a deep breath and relax." If the emotion engine has learned the user's long-term emotional tendencies, the response can be further personalized.

[1216] Response Send

[1217] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[1218] Response Display

[1219] The user's device (smartphone) receives a response that is displayed as text on the screen or played aloud using a speech synthesis system. The user can see the message displayed on the screen or listen to it. The emotion engine may also visually display the user's emotional state.

[1220] Specific examples

[1221] Example 1: When a user is stressed

[1222] (situation)

[1223] The user says to their smartphone, "I've been feeling tired at work lately."

[1224] (process)

[1225] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[1226] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[1227] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[1228] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "fatigue" and "stress."

[1229] 5. Response generation: The server uses generative AI to generate a response such as, "Maybe you need a break. Let's take a deep breath and relax." It also generates a personalized response based on the user's long-term emotional trends.

[1230] 6. Send response: The server sends the response to the smartphone.

[1231] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[1232] Example 2: User seeking advice

[1233] (situation)

[1234] A user says, "I want to start a new hobby. What should I do?"

[1235] (process)

[1236] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[1237] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[1238] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[1239] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "curiosity" and "expectation."

[1240] 5. Response generation: The server uses generative AI to generate a response such as, "Gardening and programming are popular hobbies these days. If you're interested, why not give them a try?" It also incorporates the user's emotional tendencies to provide more personalized advice.

[1241] 6. Send response: The server sends the response to the smartphone.

[1242] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[1243] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[1244] The processing flow will be explained below.

[1245] Step 1:

[1246] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[1247] Step 2:

[1248] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[1249] Step 3:

[1250] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[1251] Step 4:

[1252] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[1253] Step 5:

[1254] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this example, the emotions "fatigue" and "stress" are analyzed. The sentiment analysis engine evaluates the emotion that matches the content of the text in real time.

[1255] Step 6:

[1256] The server uses the generative AI to generate an appropriate response based on the emotional data extracted by the emotion analysis engine. The generative AI generates a text response such as, "Maybe you need a little break. Shall we take a deep breath and relax?"

[1257] Step 7:

[1258] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[1259] Step 8:

[1260] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[1261] Step 9:

[1262] The user sees a message displayed on the device or hears a spoken response, such as "You might need a little break. Would you like to take a moment to breathe and relax?"

[1263] Step 10:

[1264] The server accepts new input data from the user and learns the user's long-term emotional trends. The emotion analysis engine uses the user's continuous data to evaluate emotional transitions and analyze long-term trends, which allows the next response to be more personalized.

[1265] In this way, a "Life Mentor" system can receive user input, analyze emotions, and generate appropriate responses based on those inputs, providing support tailored to individual situations, such as helping people relieve stress in their daily lives or suggesting new hobbies.

[1266] Example 2

[1267] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1268] In modern society, feelings of loneliness and stress experienced by users have become serious issues. Current personal assistant systems are unable to adequately address these emotional issues. This is due to a lack of technology to accurately grasp a user's emotional state and generate appropriate responses. Another problem is that it is difficult to provide personalized responses that incorporate a user's long-term emotional trends. As a result, users are unable to receive support that is in tune with their own emotions, and it is difficult to alleviate feelings of loneliness and stress.

[1269] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for visually displaying the user's emotional state, means for adaptively updating an emotion evaluation in accordance with changes in the user's input frequency and content, and means for personalizing the generated response by learning the user's long-term emotional tendency. This makes it possible to accurately grasp the user's emotional state and provide an appropriate and personalized response.

[1270] "User" refers to a person who uses the system to input natural language.

[1271] "Natural language input" refers to linguistic input that a user makes to a system using voice or text.

[1272] "Speech recognition" is a technology that converts voice data into text data.

[1273] "Text" refers to character string data generated based on natural language input.

[1274] "Analysis" refers to the process of understanding the content of text and determining the user's intent and sentiment.

[1275] "Natural language processing" is a technology that analyzes text data to determine the user's intent.

[1276] "Sentiment analysis" is a technique for assessing a user's emotional state from text and extracting specific emotional elements.

[1277] "Generative AI" refers to artificial intelligence that generates responses based on the user's intent and emotional state.

[1278] "Response" refers to the reply or message that the generating AI provides to the user.

[1279] "Terminal" refers to devices such as smartphones and personal computers used by users.

[1280] "Display" refers to displaying the generated response as text on the terminal screen.

[1281] "Voice playback" refers to playing back the generated response as voice using speech synthesis technology.

[1282] "Visual display" refers to a display means for visually representing a user's emotional state.

[1283] "Adaptive" refers to dynamically responding to changes in the frequency and content of user input.

[1284] "Personalization" refers to learning a user's long-term emotional tendencies and responding to them individually.

[1285] The present invention relates to a personal assistant system for reducing a user's feelings of loneliness and stress. This system accepts natural language input and generates a response that is in tune with the user's feelings by combining a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[1286] Hardware and Software Overview

[1287] server:

[1288] The servers have central processing units (CPUs) that perform speech recognition, natural language processing, sentiment analysis, and generative AI functions. The servers are deployed on high-performance cloud infrastructure, such as Google Cloud Platform or Amazon Web Services.

[1289] Device:

[1290] The device refers to the smartphone or computer used by the user, which is equipped with audio capture and network connectivity functions.

[1291] Program processing

[1292] Request reception

[1293] When a user says to their smartphone, "I've been feeling tired at work lately," the smartphone sends the voice data to the server.

[1294] Voice Recognition

[1295] The server uses Google Cloud Speech-to-Text to convert the received voice data into text data, generating the text "I've been tired at work lately" from the voice.

[1296] Natural Language Processing

[1297] The server passes the generated text data to a natural language processing engine (such as OpenAI's GPT-3) to determine the user's intent. In this case, the NLP model analyzes the intent as "feeling tired and stressed."

[1298] Emotion analysis

[1299] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotional elements in the text, extracting the emotions of "fatigue" and "stress."

[1300] Response Generation

[1301] The server uses a generative AI (such as OpenAI's ChatGPT) to generate an appropriate response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a break. Shall we take a moment to take a deep breath and relax?"

[1302] Response Send

[1303] The server generates a response and sends it to the user's device (smartphone). The response is then transferred to the smartphone via the network.

[1304] Response Display

[1305] The user's device (smartphone) receives a response, which is then displayed as text on the screen or played aloud using a speech synthesis system such as AWS Polly. The user can then see the message displayed on the screen or listen to it. In addition, an emotion engine visually displays the user's emotional state.

[1306] Specific examples

[1307] Example 1: When a user is stressed

[1308] (situation)

[1309] The user says to their smartphone, "I've been feeling tired at work lately."

[1310] (Processing) Based on this statement, the server converts the speech into text data, performs sentiment analysis, and generates and sends an appropriate response.

[1311] Example 2: User seeking advice

[1312] (situation)

[1313] A user says, "I want to start a new hobby. What should I do?"

[1314] (Processing) Based on this utterance, the server converts the speech into text data, analyzes emotions, and generates a response suggesting recommended hobbies.

[1315] Prompt Sentence Examples

[1316] Stress prompts

[1317] “If a user expresses that they are tired, what words of encouragement would you offer them?”

[1318] New Hobby Prompt

[1319] "When a user is looking for a new hobby, how can we suggest trending hobbies?"

[1320] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[1321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1322] Step 1:

[1323] The user speaks into the input device (smartphone) saying, "I've been feeling tired at work lately." Voice input is performed, and the smartphone captures this voice data. This captured voice data becomes the input data used in the next processing step.

[1324] Step 2:

[1325] The device (smartphone) sends the captured audio data to the server. The device uses a network connection to transfer this audio data to the server in real time. The server receives this audio data and uses it in the next processing step.

[1326] Step 3:

[1327] The server uses a speech recognition system to convert the received voice data into text data. Specifically, Google Cloud Speech-to-Text is used to convert the voice data into text such as "I've been feeling tired at work lately." The input is voice data, and the output is text data.

[1328] Step 4:

[1329] The server passes the generated text data to a natural language processing (NLP) engine to determine the user's intent. Specifically, OpenAI's GPT-3 is used to analyze the intent of "feeling tired and stressed." The input is text data, and the output is data indicating the user's intent.

[1330] Step 5:

[1331] The server uses an emotion engine to analyze the emotional elements in the text data. It uses IBM Watson Tone Analyzer to extract the emotions of "fatigue" and "stress." The input is text data, and the output is data indicating the emotional state.

[1332] Step 6:

[1333] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. It uses OpenAI's ChatGPT to generate a text response such as, "Maybe you need a break. Let's take a deep breath and relax." The input is the user's intent and emotional data, and the output is a text response.

[1334] Step 7:

[1335] The server generates a text response and sends it to the user's device (smartphone). The server then transfers the text response to the smartphone via the network. The input is the text response, and the output is data sent to the device.

[1336] Step 8:

[1337] The terminal receives the text response sent from the server. The terminal receives this data via the network and uses it in the next processing step. The input is the text response data from the server.

[1338] Step 9:

[1339] The device displays or plays audible the text response it receives. Specifically, it uses AWS Polly to synthesize speech and plays a message to the user audibly or displays it as text on the screen. The input is the text response data, and the output is visual or auditory feedback to the user.

[1340] This series of processes makes it possible to generate and provide an appropriate response to the emotions and intentions expressed by the user in natural language.

[1341] (Application example 2)

[1342] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1343] Conventional personal assistant systems often only generate responses by analyzing the user's natural language input and emotional state, and have the problem of not being able to suggest products or services that correspond to the user's specific requests and emotions. For this reason, there has been a demand for systems that not only reduce the sense of loneliness and stress felt by users, but also improve the user experience by suggesting specific actions.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1345] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, and means for suggesting products and services to the user based on the response. This makes it possible to provide a response that is in tune with the user's emotions while improving the user experience by suggesting specific products and services.

[1346] "Natural language input" refers to input made by a user in natural language, in the form of voice or text.

[1347] "Speech recognition" is a technology for converting voice data into text data, and is a technology for acquiring user utterances as text information.

[1348] "Text" refers to character information generated by speech recognition, and is a string of characters that expresses the content of a user's speech.

[1349] "Natural language processing" is a technology for analyzing natural language text to understand its meaning and intent, and is a means of accurately determining the user's intent.

[1350] "Sentiment analysis" is a technique for assessing and extracting a user's emotional state from text, and is a means of understanding a user's psychological state.

[1351] "Generative AI" is a technology that uses artificial intelligence to generate responses based on a user's intent and emotional state, and is a means of creating natural conversations.

[1352] "User terminal" refers to a device such as a smartphone, smart glasses, or head-mounted display on which responses can be received and displayed.

[1353] "Response display" refers to displaying the generated response on the user's terminal, allowing the user to visually confirm the response content.

[1354] "Audio playback" refers to the use of speech synthesis technology to transmit the generated response to the user audibly, allowing the user to confirm the response content through their hearing.

[1355] "Product and service suggestions" refers to introducing specific products and services that correspond to the user's emotions and intentions based on the generated responses, and suggests actions to satisfy the user's needs.

[1356] System Configuration

[1357] The present invention relates to a personal assistant system for reducing a user's sense of loneliness and stress, and is implemented by combining the following hardware and software:

[1358] Hardware used

[1359] Smartphone (iPhone, Android, etc.)

[1360] Smart glasses (general name)

[1361] Head-mounted display (general name)

[1362] Server (cloud-based: AWS, Google Cloud Platform, etc.)

[1363] Software used

[1364] Speech recognition systems (Google Speech-to-Text, IBM Watson, etc.)

[1365] Natural language processing engine (GPT-4, BERT, etc.)

[1366] Sentiment analysis engine (Microsoft Azure Emotion API, etc.)

[1367] Generative AI models (OpenAI GPT-4, etc.)

[1368] Program processing and data processing

[1369] 1. Request acceptance

[1370] When a user says something like "I'm tired from work, so I want to eat something delicious" via a smartphone or smart glasses, the voice data is sent to the server, where it is converted into text data using a voice recognition system.

[1371] 2. Voice Recognition

[1372] The server uses a speech recognition system (such as Google Speech-to-Text) to convert the received voice data into text data, which generates the text "I'm tired from work, so I want to eat something delicious."

[1373] 3. Natural Language Processing

[1374] The server passes this text data to a natural language processing (NLP) engine to analyze the user's intent. In this case, the NLP engine (such as GPT-4) determines the intent as "I'm feeling tired and want to eat something delicious."

[1375] 4. Emotion analysis

[1376] The server uses a sentiment analysis engine to assess and extract the user's emotional state (fatigue and anticipation) from the text.

[1377] 5. Response Generation

[1378] The server uses a generative AI model to generate appropriate responses and meal suggestions based on the user's intent and emotional state, such as, "You seem tired today. In that case, I'd recommend a warm soup or a nutritious salad. What do you think?"

[1379] 6. Sending a Response

[1380] The generated response is sent from the server to the user's terminal.

[1381] 7. Response Display

[1382] The user's device (smartphone or smart glasses) will display the received response on the screen, allowing the user to visually confirm the response and select and order a meal suggestion if necessary.

[1383] Specific examples

[1384] Example 1: Suggestions for when you're tired

[1385] (situation)

[1386] The user says to their smartphone, "I'm tired from work, so I want to eat something delicious."

[1387] (process)

[1388] 1. Request acceptance

[1389] 2. Speech recognition: "I'm tired from work and want to eat something delicious."

[1390] 3. Natural Language Processing: "I'm feeling tired and want something tasty to eat."

[1391] 4. Emotion analysis: Analyzing "fatigue" and "expectation"

[1392] 5. Response Generation: "I'd love a warm soup or a hearty salad. What do you think?"

[1393] 6. Sending a Response

[1394] 7. Response display: Responses and meal suggestions are displayed on the user's smartphone or smart glasses.

[1395] Prompt Sentence Examples

[1396] Suggestions for when you're tired

[1397] User request: "I'm tired from work, so I want to eat something delicious."

[1398] Response-generating prompt:

[1399] "What kind of food would you recommend for users when they're tired? Also, add some encouraging messages to help users relax."

[1400] Example response of the generative AI model:

[1401] "You must be tired today. In that case, I recommend a warm soup or a nutritious salad. Take a little rest and relax your body and mind."

[1402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1403] Step 1: Request acceptance

[1404] The user speaks into their smartphone or smart glasses, "I'm tired from work, I want to eat something delicious." The voice data is sent to the server via a URL. This is the input. The server receives this voice data and prepares for the next step.

[1405] Step 2: Voice Recognition

[1406] The server uses a speech recognition system (e.g., Google Speech-to-Text) to convert the received voice data into text data. The input of this step is the voice data, and the output is the text "I'm tired from work, so I want to eat something delicious." The speech recognition system analyzes the voice waveform and generates a corresponding string of characters.

[1407] Step 3: Natural Language Processing

[1408] The server passes the generated text data to a natural language processing engine (e.g., GPT-4). The input of this step is text data, and the output is the intent, "I feel tired and want to eat something delicious." The natural language processing engine analyzes words and understands the context to determine the user's intent.

[1409] Step 4: Sentiment Analysis

[1410] The server passes the text data to a sentiment analysis engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The input for this step is the text data, and the output is the emotional states of "fatigue" and "expectation." The sentiment analysis engine identifies emotional keywords in the text and evaluates the emotion.

[1411] Step 5: Response Generation

[1412] The server uses a generative AI model to generate a response based on the user's intention and emotional state. The input for this step is the user's intention and emotional state, and the output is a response message such as, "You seem tired today, don't you? In that case, I recommend a warm soup or a nutritious salad. What do you think?" The generative AI model generates a sentence that continues the conversation in a natural way based on the prompt.

[1413] Step 6: Send response

[1414] The generated response message is sent from the server to the user's device (smartphone or smart glasses). The input of this step is the generated response message, and the output is the completion of message transmission. The server sends the message to the device via network communication.

[1415] Step 7: Display the response

[1416] The user's device (smartphone or smart glasses) displays the received response on its screen and, if necessary, plays it aloud using speech synthesis technology. The input for this step is the received response message, and the output is in a format that the user can confirm visually or audibly. The device's display and speaker are used to notify the user of the response content.

[1417] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1418] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1419] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1420] [Fourth embodiment]

[1421] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1422] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1423] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1424] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1425] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1426] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1427] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1428] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1429] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1430] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1431] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1432] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1433] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1434] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[1435] System program processing

[1436] Request reception

[1437] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[1438] Voice Recognition

[1439] The server uses a speech recognition system to convert the received voice data into text data. In this case, the server generates the text "I've been tired at work lately" from the voice.

[1440] Natural Language Processing

[1441] The server passes the transferred text to a natural language processing engine to analyze the user's intent. In this case, the NLP model analyzes the intent as "I feel tired and stressed."

[1442] Emotion analysis

[1443] The server evaluates the user's emotional state from this text and extracts emotional elements from the text, in this case identifying the emotions of "fatigue" and "stress."

[1444] Response Generation

[1445] The server uses generative AI to generate an appropriate response based on the user's intent and emotional state, generating a mentor message like, "Maybe you need a little break. Let's take a deep breath and relax."

[1446] Response Send

[1447] The server generates a response and sends it to the user's device. In this case, the response is sent back to the smartphone.

[1448] Response Display

[1449] The user's device (smartphone) receives the response and displays it on the screen as text or uses speech synthesis to communicate it to the user. The user can confirm the message by looking at it on the screen or by listening to it.

[1450] Specific examples

[1451] Example 1: When a user is stressed

[1452] (situation)

[1453] The user says to their smartphone, "I've been feeling tired at work lately."

[1454] (process)

[1455] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[1456] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[1457] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[1458] 4. Emotion analysis: The server analyzes the emotions "fatigue" and "stress."

[1459] 5. Response generation: The server uses a generative AI to generate a response such as, "Maybe you need a little break. Shall we take a deep breath and take a moment to relax?"

[1460] 6. Send response: The server sends the response to the smartphone.

[1461] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[1462] Example 2: User seeking advice

[1463] (situation)

[1464] A user says, "I want to start a new hobby. What should I do?"

[1465] (process)

[1466] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[1467] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[1468] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[1469] 4. Sentiment analysis: The server analyzes the text for the emotional elements of "curiosity" and "expectation."

[1470] 5. Response generation: The server uses a generation AI to generate a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[1471] 6. Send response: The server sends the response to the smartphone.

[1472] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[1473] In this way, the personal assistant system of the present invention generates an appropriate response to a user's natural language input and is used to reduce the user's sense of loneliness and stress.

[1474] The processing flow will be explained below.

[1475] Step 1:

[1476] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[1477] Step 2:

[1478] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[1479] Step 3:

[1480] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[1481] Step 4:

[1482] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[1483] Step 5:

[1484] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this case, the emotions "fatigue" and "stress" are analyzed.

[1485] Step 6:

[1486] The server uses generative AI to generate a response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[1487] Step 7:

[1488] The server sends the generated response to the user's device (smartphone). Again, the generated text response is transferred to the smartphone via the network.

[1489] Step 8:

[1490] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[1491] Step 9:

[1492] The user sees a message displayed on the device or hears a spoken response. For example, the user reads or hears the message, "You might need a little break. Would you like to take a moment to take a deep breath and relax?"

[1493] In this way, through each of the above steps, the LifeMentor system stays close to the user's heart and provides appropriate responses.

[1494] Example 1

[1495] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1496] In modern society, users often feel lonely and stressed, and appropriate support is needed to address these issues. Conventional personal assistant systems face the challenge of being unable to fully understand a user's emotions and intentions and provide appropriate responses. Therefore, a system that can accurately analyze a user's emotions and intentions and generate appropriate responses based on those analysis is needed.

[1497] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1498] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generative AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for transmitting received speech data to a speech recognition service, means for transmitting text data acquired from the speech recognition service to a natural language processing engine, means for transmitting intention information acquired from the natural language processing engine to an emotion analysis engine, means for generating a prompt sentence based on the emotion information acquired from the emotion analysis engine and inputting it to a generative AI model, and means for transmitting response data acquired from the generative AI model to the user's terminal. This makes it possible to accurately analyze the user's emotions and intentions and provide an appropriate response based on the analysis.

[1499] "Natural language input" refers to natural language, such as the words spoken or written by a user.

[1500] "Speech recognition" refers to the technology of analyzing speech and converting it into text data.

[1501] "Text data" refers to character data converted by speech recognition.

[1502] "Natural language processing" refers to the technology of analyzing text data to understand its meaning and intent.

[1503] "Emotion analysis" refers to the technology of identifying a user's emotions and psychological state from text data.

[1504] "Generative AI" refers to artificial intelligence that generates responses based on the user's intentions and emotional state.

[1505] A "prompt sentence" refers to an instruction sentence to be input into the generation AI.

[1506] "Terminal" refers to a device that a user directly operates (e.g., a smartphone or tablet).

[1507] "Speech Recognition Service" refers generally to any external cloud-based or on-premise service that provides speech recognition.

[1508] A "natural language processing engine" refers to software or a system in general that analyzes text data and understands the user's intent.

[1509] "Sentiment analysis engine" refers to software or a system in general that analyzes text data to identify a user's emotions.

[1510] "Generative AI model" refers to any artificial intelligence model that generates a response based on an input prompt.

[1511] "Response Data" refers to the text data of the response generated by the generative AI model.

[1512] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress. By combining generative AI and an emotion engine, we provide a system that generates responses that are close to the user's heart.

[1513] Specific system configuration

[1514] Hardware and Software

[1515] This system operates using the following hardware and software.

[1516] User device: smartphone, tablet, or computer

[1517] Server: Cloud-based or on-premise server

[1518] Speech Recognition Service: An external cloud service for converting voice input into text (e.g., Google Speech-to-Text API)

[1519] Natural language processing engine: Software for analyzing text data (e.g., IBM Watson NLP)

[1520] Sentiment analysis engine: Software for identifying emotions (e.g., Microsoft Azure Cognitive Services Text Analytics)

[1521] Generative AI model: artificial intelligence for generating responses (e.g., OpenAI GPT-4)

[1522] System operation explanation

[1523] Request reception

[1524] When a user speaks natural language into a smartphone, such as "I'm tired from work these days," the device captures the speech and transmits it to a server as voice data. The device is equipped with an appropriate microphone and communication interface to accept voice input.

[1525] Voice Recognition

[1526] The server sends the received voice data to a speech recognition service and converts the voice data into text data. For example, by using the Google Speech-to-Text API, speech can be converted into text with high accuracy.

[1527] Natural Language Processing

[1528] The server then sends the converted text data to a natural language processing engine to analyze the user's intent. Here, the IBM Watson NLP engine is used to identify the user's specific intent (e.g., "I feel tired and stressed") from the text data.

[1529] Emotion analysis

[1530] The server sends the parsed intent information to a sentiment analysis engine, which uses Microsoft Azure Cognitive Services Text Analytics to identify emotional elements (e.g., "fatigue" and "stress") contained within the text.

[1531] Response Generation

[1532] The server generates a prompt based on the identified intent and emotional information and inputs it into the generative AI model. An example of a prompt is, "The user is tired from work. Please generate a response recommending a break." OpenAI GPT-4 is used to generate a response, and an appropriate response is obtained, for example, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[1533] Response Send

[1534] The server sends the generated response data to the user's terminal, which receives the response and notifies the user.

[1535] Response Display

[1536] The device will display the received response as text on the screen or play it aloud using a speech synthesis function, allowing the user to confirm the response visually or audibly.

[1537] Specific examples

[1538] Example 1: When a user is stressed

[1539] User: "I've been feeling tired at work lately," says the smartphone.

[1540] Server: Receives the voice data and sends it to the voice recognition service.

[1541] Server: Obtain the text data "I've been tired at work lately."

[1542] Server: Sends text data to a natural language processing engine and analyzes intent.

[1543] Server: Sends intent information to the emotion analysis engine to identify emotions of "fatigue" and "stress."

[1544] Server: Generates a prompt sentence, "The user is tired from work. Please generate a response recommending a break." and inputs it into the generative AI model.

[1545] Server: The response obtained from GPT-4, "Maybe you need a break. Would you like to take a deep breath and relax?", is sent to the user's device.

[1546] Terminal: Display or play audible responses received.

[1547] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and is used to reduce the user's feelings of loneliness and stress.

[1548] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1549] Processing Steps

[1550] Step 1: Request acceptance

[1551] Subject: User, Device

[1552] Action: The user speaks into the smartphone.

[1553] Input: User speech (e.g., "I've been feeling tired at work lately.")

[1554] Specific operation: The device uses a microphone to capture audio data, which is then sent to the server.

[1555] Output: The audio data is sent to the server.

[1556] Step 2: Voice Recognition

[1557] Subject: Server

[1558] How it works: The server sends the voice data to the voice recognition service.

[1559] Input: Audio data

[1560] Specific operation: The server sends the audio data to the Google Speech-to-Text API, which converts the audio into text data.

[1561] Output: Text data from the speech recognition service (e.g., "I've been feeling tired at work lately.")

[1562] Step 3: Natural Language Processing

[1563] Subject: Server

[1564] How it works: The server sends text data to a natural language processing engine.

[1565] Input: Text data

[1566] Specific operation: The server sends text data to the IBM Watson NLP engine to analyze the user's intent. The NLP engine analyzes the text and identifies meaning and intent.

[1567] Output: Intent information from the NLP engine (e.g., "I feel tired and stressed")

[1568] Step 4: Sentiment Analysis

[1569] Subject: Server

[1570] How it works: The server sends the intent information to the sentiment analysis engine.

[1571] Input: Intention information

[1572] Specific operation: The server sends the intent information to Microsoft Azure Cognitive Services Text Analytics, which analyzes the emotion. The emotion analysis engine identifies "fatigue" and "stress" from the text.

[1573] Output: Emotion information from the emotion analysis engine (e.g., "fatigue" and "stress")

[1574] Step 5: Response Generation

[1575] Subject: Server

[1576] How it works: The server generates prompt sentences based on emotional information and inputs them into the generative AI model.

[1577] Input: Emotion information

[1578] Specific operation: The server generates a prompt saying, "The user is tired from work. Please generate a response recommending a break." and sends it to OpenAI's GPT-4. The generative AI model generates a response based on the prompt.

[1579] Output: Response data from the generative AI model (e.g., "Maybe you need a break. Shall we take a moment to take a deep breath and relax?")

[1580] Step 6: Send response

[1581] Subject: Server

[1582] Action: The server generates a response and sends it to the user's device.

[1583] Input: Response data

[1584] Specific operation: The server sends response data to the smartphone.

[1585] Output: Delivery of response data to smartphone

[1586] Step 7: Display the response

[1587] Subject: Terminal

[1588] Action: The device displays or plays audibly the response it receives.

[1589] Input: Response data

[1590] What it does: Your smartphone will display the response on the screen as text, or play it aloud using its speech synthesis feature.

[1591] Output: The user confirms the response visually or audibly.

[1592] Thus, each processing step provides a detailed description of specific operations, showing how an appropriate response is generated based on the user's natural language input.

[1593] (Application example 1)

[1594] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1595] Conventional personal assistant systems generate responses based on natural language input from users, but lack the ability to provide specific advice and product suggestions to reduce users' feelings of loneliness and stress. In particular, there is a need for systems that can provide advice and help users reduce stress when shopping in virtual stores.

[1596] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1597] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, response generation means using a generative model for generating a response based on the user's intention and emotional state, means for providing the user with product suggestions and stress relief advice based on the generated response, means for transmitting the response to the user's terminal, and means for displaying or playing the response by audio on the user's terminal, thereby reducing the user's sense of loneliness and stress and improving the shopping experience in the virtual store.

[1598] "User" refers to an individual or end user who uses this system to communicate in natural language.

[1599] "Natural language input" refers to input that is given by a user in language and is expressed in voice or text format.

[1600] "Speech recognition" is a technology that converts voice data into text data.

[1601] "Text" refers to textual information generated by speech recognition.

[1602] "Natural language processing" is a technology for analyzing text data and understanding its intent and meaning.

[1603] "Emotion analysis" refers to a technology that identifies a user's emotional state from text data and extracts its emotional components.

[1604] "Generative model" refers to artificial intelligence for generating appropriate responses based on a user's intent and emotional state.

[1605] "Response generation means" refers to a function that uses a generative model to generate natural conversations and suggestions based on the user's intentions and emotional state.

[1606] "Terminal" refers to a device used by a user, including a smartphone, a head-mounted display (HMD), or other device.

[1607] "Response display" refers to the function of outputting the generated response to the user's terminal as text or voice.

[1608] "Product suggestions" refers to the function of recommending appropriate products and services based on the user's intentions and emotions.

[1609] "Stress relief advice" refers to a function that provides specific advice to reduce the stress and fatigue that the user is feeling.

[1610] "Generative AI means" refers to technology for generating responses using generative models.

[1611] This invention relates to a personal assistant system that reduces users' feelings of loneliness and stress and improves their shopping experience in virtual stores. The system combines generative AI and an emotion analysis engine to provide users with appropriate product suggestions and advice on how to relieve stress.

[1612] System configuration

[1613] Hardware

[1614] Terminal: A smart device used by a user (e.g., a smartphone, a head-mounted display (HMD)).

[1615] Microphone: Used to capture the user's voice input.

[1616] software

[1617] Speech recognition system: Uses the speech_recognition library to convert voice data into text data.

[1618] Natural Language Processing (NLP) engine: Performs natural language processing and analyzes user intent.

[1619] Sentiment analysis engine: Analyzes emotional elements from text data.

[1620] Generative Models: Use the transformers library to utilize generative AI (GPT-2) models.

[1621] Response generation means: Generates an appropriate response for the user.

[1622] System program processing

[1623] This system operates in the following steps:

[1624] 1. Speech Recognition:

[1625] The server receives user voice input through a microphone, and the voice data is converted to text data using the speech_recognition library.

[1626] 2. Natural Language Processing:

[1627] The server uses a natural language processing engine to analyze the received text data and determine the user's intent.

[1628] 3. Emotion analysis:

[1629] The server uses an emotion analysis engine to analyze the user's emotional state from the text data.

[1630] 4. Response Generation:

[1631] The server uses the generative model to generate appropriate responses based on the user's intentions and emotional state, such as product suggestions and stress-relief advice.

[1632] 5. Response transmission and display:

[1633] The server sends the generated response to the user's terminal, which displays or plays the response audibly.

[1634] Specific examples

[1635] Example 1: When a user is stressed

[1636] Situation: A user says into their smartphone, "I've been feeling tired at work lately."

[1637] process:

[1638] The speech recognition system converts the speech into text: "I've been feeling tired at work lately."

[1639] The natural language processing engine determines that the person is feeling tired and stressed.

[1640] The emotion analysis engine analyzes emotions such as "fatigue" and "stress."

[1641] The generative model generates a response like, "Maybe you need a little break. Would you like to take a deep breath and relax?"

[1642] The server sends this response to the smartphone, which displays the response.

[1643] Example 2: User asks for new hobbies

[1644] Situation: The user speaks to the HMD, "I want to start a new hobby. What should I do?"

[1645] process:

[1646] The speech recognition system converts the speech into text: "I want to start a new hobby. What should I do?"

[1647] The natural language processing engine determines that the person is looking for suggestions for a new hobby.

[1648] The emotion analysis engine analyzes emotions such as "curiosity" and "expectation."

[1649] The generative model generates a response such as, "Popular hobbies these days include gardening and programming. If you're interested, why not give them a try?"

[1650] The server sends this response to the HMD, which displays or plays the response audibly.

[1651] Prompt Sentence Examples

[1652] "Recommend food suggestions based on the user's recent mood."

[1653] "What are some of your most popular items and services these days?"

[1654] As a result, users can receive support to reduce feelings of loneliness and stress while shopping at virtual stores and in their daily lives, and to lead richer lives.

[1655] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1656] Step 1: Accepting voice input

[1657] The user speaks in natural language into the device (smartphone or HMD), which then captures the user's voice through a microphone. The input is the user's voice data, and the output is the captured voice data.

[1658] Step 2: Voice Recognition

[1659] The server uses a speech recognition system to convert the captured audio data into text data. Specifically, it uses the speech_recognition library. Audio data is the input and text data is the output.

[1660] Step 3: Natural Language Processing

[1661] The server passes the text data to a natural language processing engine, which analyzes the user's intent. For example, the NLP engine determines intent such as "I feel tired and stressed" or "I'm looking for a new hobby." The input is text data obtained by speech recognition, and the output is data about the user's intent.

[1662] Step 4: Sentiment Analysis

[1663] The server uses an emotion analysis engine to identify the user's emotional state from the text data. For example, emotions such as "fatigue" or "stress" are extracted. The input is text data obtained by natural language processing, and the output is data about the emotional state.

[1664] Step 5: Response Generation

[1665] The server uses a generative model to generate a prompt based on the user's intention and emotional state, and creates a response based on that. Specifically, it uses a generative AI model (GPT-2) using the transformers library. The input is a prompt based on the user's intention and emotional state, and the output is the generated response text.

[1666] Step 6: Send response

[1667] The server sends the generated response text to the user's terminal. The input is the generated response text, and the output is the response text sent to the user's terminal.

[1668] Step 7: Display the response

[1669] The user's device displays the received response text on the screen or plays it aloud using speech synthesis. Specific operations include displaying the text on the screen or playing it aloud using a speech synthesis system. The input is the transmitted response text, and the output is the response that is communicated to the user visually or audibly.

[1670] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1671] The present invention relates to a personal assistant system for reducing the user's feelings of loneliness and stress, and generates responses that are considerate to the user's feelings by combining a generative AI and an emotion engine. Below, we will explain the program processing and specific examples of the system of the present invention.

[1672] System program processing

[1673] Request reception

[1674] The server receives requests from users. When a user says to their smartphone, "I'm tired from work these days," the smartphone sends the voice data to the server.

[1675] Voice Recognition

[1676] The server uses a speech recognition system to convert the received voice data into text data. In this case, the text "I've been tired at work lately" is generated from the voice.

[1677] Natural Language Processing

[1678] The server passes the transferred text data to a natural language processing (NLP) engine to determine the user's intent. In this case, the NLP model parses the intent as "I feel tired and stressed."

[1679] Emotion analysis

[1680] The server evaluates the user's emotional state from this text and uses an emotion engine to extract emotional elements from the text. In this case, the emotions "fatigue" and "stress" are analyzed. The emotion engine does this in real time, adaptively updating its emotion assessment based on changes in the frequency and content of user input.

[1681] Response Generation

[1682] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. The generative AI might generate a text response like, "Maybe you need a break. Let's take a deep breath and relax." If the emotion engine has learned the user's long-term emotional tendencies, the response can be further personalized.

[1683] Response Send

[1684] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[1685] Response Display

[1686] The user's device (smartphone) receives a response that is displayed as text on the screen or played aloud using a speech synthesis system. The user can see the message displayed on the screen or listen to it. The emotion engine may also visually display the user's emotional state.

[1687] Specific examples

[1688] Example 1: When a user is stressed

[1689] (situation)

[1690] The user says to their smartphone, "I've been feeling tired at work lately."

[1691] (process)

[1692] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[1693] 2. Speech recognition: The server converts the speech data into text such as "I've been feeling tired at work lately."

[1694] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the person is feeling tired and stressed.

[1695] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "fatigue" and "stress."

[1696] 5. Response generation: The server uses generative AI to generate a response such as, "Maybe you need a break. Let's take a deep breath and relax." It also generates a personalized response based on the user's long-term emotional trends.

[1697] 6. Send response: The server sends the response to the smartphone.

[1698] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[1699] Example 2: User seeking advice

[1700] (situation)

[1701] A user says, "I want to start a new hobby. What should I do?"

[1702] (process)

[1703] 1. Request acceptance: The server accepts the voice data sent from the smartphone.

[1704] 2. Speech recognition: The server converts the speech data into text such as "I want to start a new hobby, what should I do?"

[1705] 3. Natural Language Processing: The server analyzes this text using an NLP engine and determines that the user is looking for suggestions for new hobbies.

[1706] 4. Emotion analysis: The server uses an emotion engine to analyze the emotions of "curiosity" and "expectation."

[1707] 5. Response generation: The server uses generative AI to generate a response such as, "Gardening and programming are popular hobbies these days. If you're interested, why not give them a try?" It also incorporates the user's emotional tendencies to provide more personalized advice.

[1708] 6. Send response: The server sends the response to the smartphone.

[1709] 7. Display response: The smartphone displays the received response on the screen for the user to confirm.

[1710] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[1711] The processing flow will be explained below.

[1712] Step 1:

[1713] The user speaks into their smartphone, saying, "I've been feeling tired at work lately." The device (smartphone) accepts this voice input.

[1714] Step 2:

[1715] The terminal sends the received voice data to the server, where it is transferred to the server via the network.

[1716] Step 3:

[1717] The server receives the voice data. It then activates an Automatic Speech Recognition (ASR) system on the server and converts the voice data into text data. For example, the generated text might say, "I've been feeling tired at work lately."

[1718] Step 4:

[1719] The server passes the generated text data to a natural language processing (NLP) engine, which analyzes the text and determines the user's intent. In this case, the intent is "feeling tired and stressed."

[1720] Step 5:

[1721] Based on the analyzed intent, the server uses a sentiment analysis engine to extract emotional data from the text. In this example, the emotions "fatigue" and "stress" are analyzed. The sentiment analysis engine evaluates the emotion that matches the content of the text in real time.

[1722] Step 6:

[1723] The server uses the generative AI to generate an appropriate response based on the emotional data extracted by the emotion analysis engine. The generative AI generates a text response such as, "Maybe you need a little break. Shall we take a deep breath and relax?"

[1724] Step 7:

[1725] The server sends the generated response to the user's device (smartphone). In this case, the generated text response is transferred to the smartphone via the network.

[1726] Step 8:

[1727] The device receives the response from the server and displays the text response on the screen or plays it aloud using a Text-to-Speech (TTS) system.

[1728] Step 9:

[1729] The user sees a message displayed on the device or hears a spoken response, such as "You might need a little break. Would you like to take a moment to breathe and relax?"

[1730] Step 10:

[1731] The server accepts new input data from the user and learns the user's long-term emotional trends. The emotion analysis engine uses the user's continuous data to evaluate emotional transitions and analyze long-term trends, which allows the next response to be more personalized.

[1732] In this way, a "Life Mentor" system can receive user input, analyze emotions, and generate appropriate responses based on those inputs, providing support tailored to individual situations, such as helping people relieve stress in their daily lives or suggesting new hobbies.

[1733] Example 2

[1734] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1735] In modern society, feelings of loneliness and stress experienced by users have become serious issues. Current personal assistant systems are unable to adequately address these emotional issues. This is due to a lack of technology to accurately grasp a user's emotional state and generate appropriate responses. Another problem is that it is difficult to provide personalized responses that incorporate a user's long-term emotional trends. As a result, users are unable to receive support that is in tune with their own emotions, and it is difficult to alleviate feelings of loneliness and stress.

[1736] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text and determining the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, means for visually displaying the user's emotional state, means for adaptively updating an emotion evaluation in accordance with changes in the user's input frequency and content, and means for personalizing the generated response by learning the user's long-term emotional tendency. This makes it possible to accurately grasp the user's emotional state and provide an appropriate and personalized response.

[1737] "User" refers to a person who uses the system to input natural language.

[1738] "Natural language input" refers to linguistic input that a user makes to a system using voice or text.

[1739] "Speech recognition" is a technology that converts voice data into text data.

[1740] "Text" refers to character string data generated based on natural language input.

[1741] "Analysis" refers to the process of understanding the content of text and determining the user's intent and sentiment.

[1742] "Natural language processing" is a technology that analyzes text data to determine the user's intent.

[1743] "Sentiment analysis" is a technique for assessing a user's emotional state from text and extracting specific emotional elements.

[1744] "Generative AI" refers to artificial intelligence that generates responses based on the user's intent and emotional state.

[1745] "Response" refers to the reply or message that the generating AI provides to the user.

[1746] "Terminal" refers to devices such as smartphones and personal computers used by users.

[1747] "Display" refers to displaying the generated response as text on the terminal screen.

[1748] "Voice playback" refers to playing back the generated response as voice using speech synthesis technology.

[1749] "Visual display" refers to a display means for visually representing a user's emotional state.

[1750] "Adaptive" refers to dynamically responding to changes in the frequency and content of user input.

[1751] "Personalization" refers to learning a user's long-term emotional tendencies and responding to them individually.

[1752] The present invention relates to a personal assistant system for reducing a user's feelings of loneliness and stress. This system accepts natural language input and generates a response that is in tune with the user's feelings by combining a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[1753] Hardware and Software Overview

[1754] server:

[1755] The servers have central processing units (CPUs) that perform speech recognition, natural language processing, sentiment analysis, and generative AI functions. The servers are deployed on high-performance cloud infrastructure, such as Google Cloud Platform or Amazon Web Services.

[1756] Device:

[1757] The device refers to the smartphone or computer used by the user, which is equipped with audio capture and network connectivity functions.

[1758] Program processing

[1759] Request reception

[1760] When a user says to their smartphone, "I've been feeling tired at work lately," the smartphone sends the voice data to the server.

[1761] Voice Recognition

[1762] The server uses Google Cloud Speech-to-Text to convert the received voice data into text data, generating the text "I've been tired at work lately" from the voice.

[1763] Natural Language Processing

[1764] The server passes the generated text data to a natural language processing engine (such as OpenAI's GPT-3) to determine the user's intent. In this case, the NLP model analyzes the intent as "feeling tired and stressed."

[1765] Emotion analysis

[1766] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotional elements in the text, extracting the emotions of "fatigue" and "stress."

[1767] Response Generation

[1768] The server uses a generative AI (such as OpenAI's ChatGPT) to generate an appropriate response based on the user's intent and emotional data. The generative AI generates a text response such as, "Maybe you need a break. Shall we take a moment to take a deep breath and relax?"

[1769] Response Send

[1770] The server generates a response and sends it to the user's device (smartphone). The response is then transferred to the smartphone via the network.

[1771] Response Display

[1772] The user's device (smartphone) receives a response, which is then displayed as text on the screen or played aloud using a speech synthesis system such as AWS Polly. The user can then see the message displayed on the screen or listen to it. In addition, an emotion engine visually displays the user's emotional state.

[1773] Specific examples

[1774] Example 1: When a user is stressed

[1775] (situation)

[1776] The user says to their smartphone, "I've been feeling tired at work lately."

[1777] (Processing) Based on this statement, the server converts the speech into text data, performs sentiment analysis, and generates and sends an appropriate response.

[1778] Example 2: User seeking advice

[1779] (situation)

[1780] A user says, "I want to start a new hobby. What should I do?"

[1781] (Processing) Based on this utterance, the server converts the speech into text data, analyzes emotions, and generates a response suggesting recommended hobbies.

[1782] Prompt Sentence Examples

[1783] Stress prompts

[1784] “If a user expresses that they are tired, what words of encouragement would you offer them?”

[1785] New Hobby Prompt

[1786] "When a user is looking for a new hobby, how can we suggest trending hobbies?"

[1787] In this way, the personal assistant system of the present invention generates appropriate responses based on the user's natural language input and emotional state, and is used to reduce the user's feelings of loneliness and stress.

[1788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1789] Step 1:

[1790] The user speaks into the input device (smartphone) saying, "I've been feeling tired at work lately." Voice input is performed, and the smartphone captures this voice data. This captured voice data becomes the input data used in the next processing step.

[1791] Step 2:

[1792] The device (smartphone) sends the captured audio data to the server. The device uses a network connection to transfer this audio data to the server in real time. The server receives this audio data and uses it in the next processing step.

[1793] Step 3:

[1794] The server uses a speech recognition system to convert the received voice data into text data. Specifically, Google Cloud Speech-to-Text is used to convert the voice data into text such as "I've been feeling tired at work lately." The input is voice data, and the output is text data.

[1795] Step 4:

[1796] The server passes the generated text data to a natural language processing (NLP) engine to determine the user's intent. Specifically, OpenAI's GPT-3 is used to analyze the intent of "feeling tired and stressed." The input is text data, and the output is data indicating the user's intent.

[1797] Step 5:

[1798] The server uses an emotion engine to analyze the emotional elements in the text data. It uses IBM Watson Tone Analyzer to extract the emotions of "fatigue" and "stress." The input is text data, and the output is data indicating the emotional state.

[1799] Step 6:

[1800] The server uses generative AI to generate an appropriate response based on the user's intent and emotional data. It uses OpenAI's ChatGPT to generate a text response such as, "Maybe you need a break. Let's take a deep breath and relax." The input is the user's intent and emotional data, and the output is a text response.

[1801] Step 7:

[1802] The server generates a text response and sends it to the user's device (smartphone). The server then transfers the text response to the smartphone via the network. The input is the text response, and the output is data sent to the device.

[1803] Step 8:

[1804] The terminal receives the text response sent from the server. The terminal receives this data via the network and uses it in the next processing step. The input is the text response data from the server.

[1805] Step 9:

[1806] The device displays or plays audible the text response it receives. Specifically, it uses AWS Polly to synthesize speech and plays a message to the user audibly or displays it as text on the screen. The input is the text response data, and the output is visual or auditory feedback to the user.

[1807] This series of processes makes it possible to generate and provide an appropriate response to the emotions and intentions expressed by the user in natural language.

[1808] (Application example 2)

[1809] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1810] Conventional personal assistant systems often only generate responses by analyzing the user's natural language input and emotional state, and have the problem of not being able to suggest products or services that correspond to the user's specific requests and emotions. For this reason, there has been a demand for systems that not only reduce the sense of loneliness and stress felt by users, but also improve the user experience by suggesting specific actions.

[1811] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1812] In this invention, the server includes means for accepting natural language input from a user, means for converting the accepted natural language input into text by speech recognition, natural language processing means for analyzing the text to determine the user's intention, emotion analysis means for analyzing the user's emotional state from the text, generation AI means for generating a response based on the user's intention and emotional state, means for transmitting the generated response to the user's terminal, means for displaying or audibly playing the response on the user's terminal, and means for suggesting products and services to the user based on the response. This makes it possible to provide a response that is in tune with the user's emotions while improving the user experience by suggesting specific products and services.

[1813] "Natural language input" refers to input made by a user in natural language, in the form of voice or text.

[1814] "Speech recognition" is a technology for converting voice data into text data, and is a technology for acquiring user utterances as text information.

[1815] "Text" refers to character information generated by speech recognition, and is a string of characters that expresses the content of a user's speech.

[1816] "Natural language processing" is a technology for analyzing natural language text to understand its meaning and intent, and is a means of accurately determining the user's intent.

[1817] "Sentiment analysis" is a technique for assessing and extracting a user's emotional state from text, and is a means of understanding a user's psychological state.

[1818] "Generative AI" is a technology that uses artificial intelligence to generate responses based on a user's intent and emotional state, and is a means of creating natural conversations.

[1819] "User terminal" refers to a device such as a smartphone, smart glasses, or head-mounted display on which responses can be received and displayed.

[1820] "Response display" refers to displaying the generated response on the user's terminal, allowing the user to visually confirm the response content.

[1821] "Audio playback" refers to the use of speech synthesis technology to transmit the generated response to the user audibly, allowing the user to confirm the response content through their hearing.

[1822] "Product and service suggestions" refers to introducing specific products and services that correspond to the user's emotions and intentions based on the generated responses, and suggests actions to satisfy the user's needs.

[1823] System Configuration

[1824] The present invention relates to a personal assistant system for reducing a user's sense of loneliness and stress, and is implemented by combining the following hardware and software:

[1825] Hardware used

[1826] Smartphone (iPhone, Android, etc.)

[1827] Smart glasses (general name)

[1828] Head-mounted display (general name)

[1829] Server (cloud-based: AWS, Google Cloud Platform, etc.)

[1830] Software used

[1831] Speech recognition systems (Google Speech-to-Text, IBM Watson, etc.)

[1832] Natural language processing engine (GPT-4, BERT, etc.)

[1833] Sentiment analysis engine (Microsoft Azure Emotion API, etc.)

[1834] Generative AI models (OpenAI GPT-4, etc.)

[1835] Program processing and data processing

[1836] 1. Request acceptance

[1837] When a user says something like "I'm tired from work, so I want to eat something delicious" via a smartphone or smart glasses, the voice data is sent to the server, where it is converted into text data using a voice recognition system.

[1838] 2. Voice Recognition

[1839] The server uses a speech recognition system (such as Google Speech-to-Text) to convert the received voice data into text data, which generates the text "I'm tired from work, so I want to eat something delicious."

[1840] 3. Natural Language Processing

[1841] The server passes this text data to a natural language processing (NLP) engine to analyze the user's intent. In this case, the NLP engine (such as GPT-4) determines the intent as "I'm feeling tired and want to eat something delicious."

[1842] 4. Emotion analysis

[1843] The server uses a sentiment analysis engine to assess and extract the user's emotional state (fatigue and anticipation) from the text.

[1844] 5. Response Generation

[1845] The server uses a generative AI model to generate appropriate responses and meal suggestions based on the user's intent and emotional state, such as, "You seem tired today. In that case, I'd recommend a warm soup or a nutritious salad. What do you think?"

[1846] 6. Sending a Response

[1847] The generated response is sent from the server to the user's terminal.

[1848] 7. Response Display

[1849] The user's device (smartphone or smart glasses) will display the received response on the screen, allowing the user to visually confirm the response and select and order a meal suggestion if necessary.

[1850] Specific examples

[1851] Example 1: Suggestions for when you're tired

[1852] (situation)

[1853] The user says to their smartphone, "I'm tired from work, so I want to eat something delicious."

[1854] (process)

[1855] 1. Request acceptance

[1856] 2. Speech recognition: "I'm tired from work and want to eat something delicious."

[1857] 3. Natural Language Processing: "I'm feeling tired and want something tasty to eat."

[1858] 4. Emotion analysis: Analyzing "fatigue" and "expectation"

[1859] 5. Response Generation: "I'd love a warm soup or a hearty salad. What do you think?"

[1860] 6. Sending a Response

[1861] 7. Response display: Responses and meal suggestions are displayed on the user's smartphone or smart glasses.

[1862] Prompt Sentence Examples

[1863] Suggestions for when you're tired

[1864] User request: "I'm tired from work, so I want to eat something delicious."

[1865] Response-generating prompt:

[1866] "What kind of food would you recommend for users when they're tired? Also, add some encouraging messages to help users relax."

[1867] Example response of the generative AI model:

[1868] "You must be tired today. In that case, I recommend a warm soup or a nutritious salad. Take a little rest and relax your body and mind."

[1869] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1870] Step 1: Request acceptance

[1871] The user speaks into their smartphone or smart glasses, "I'm tired from work, I want to eat something delicious." The voice data is sent to the server via a URL. This is the input. The server receives this voice data and prepares for the next step.

[1872] Step 2: Voice Recognition

[1873] The server uses a speech recognition system (e.g., Google Speech-to-Text) to convert the received voice data into text data. The input of this step is the voice data, and the output is the text "I'm tired from work, so I want to eat something delicious." The speech recognition system analyzes the voice waveform and generates a corresponding string of characters.

[1874] Step 3: Natural Language Processing

[1875] The server passes the generated text data to a natural language processing engine (e.g., GPT-4). The input of this step is text data, and the output is the intent, "I feel tired and want to eat something delicious." The natural language processing engine analyzes words and understands the context to determine the user's intent.

[1876] Step 4: Sentiment Analysis

[1877] The server passes the text data to a sentiment analysis engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state. The input for this step is the text data, and the output is the emotional states of "fatigue" and "expectation." The sentiment analysis engine identifies emotional keywords in the text and evaluates the emotion.

[1878] Step 5: Response Generation

[1879] The server uses a generative AI model to generate a response based on the user's intention and emotional state. The input for this step is the user's intention and emotional state, and the output is a response message such as, "You seem tired today, don't you? In that case, I recommend a warm soup or a nutritious salad. What do you think?" The generative AI model generates a sentence that continues the conversation in a natural way based on the prompt.

[1880] Step 6: Send response

[1881] The generated response message is sent from the server to the user's device (smartphone or smart glasses). The input of this step is the generated response message, and the output is the completion of message transmission. The server sends the message to the device via network communication.

[1882] Step 7: Display the response

[1883] The user's device (smartphone or smart glasses) displays the received response on its screen and, if necessary, plays it aloud using speech synthesis technology. The input for this step is the received response message, and the output is in a format that the user can confirm visually or audibly. The device's display and speaker are used to notify the user of the response content.

[1884] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1885] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1886] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1887] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1888] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1889] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1890] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1891] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1892] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1893] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1894] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1895] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1896] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1897] 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.

[1898] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1899] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1900] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1901] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1902] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1903] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1904] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1905] The following is further disclosed regarding the above embodiment.

[1906] (Claim 1)

[1907] means for accepting natural language input from a user;

[1908] means for converting the accepted natural language input into text using speech recognition;

[1909] natural language processing means for analyzing the text to determine user intent;

[1910] emotion analysis means for analyzing the user's emotional state from the text;

[1911] A generation AI means for generating a response based on the user's intention and emotional state;

[1912] means for transmitting the generated response to a user terminal;

[1913] means for displaying or audibly playing the response on the user's terminal;

[1914] Personal assistant system including.

[1915] (Claim 2)

[1916] 2. The personal assistant system according to claim 1, further comprising a means for performing speech recognition when accepting a speech input as the natural language input.

[1917] (Claim 3)

[1918] 2. The personal assistant system according to claim 1, characterized in that the generation AI means for generating the response includes means for using artificial intelligence to generate natural conversation based on the user's intentions and emotional state.

[1919] "Example 1"

[1920] (Claim 1)

[1921] means for accepting natural language input from a user;

[1922] means for converting the accepted natural language input into text using speech recognition;

[1923] natural language processing means for analyzing the text to determine user intent;

[1924] emotion analysis means for analyzing the user's emotional state from the text;

[1925] A generation AI means for generating a response based on the user's intention and emotional state;

[1926] means for transmitting the generated response to a user terminal;

[1927] means for displaying or audibly playing the response on the user's terminal;

[1928] means for transmitting the received voice data to a voice recognition service;

[1929] means for transmitting text data obtained from the speech recognition service to a natural language processing engine;

[1930] means for transmitting the intention information acquired from the natural language processing engine to an emotion analysis engine;

[1931] A means for generating prompt sentences based on emotional information obtained from the emotion analysis engine and inputting the prompt sentences into a generative AI model;

[1932] A means for transmitting response data obtained from the generative AI model to a user's device;

[1933] A system including:

[1934] (Claim 2)

[1935] 2. The system according to claim 1, further comprising means for performing speech recognition when accepting a speech input as the natural language input.

[1936] (Claim 3)

[1937] The system of claim 1, characterized in that the generation AI means for generating the response includes means for using artificial intelligence to generate natural conversation based on the user's intentions and emotional state.

[1938] "Application Example 1"

[1939] (Claim 1)

[1940] means for accepting natural language input from a user;

[1941] means for converting the accepted natural language input into text using speech recognition;

[1942] natural language processing means for analyzing the text to determine user intent;

[1943] emotion analysis means for analyzing the user's emotional state from the text;

[1944] a response generation means using a generation model to generate a response based on the user's intention and emotional state;

[1945] means for providing product suggestions and stress relief advice to the user based on the generated response;

[1946] means for transmitting the response to a user terminal;

[1947] means for displaying or audibly playing the response on the user's terminal;

[1948] A system including:

[1949] (Claim 2)

[1950] 2. The system according to claim 1, further comprising means for performing speech recognition when accepting a speech input as the natural language input.

[1951] (Claim 3)

[1952] 10. The system of claim 1, further comprising means for using artificial intelligence to generate natural-sounding conversations using prompt sentences based on the user's intent and emotional state as a generative model for generating the responses.

[1953] "Example 2: Combining Emotion Engines"

[1954] (Claim 1)

[1955] means for accepting natural language input from a user;

[1956] means for converting the accepted natural language input into text using speech recognition;

[1957] natural language processing means for analyzing the text to determine user intent;

[1958] emotion analysis means for analyzing the user's emotional state from the text;

[1959] A generation AI means for generating a response based on the user's intention and emotional state;

[1960] means for transmitting the generated response to a user terminal;

[1961] means for displaying or audibly playing the response on the user's terminal;

[1962] means for visually displaying the emotional state of the user;

[1963] A system including:

[1964] (Claim 2)

[1965] 2. The system according to claim 1, further comprising means for performing speech recognition when accepting a speech input as the natural language input.

[1966] (Claim 3)

[1967] The system of claim 1, characterized in that the generation AI means for generating the response includes means for using artificial intelligence to generate natural conversation based on the user's intentions and emotional state.

[1968] (Claim 4)

[1969] 2. The system according to claim 1, wherein the emotion evaluation is adaptively updated in response to changes in the frequency and content of input from the user.

[1970] (Claim 5)

[1971] 10. The system of claim 1, wherein the generated responses are personalized by learning the user's long-term emotional tendencies.

[1972] "Application example 2 when combining emotion engines"

[1973] (Claim 1)

[1974] means for accepting natural language input from a user;

[1975] means for converting the accepted natural language input into text using speech recognition;

[1976] natural language processing means for analyzing the text to determine user intent;

[1977] emotion analysis means for analyzing the user's emotional state from the text;

[1978] A generation AI means for generating a response based on the user's intention and emotional state;

[1979] means for transmitting the generated response to a user terminal;

[1980] means for displaying or audibly playing the response on the user's terminal;

[1981] means for suggesting products and services to the user based on the response;

[1982] A system including:

[1983] (Claim 2)

[1984] 2. The system according to claim 1, further comprising means for performing speech recognition when accepting a speech input as the natural language input.

[1985] (Claim 3)

[1986] The system of claim 1, characterized in that the generation AI means for generating the response includes means for using artificial intelligence to generate natural conversation based on the user's intentions and emotional state. [Explanation of symbols]

[1987] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for accepting natural language input from a user; means for converting the accepted natural language input into text using speech recognition; natural language processing means for analyzing the text to determine user intent; emotion analysis means for analyzing the user's emotional state from the text; A generation AI means for generating a response based on the user's intention and emotional state; means for transmitting the generated response to a user terminal; means for displaying or audibly playing the response on the user's terminal; Personal assistant system including.

2. 2. The personal assistant system according to claim 1, further comprising means for performing speech recognition when accepting a speech input as the natural language input.

3. 2. The personal assistant system according to claim 1, further comprising means for using artificial intelligence to generate natural conversation based on the user's intentions and emotional state as the AI ​​generation means for generating the response.

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