System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an input from a user; means for analyzing the user's input data to determine an emotion; means for generating an emotion-inducing response based on the determined emotion; and means for outputting the generated response as speech.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] Conventional voice-generating devices focus on conveying appropriate content to users, but have the problem of being unable to deliver emotionally charged, friendly speech. For users who feel particularly lonely or isolated, mechanical responses can feel cold and insufficient to provide psychological care. The objective of this invention is to provide a system that can deliver emotional speech, helping users feel less lonely or isolated. [Means for solving the problem]

[0005] In order to solve the above problems, the following means are provided.

[0006] means for receiving input from a user;

[0007] A means for analyzing user input data to determine emotions;

[0008] means for generating an emotion-eliciting response based on the determined emotion;

[0009] means for outputting the generated response as speech;

[0010] A system including:

[0011] Specifically, the system converts the user's speech into text data using speech recognition, and then analyzes the text data using natural language processing technology to determine their emotions. Based on the determined emotions, appropriate encouraging sentences are generated and output as voice using speech synthesis technology. This makes it possible to reduce the user's feelings of loneliness and isolation, and to provide psychological care.

[0012] "User" refers to an individual or corporation that uses the system.

[0013] "Input" refers to information that a user uses to communicate their intentions to a system using voice or text.

[0014] "Means" refers to hardware, software, or a combination thereof for achieving a specific function.

[0015] "Analysis" refers to the process of breaking down input data and understanding its meaning and structure.

[0016] "Emotion" refers to the user's psychological state, and includes psychological reactions such as joy, sadness, anger, and surprise.

[0017] "Judgment" refers to the act of identifying a specific emotion or state based on the analysis results.

[0018] "Response" refers to a reply or message that the system generates in response to user input.

[0019] "Generation" refers to the process of creating new data or information based on the results of analysis.

[0020] "Voice" refers to voice data and voice messages that the system issues to the user.

[0021] "Output" refers to the act of providing data or information generated by the system to the user.

[0022] "Natural language processing technology" refers to technology that uses machine learning algorithms and models to understand and generate human language.

[0023] "Speech synthesis technology" refers to technology that generates and outputs voice data from text data. [Brief explanation of the drawings]

[0024] [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

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] ---

[0046] A specific embodiment for carrying out the present invention will be described below. The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs it as voice.

[0047] First, the device receives input from the user. This input is often in the form of voice, which the device converts into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," this voice data is converted into text data.

[0048] This text data is then sent from the device to a server. The server receives the text data and uses natural language processing technology to analyze its content and determine the user's emotions. Specifically, it applies a sentiment analysis algorithm to extract the emotion "loneliness" from the text data.

[0049] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "That must have been lonely. I'm here, so please feel free to talk to me about anything." This response is generated using Transformer-based natural language generation technology.

[0050] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0051] Through this process, users will feel less lonely and isolated. For users who feel particularly lonely, providing emotional responses can provide psychological care.

[0052] As a specific example, consider the case where a user makes the following statement:

[0053] User: "Today was a really tough day."

[0054] In this case, the device converts the message into text and sends the text data to the server. The server determines from the text that the user is feeling "tough" and generates a response such as "That must be tough. Let me know if there's anything I can do to help you." Finally, this response is transmitted to the user as voice from the device.

[0055] This system is an effective means of cheering up and providing psychological support to users who feel lonely or isolated. By implementing this invention, users can enjoy conversations while feeling a sense of familiarity, and can help alleviate feelings of loneliness.

[0056] ---

[0057] The processing flow will be explained below.

[0058] ---

[0059] Step 1:

[0060] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[0061] Step 2:

[0062] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[0063] Step 3:

[0064] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[0065] Step 4:

[0066] The server uses natural language processing technology to analyze the received text data, extracting sentence structure, keywords, and sentiment.

[0067] Step 5:

[0068] The server applies a sentiment analysis algorithm to determine the user's sentiment, for example, "I felt lonely."

[0069] Step 6:

[0070] The server generates an appropriate response based on the determined emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[0071] Step 7:

[0072] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[0073] Step 8:

[0074] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[0075] Step 9:

[0076] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0077] ---

[0078] The above are the specific processing steps from the user's input to the utterance of a response. This system allows the user to feel closer to the person and alleviate their sense of loneliness.

[0079] Example 1

[0080] 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."

[0081] In recent years, the number of individuals experiencing feelings of loneliness and isolation has been increasing, but conventional systems have had difficulty accurately assessing the user's emotions and generating empathetic responses based on those emotions. Furthermore, while there is a need for a smooth process from voice input to voice response, previous systems have not been able to adequately address this issue. As a result, users' psychological care has not been effective, and this issue needs to be resolved.

[0082] 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.

[0083] In this invention, the server includes means for converting voice input into text data, means for analyzing the text data to determine emotions, and means for generating an appropriate response based on the determined emotions, thereby enabling the server to empathize with the user's emotions, generate an appropriate response, and output it as voice.

[0084] "User" refers to an individual, company, or other entity that operates or inputs data into the system.

[0085] "Voice input" refers to the act of a user providing information to a system using voice.

[0086] "Means" refers to a hardware or software method or technique for realizing a specific function within a system.

[0087] "Text data" refers to data that has been converted from voice input into text information.

[0088] "Voice recognition technology" refers to the technology that analyzes voice input and converts it into text data.

[0089] "Natural language processing technology" refers to all technologies that enable systems to understand and process human language.

[0090] "Judging emotions" refers to analyzing and recognizing user emotions from the content of text data using natural language processing technology.

[0091] "Generative AI models" refer to techniques and algorithms that use artificial intelligence to generate responses based on user input data and emotions.

[0092] "Response" refers to the verbal or audio output that a system generates and provides in response to user input.

[0093] "Speech synthesis technology" refers to the technology that converts text data into audible speech.

[0094] The present invention relates to a system that analyzes voice input from a user, determines the emotion, and generates an appropriate response based on that emotion and outputs it as voice. This system uses a combination of speech recognition technology, natural language processing technology, generative AI models, and speech synthesis technology.

[0095] First, the device receives voice input from the user. This device is a device that allows voice input, such as a smartphone, smart speaker, or PC. Once voice input is made, the device uses voice recognition technology to convert this voice into text data. Specifically, voice recognition services such as Google Speech-to-Text API and IBM Watson can be used. For example, if a user says, "I haven't had anyone to talk to all day, and I've been really lonely," this voice data is converted into text data that reads, "I haven't had anyone to talk to all day, and I've been really lonely."

[0096] Next, the device sends the converted text data to the server. This transmission process is carried out using an HTTP request, and the data is sent in JSON format. Once the server receives the text data, it uses natural language processing technology to analyze its content and determine the emotion. Specific implementations include the Google Natural Language API and Microsoft Azure text analysis services. In this step, emotions such as "loneliness" are extracted from the text data.

[0097] Based on the analysis results, the server uses a generative AI model (for example, OpenAI's GPT-3 or GPT-4) to generate an appropriate response. In this case, the response generated is something like, "I'm sure that was lonely. I'm here, so please feel free to talk to me." The generated response is then sent from the server to the device. This is also done via an HTTP request, with the response data being sent in JSON format.

[0098] The device converts the received response into speech using speech synthesis technology. Speech synthesis technologies available include Amazon Polly and Google Text-to-Speech. The converted speech is spoken to the user in a gentle tone. For example, the device might say, "That must have been lonely. I'm here, so please feel free to talk to me."

[0099] This system helps users feel less lonely and isolated, and provides psychological care. As a concrete example, we will explain the behavior when a user says, "Today was a very difficult day."

[0100] For example, a user might say, "Today was a really tough day." This speech is converted into text data and sent to a server. The server analyzes the text data and recognizes the emotion "tough." The server then uses a generative AI model to generate a response such as, "That must have been tough. Let me know if there's anything I can do to help you." This response is sent to the device, converted into speech, and spoken to the user.

[0101] The following are examples of prompt sentences:

[0102] "Please advise how to respond when a user feels lonely."

[0103] "If a user says, 'I had a really hard day today,' what would be an appropriate response?"

[0104] This invention allows users to enjoy conversation while feeling a sense of familiarity, and can alleviate feelings of loneliness.

[0105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0106] Step 1:

[0107] The user inputs voice into the terminal. For example, the user says, "I haven't been able to talk to anyone all day and I've been really lonely." This input is passed to the terminal as voice data.

[0108] Step 2:

[0109] The device receives voice input and converts this voice into text data using voice recognition technology. Specifically, it uses voice recognition technology (e.g., voice recognition API) to analyze the input voice and generate text data such as "I haven't been able to talk to anyone all day today and I've been really lonely." The input here is voice data and the output is text data.

[0110] Step 3:

[0111] The terminal sends the converted text data to the server. This transmission is done using an HTTP request, and the text data is sent to the server in JSON format. Here, the input is the text data, and the output is the request to the server.

[0112] Step 4:

[0113] The server analyzes the received text data and determines the emotion. Specifically, it uses natural language processing technology to analyze the text and extract the emotion (e.g., text analysis service). In this case, the emotion "loneliness" is extracted. The input here is text data, and the output is emotion data.

[0114] Step 5:

[0115] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., a generative AI model), it generates a response based on the emotion data: "That must have been lonely. I'm here, so please feel free to talk to me." The input here is emotion data, and the output is response data.

[0116] Step 6:

[0117] The server generates a response and sends it to the terminal. This is also done using an HTTP request, and the response data is sent to the terminal in JSON format. The input here is the response data, and the output is the request to the terminal.

[0118] Step 7:

[0119] The response received by the device is converted into voice data using voice synthesis technology. Specifically, the text data is analyzed, and voice synthesis technology (e.g., a voice synthesis service) is used to generate voice data that says, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is the response data, and the output is voice data.

[0120] Step 8:

[0121] The device speaks the generated voice data to the user. The device speaks in a gentle tone, saying, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is voice data, and the output is voice output to the user.

[0122] This series of processing steps helps users reduce feelings of loneliness and isolation and allows them to receive psychological care.The system provides a friendly conversational experience by understanding emotions from user input and generating and uttering responses based on those emotions.

[0123] (Application example 1)

[0124] 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."

[0125] In modern society, users often seek psychological support to alleviate everyday stress and anxiety. It is particularly important to provide a sense of security through appropriate dialogue tailored to individual situations and emotions. However, conventional systems have had difficulty accurately analyzing users' emotions and providing appropriate responses. Therefore, there is a need for an effective system that uses the user's voice input to analyze emotions and generate responses that induce a sense of security.

[0126] 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.

[0127] In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine emotions, means for generating an emotion-inducing response based on the determined emotions, means for outputting the generated response as voice, means including voice recognition technology for converting the voice input into text data, server means including an emotion analysis algorithm for analyzing emotions from the text data, and means including voice synthesis technology for converting the generated response into voice data. This makes it possible to accurately analyze the user's emotions and provide an appropriate response in voice format according to each individual situation.

[0128] The "means for receiving input from the user" is an interface that allows the user to input data to the system in the form of voice, text, or the like.

[0129] "Means for analyzing user input data to determine emotion" refers to the algorithms or software used to process the input data and identify the user's emotional state.

[0130] "Means for generating a response that elicits an emotion" refers to a technique or method for creating an appropriate response that is in tune with the user's emotions based on the determined user's emotions.

[0131] The "means for outputting the generated response as audio" is a technology for converting the generated text data response into audio that can be heard by humans and playing it back through an output device such as a speaker or earphones.

[0132] "Speech recognition technology for converting speech input into text data" refers to technology that captures a user's speech and converts that speech into digital text format.

[0133] The "server means including a sentiment analysis algorithm for analyzing sentiment from text data" refers to a server and an algorithm running on the server for analyzing the text input by the user and determining the emotional state.

[0134] The "speech synthesis technology for converting the generated response into voice data" is a technology for converting a text-format response into voice format and outputting it as natural speech.

[0135] The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs the response as voice. Specific embodiments will be described in detail below.

[0136] First, the user inputs voice using a smartphone. This voice is collected through a microphone and converted into text data using the device's voice recognition technology (e.g., speech_recognition library). This text data is then sent to a server via the Internet.

[0137] The server receives the text data and analyzes the content using natural language processing techniques (e.g., a sentiment analysis algorithm using the Transformers model) to determine the user's sentiment. A specific sentiment analysis algorithm (e.g., the BERT model using the Transformers library) is used for this analysis.

[0138] For example, if a user says, "I haven't been able to sleep lately and I'm worried," the server performs sentiment analysis on the text data and extracts the emotion "anxiety." Based on the emotion determined, a response that induces a sense of relief is generated. This response is generated using natural language generation technology using a transformer model (e.g., the transformers library). An example of a response might be, "That's terrible. Is there anything I can help you with?"

[0139] The generated response is sent back to the terminal from the server, and the terminal converts the response into voice data using speech synthesis technology (e.g., the pyttsx3 library) and speaks it to the user, allowing the user to receive appropriate psychological care in the form of voice.

[0140] This system allows users to receive appropriate support tailored to their individual circumstances to cope with everyday anxieties and stress, and it also improves their sense of security, especially in terms of security.

[0141] As a specific example, suppose the user says, "I haven't been able to sleep lately and I'm worried." In this case, the following process is performed.

[0142] 1. The user's voice is collected by the smartphone and converted into text.

[0143] 2. The converted text is sent to the server for sentiment analysis.

[0144] 3. The emotion is determined to be "anxiety" and an appropriate response is generated (e.g., "That's terrible. Is there anything I can help you with?").

[0145] 4. The response is sent back to the smartphone and returned to the user as audio.

[0146] Example prompt sentence:

[0147] When a user says, "I've been having trouble sleeping lately and I'm worried," the app analyzes the emotion and responds, "That's tough. Is there anything I can help you with?"

[0148] The above is a specific embodiment for carrying out the invention.

[0149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0150] Step 1:

[0151] The user uses a smartphone to input voice data. Specifically, the user speaks into the microphone. This voice data becomes the input.

[0152] Step 2:

[0153] The device collects voice input and converts the voice data into text data using speech recognition technology (e.g., the speech_recognition library). Specifically, the speech recognition engine analyzes the voice waveform and converts it into corresponding text. This process converts the voice data into text data.

[0154] Step 3:

[0155] The terminal sends the converted text data to the server. Specifically, the data is sent to the server via internet communication. This input allows the server to receive the text data.

[0156] Step 4:

[0157] To analyze the received text data, the server applies a sentiment analysis algorithm using natural language processing techniques (e.g., Transformer model). Specifically, the server analyzes the text data and determines the emotional state (e.g., "anxiety"). Based on this input, the server identifies the user's emotion.

[0158] Step 5:

[0159] Based on the determined emotion, the server generates an appropriate response using a generative AI model (e.g., a Transformer model). Specifically, depending on the emotional state, it generates an appropriate response text (e.g., "That's terrible. Is there anything I can help you with?"). This input generates the response text.

[0160] Step 6:

[0161] The server sends the generated response text to the terminal. Specifically, data is sent to the terminal via Internet communication. This input allows the terminal to receive the response text.

[0162] Step 7:

[0163] The device uses speech synthesis technology (e.g., the pyttsx3 library) to convert the received response text into voice data. Specifically, the text data is analyzed by a speech synthesis engine and generated in a natural voice format. This process converts the response text into voice data.

[0164] Step 8:

[0165] The terminal outputs voice data and speaks to the user. Specifically, the voice is played back through a speaker. This process allows the user to receive an appropriate voice response generated by the terminal.

[0166] The above are the specific processing steps of the system that realizes the application example.

[0167] 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.

[0168] ---

[0169] A specific embodiment for carrying out the present invention will be described. This invention relates to a system that uses an emotion engine to recognize and determine a user's emotion, generates an appropriate response based on that emotion, and outputs it as voice.

[0170] First, the device receives voice input from the user. This input is in the form of voice, and the device converts it into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," the voice data is converted into text data.

[0171] This text data is then sent from the device to a server. The server receives the text data and uses an emotion engine to analyze its contents and recognize and determine the user's emotion. Specifically, the emotion engine uses machine learning technology to extract the emotion of "loneliness" from the text data.

[0172] If this emotion engine uses the user's past input data, it can achieve more accurate emotion recognition. It learns the user's tendencies and patterns from past data and accurately determines their current emotions based on that.

[0173] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "I'm sure that was lonely. I'm here, so please feel free to talk to me." This response is generated using Transformer-based natural language generation technology.

[0174] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0175] As a specific example, consider the case where a user makes the following statement:

[0176] User: "Today was a really tough day."

[0177] In this case, the device converts the voice into text and sends the text data to the server. The server then analyzes the text using its emotion engine and recognizes and determines that the user is expressing the emotion "tough." Based on the results of this analysis, the server then generates a response such as "That must be tough. Let me know if there's anything I can do to help you." This response is then transmitted to the user as voice from the device.

[0178] Through this series of processes, users can feel closer to the person and alleviate their feelings of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses. The system of the present invention is an effective means of cheering up and providing psychological support to users who feel lonely or isolated.

[0179] ---

[0180] The processing flow will be explained below.

[0181] ---

[0182] Step 1:

[0183] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[0184] Step 2:

[0185] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[0186] Step 3:

[0187] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[0188] Step 4:

[0189] The server analyzes the received text data using natural language processing techniques, first by tokenizing and parsing the text to analyze its context and structure.

[0190] Step 5:

[0191] The server uses an emotion engine to recognize and determine the user's emotions from the analyzed text data. For example, the emotion engine extracts the emotion "lonely." Furthermore, the accuracy of emotion recognition is improved by referencing the user's past input data.

[0192] Step 6:

[0193] The server generates an appropriate response based on the recognized emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[0194] Step 7:

[0195] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[0196] Step 8:

[0197] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[0198] Step 9:

[0199] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0200] ---

[0201] These are the specific processing steps from user input to the utterance of a response. This system allows users to feel closer to the person and alleviate their sense of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses.

[0202] Example 2

[0203] 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."

[0204] In modern society, the number of people feeling lonely and isolated is increasing, and measures to address this are needed. However, conventional systems have difficulty accurately recognizing users' emotions and providing appropriate responses. To solve this problem, a system is needed that can accurately recognize users' emotions and generate and output appropriate responses based on those emotions.

[0205] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice input from the user, means for converting the voice input into text data, means for analyzing the text data to determine the user's emotion, means for generating an appropriate response based on the determined emotion, and means for outputting the generated response as voice. This makes it possible to accurately recognize the user's emotion and provide a response based on that emotion, thereby reducing feelings of loneliness and sadness.

[0206] ---

[0207] "User" refers to any individual or entity that uses the System.

[0208] "Voice input" refers to voice data uttered by a user.

[0209] "Means" refers to a device, program, or method for performing a specified function.

[0210] "Text data" refers to text information converted from voice input.

[0211] "Analysis" refers to the process of analyzing data and extracting the information it contains.

[0212] "Emotion" represents the user's psychological state, and refers to states such as "lonely" or "difficult."

[0213] "Judgment" refers to the process of reaching a particular conclusion or evaluation based on the results of an analysis.

[0214] An "appropriate response" refers to a response content generated according to the user's emotions.

[0215] "Generation" refers to the process of creating new data or content.

[0216] "Speech" refers to auditory information that is output to the user.

[0217] "Output" refers to the process of providing generated information to the outside world.

[0218] MODE FOR CARRYING OUT THE INVENTION

[0219] The present invention is a system that analyzes a user's voice input, determines the emotion, generates an appropriate response, and outputs it as voice. Specific embodiments of this system are described in detail below.

[0220] First, the device receives voice input from the user. This voice input is recorded using a standard microphone. For example, if the user says, "I haven't been able to talk to anyone all day and I've been really lonely," the voice data is captured by the device.

[0221] The device converts the recorded voice data into text data using voice recognition technology (for example, general-purpose voice recognition software). Specifically, it converts the voice data into text data by using a voice recognition API such as Google Speech-to-Text. The converted text data will contain the following content: "I haven't been able to talk to anyone all day and I've been really lonely."

[0222] The device then sends this text data to a server over the Internet using the HTTPS protocol to ensure data security.

[0223] The server receives the text data and analyzes it using an emotion engine (e.g., emotion detection software using natural language processing technology). Specifically, it uses an API such as IBM Watson Natural Language Understanding to extract the user's emotion from the text data. In this case, the emotion engine recognizes the emotion of "loneliness."

[0224] Based on the analysis results, the server generates an appropriate response based on the user's emotions. Using a generative AI model (for example, Transformer-based natural language generation technology), the server generates a response such as, "That must have been lonely. I'm here, so please feel free to talk to me." Specifically, a generative AI model such as GPT-4 is used.

[0225] The generated response is then sent back to the device from the server, where it is converted into voice data using speech synthesis technology (for example, general-purpose speech synthesis software). This uses a speech synthesis API such as Amazon Polly. The converted voice data is then spoken to the user via the speaker.

[0226] As a concrete example, consider the following situation where a user says:

[0227] User: "Today was a really tough day."

[0228] In this case, the system performs the following process:

[0229] 1. The device receives the user's voice and converts the voice data into text data using Google Speech-to-Text.

[0230] 2. Send text data from the device to the server.

[0231] 3. The server uses an emotion engine to analyze the text data and recognize the emotion "difficult."

[0232] 4. The server uses the generative AI model to generate a text response saying, "That's tough work. Let me know if there's anything I can do to help."

[0233] 5. The server sends the generated response to the device.

[0234] 6. The device uses Amazon Polly to convert the response text into speech data and speaks it to the user.

[0235] This system allows users to receive friendly responses without feeling isolated or lonely. The combination of a generative AI model and an emotion engine makes it possible to accurately recognize the user's emotions and provide appropriate responses.

[0236] Prompt Sentence Examples

[0237] "Describe a system that accepts user input of emotionally charged speech and analyzes that speech to generate an appropriate response."

[0238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0239] Processing Steps

[0240] Step 1:

[0241] The terminal receives the user's voice input.

[0242] Input: Voice data from the user Example: "I haven't had anyone to talk to all day today and I've been really lonely."

[0243] Output: Recorded audio data

[0244] Data processing: Audio is collected through a microphone.

[0245] Specific operation: The device's microphone records audio and stores it as data.

[0246] Step 2:

[0247] The device converts the recorded voice data into text data using voice recognition technology.

[0248] Input: Recorded audio data

[0249] Output: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0250] Data processing: Using speech recognition technology (e.g., Google Speech-to-Text), words are extracted while analyzing the audio data.

[0251] Specific operation: The device calls the voice recognition API and converts the voice data into text data.

[0252] Step 3:

[0253] The terminal transmits the converted text data to the server.

[0254] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0255] Output: Text data sent to the server

[0256] Data processing: Text data is sent to the server using the HTTP protocol.

[0257] Specific behavior: The device creates an HTTPS request, includes text data as a payload, and sends it to the specified server endpoint.

[0258] Step 4:

[0259] The server analyzes the text data and determines the user's emotions.

[0260] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0261] Output: User's emotion e.g. "loneliness"

[0262] Data Computing: Extract sentiment using natural language processing techniques (e.g., IBM Watson Natural Language Understanding).

[0263] Specific operation: The server calls the emotion engine API, analyzes the text data, and obtains emotion labels.

[0264] Step 5:

[0265] The server generates an appropriate response based on the user's emotions.

[0266] Input: User's emotion e.g. "loneliness"

[0267] Output: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0268] Data Computing: Use a generative AI model (e.g., GPT-4) to generate appropriate response text.

[0269] Specific operation: The server sends emotion labels and prompts to the generative AI model, which then generates an appropriate response.

[0270] Step 6:

[0271] The server sends the generated response text to the terminal.

[0272] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0273] Output: Response text sent to the terminal

[0274] Data processing: The response text is sent to the terminal using the HTTP protocol.

[0275] Specific behavior: The server includes the generated text response as the payload of an HTTPS request and sends it to the specified device endpoint.

[0276] Step 7:

[0277] The terminal converts the response text into voice data using speech synthesis technology and speaks it to the user.

[0278] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0279] Output: Audio data

[0280] Data processing: Convert text data into audio data using speech synthesis technology (e.g., Amazon Polly).

[0281] Specific operation: The device calls the speech synthesis API, converts the text data into speech data, and plays it back to the user through the speaker.

[0282] The above is a detailed flow of each processing step in this system, which allows users to receive friendly responses and reduce feelings of loneliness.

[0283] (Application example 2)

[0284] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0285] In modern brick-and-mortar stores, customer interaction is an important element. However, some customers find it difficult to express their emotions, and store clerks are often too busy to respond to each individual customer. This can result in customers being dissatisfied or not receiving appropriate service. The present invention solves these problems and realizes a system that provides emotional responses to customers.

[0286] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine the emotion, means for generating a response that induces the emotion based on the determined emotion, means for outputting the generated response as voice, and means for automatically generating an appropriate response according to the user's emotion and providing customer service in a physical store. This makes it possible to respond based on the individual emotions of customers.

[0287] A "user" is a person using the system.

[0288] "Input" refers to audio and text information provided by a user.

[0289] "Means" refer to the methods or techniques used to achieve a certain goal.

[0290] "Data" refers to text and audio information collected based on user input.

[0291] "Analysis" is the process of examining data in detail and extracting meaning and sentiment.

[0292] "Emotion" refers to the psychological state that a user is feeling.

[0293] "Judgment" is the process of drawing conclusions from analyzed data.

[0294] A "response" is a response generated by the system in response to a user's input.

[0295] "Generation" refers to the process by which a system creates new data or information.

[0296] "Voice" refers to vocal information produced by a user or a system.

[0297] "Output" refers to the process of providing generated data or responses to the user.

[0298] "Automatic generation" refers to the process by which a system generates a response without human intervention.

[0299] "Brick and mortar store" refers to a place that offers goods or services in a physical location.

[0300] "Customer service" refers to the process of providing service and guidance to customers who visit a physical store.

[0301] The present invention relates to a system for analyzing user emotions, generating appropriate responses, and providing customer service in a physical store. A specific embodiment for realizing this system is described below.

[0302] First, the system receives input from the user (customer). The input can be provided by voice or text. This process uses a microphone for voice input and speech recognition technology. The speech_recognition library is a suitable software for this.

[0303] Next, the user's input data is sent from the device to the server. The server receives this input data and analyzes it. Natural language processing technology is used for the analysis, and the user's emotions are analyzed and determined from the input data. A natural language processing library is used for this process.

[0304] The server then automatically generates a response based on the user's emotions based on the analysis results. The generative AI model uses the Transformers library and the GPT-2 model. This model is used to generate an appropriate response. For example, the following prompt sentence is input:

[0305] "Your customer is feeling tired. An appropriate response would be: Hello. Where are you from today? If you're tired, please rest on the couch here."

[0306] The generated response is sent from the server to the device, where it is converted into speech using speech synthesis technology using the pyttsx3 library, and the device then transmits the converted speech to the user through its speaker.

[0307] This process allows users to receive appropriate responses based on their emotions. For example, if a user says, "Today was a very difficult day," the system analyzes this input and determines the emotion "difficult." It then generates a response to the user, saying, "That must have been difficult. Let me know if there's anything I can do to help you," and delivers it to the user via voice.

[0308] The system of the present invention can dramatically improve customer service in brick-and-mortar stores by accurately analyzing the user's emotions and providing appropriate responses based on those emotions.

[0309] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0310] Step 1:

[0311] The user inputs voice into the device. The device recognizes the user's voice and converts the voice data into text data. At this time, the device uses the speech_recognition library. Voice input (voice data) is input and text data is output.

[0312] Step 2:

[0313] The terminal sends text data to the server. The server receives this text data and prepares it for analysis. The input is the text data sent from the terminal, and the output is the completed analysis preparation.

[0314] Step 3:

[0315] The server analyzes emotions based on text data. Here, natural language processing technology is used to extract meaning and emotions from the text. A natural language processing library is used for this process. The input is text data, and emotional data is obtained as the output.

[0316] Step 4:

[0317] The server generates an appropriate response based on the analyzed emotional data. This process uses a generative AI model using GPT-2. The generated prompt sentence includes the emotional data and is input to the generative model. The input is the emotional data and the prompt sentence, and the output is the generated response text.

[0318] Step 5:

[0319] The server sends the generated response text to the terminal, which converts the response text to speech. This process uses the pyttsx3 library. The input is the generated response text, and the output is speech data.

[0320] Step 6:

[0321] The terminal transmits the voice data to the user through a speaker, so that the user can hear the generated response. The input is voice data, and the output is the actual voice transmitted to the user.

[0322] This series of processing steps enables the system to accurately analyze the user's emotions and automatically generate an appropriate response in voice.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] [Second embodiment]

[0327] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0328] 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.

[0329] 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).

[0330] 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.

[0331] 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.

[0332] 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).

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] 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."

[0339] ---

[0340] A specific embodiment for carrying out the present invention will be described below. The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs it as voice.

[0341] First, the device receives input from the user. This input is often in the form of voice, which the device converts into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," this voice data is converted into text data.

[0342] This text data is then sent from the device to a server. The server receives the text data and uses natural language processing technology to analyze its content and determine the user's emotions. Specifically, it applies a sentiment analysis algorithm to extract the emotion "loneliness" from the text data.

[0343] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "That must have been lonely. I'm here, so please feel free to talk to me about anything." This response is generated using Transformer-based natural language generation technology.

[0344] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0345] Through this process, users will feel less lonely and isolated. For users who feel particularly lonely, providing emotional responses can provide psychological care.

[0346] As a specific example, consider the case where a user makes the following statement:

[0347] User: "Today was a really tough day."

[0348] In this case, the device converts the message into text and sends the text data to the server. The server determines from the text that the user is feeling "tough" and generates a response such as "That must be tough. Let me know if there's anything I can do to help you." Finally, this response is transmitted to the user as voice from the device.

[0349] This system is an effective means of cheering up and providing psychological support to users who feel lonely or isolated. By implementing this invention, users can enjoy conversations while feeling a sense of familiarity, and can help alleviate feelings of loneliness.

[0350] ---

[0351] The processing flow will be explained below.

[0352] ---

[0353] Step 1:

[0354] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[0355] Step 2:

[0356] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[0357] Step 3:

[0358] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[0359] Step 4:

[0360] The server uses natural language processing technology to analyze the received text data, extracting sentence structure, keywords, and sentiment.

[0361] Step 5:

[0362] The server applies a sentiment analysis algorithm to determine the user's sentiment, for example, "I felt lonely."

[0363] Step 6:

[0364] The server generates an appropriate response based on the determined emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[0365] Step 7:

[0366] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[0367] Step 8:

[0368] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[0369] Step 9:

[0370] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0371] ---

[0372] The above are the specific processing steps from the user's input to the utterance of a response. This system allows the user to feel closer to the person and alleviate their sense of loneliness.

[0373] Example 1

[0374] 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."

[0375] In recent years, the number of individuals experiencing feelings of loneliness and isolation has been increasing, but conventional systems have had difficulty accurately assessing the user's emotions and generating empathetic responses based on those emotions. Furthermore, while there is a need for a smooth process from voice input to voice response, previous systems have not been able to adequately address this issue. As a result, users' psychological care has not been effective, and this issue needs to be resolved.

[0376] 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.

[0377] In this invention, the server includes means for converting voice input into text data, means for analyzing the text data to determine emotions, and means for generating an appropriate response based on the determined emotions, thereby enabling the server to empathize with the user's emotions, generate an appropriate response, and output it as voice.

[0378] "User" refers to an individual, company, or other entity that operates or inputs data into the system.

[0379] "Voice input" refers to the act of a user providing information to a system using voice.

[0380] "Means" refers to a hardware or software method or technique for realizing a specific function within a system.

[0381] "Text data" refers to data that has been converted from voice input into text information.

[0382] "Voice recognition technology" refers to the technology that analyzes voice input and converts it into text data.

[0383] "Natural language processing technology" refers to all technologies that enable systems to understand and process human language.

[0384] "Judging emotions" refers to analyzing and recognizing user emotions from the content of text data using natural language processing technology.

[0385] "Generative AI models" refer to techniques and algorithms that use artificial intelligence to generate responses based on user input data and emotions.

[0386] "Response" refers to the verbal or audio output that a system generates and provides in response to user input.

[0387] "Speech synthesis technology" refers to the technology that converts text data into audible speech.

[0388] The present invention relates to a system that analyzes voice input from a user, determines the emotion, and generates an appropriate response based on that emotion and outputs it as voice. This system uses a combination of speech recognition technology, natural language processing technology, generative AI models, and speech synthesis technology.

[0389] First, the device receives voice input from the user. This device is a device that allows voice input, such as a smartphone, smart speaker, or PC. Once voice input is made, the device uses voice recognition technology to convert this voice into text data. Specifically, voice recognition services such as Google Speech-to-Text API and IBM Watson can be used. For example, if a user says, "I haven't had anyone to talk to all day, and I've been really lonely," this voice data is converted into text data that reads, "I haven't had anyone to talk to all day, and I've been really lonely."

[0390] Next, the device sends the converted text data to the server. This transmission process is carried out using an HTTP request, and the data is sent in JSON format. Once the server receives the text data, it uses natural language processing technology to analyze its content and determine the emotion. Specific implementations include the Google Natural Language API and Microsoft Azure text analysis services. In this step, emotions such as "loneliness" are extracted from the text data.

[0391] Based on the analysis results, the server uses a generative AI model (for example, OpenAI's GPT-3 or GPT-4) to generate an appropriate response. In this case, the response generated is something like, "I'm sure that was lonely. I'm here, so please feel free to talk to me." The generated response is then sent from the server to the device. This is also done via an HTTP request, with the response data being sent in JSON format.

[0392] The device converts the received response into speech using speech synthesis technology. Speech synthesis technologies available include Amazon Polly and Google Text-to-Speech. The converted speech is spoken to the user in a gentle tone. For example, the device might say, "That must have been lonely. I'm here, so please feel free to talk to me."

[0393] This system helps users feel less lonely and isolated, and provides psychological care. As a concrete example, we will explain the behavior when a user says, "Today was a very difficult day."

[0394] For example, a user might say, "Today was a really tough day." This speech is converted into text data and sent to a server. The server analyzes the text data and recognizes the emotion "tough." The server then uses a generative AI model to generate a response such as, "That must have been tough. Let me know if there's anything I can do to help you." This response is sent to the device, converted into speech, and spoken to the user.

[0395] The following are examples of prompt sentences:

[0396] "Please advise how to respond when a user feels lonely."

[0397] "If a user says, 'I had a really hard day today,' what would be an appropriate response?"

[0398] This invention allows users to enjoy conversation while feeling a sense of familiarity, and can alleviate feelings of loneliness.

[0399] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0400] Step 1:

[0401] The user inputs voice into the terminal. For example, the user says, "I haven't been able to talk to anyone all day and I've been really lonely." This input is passed to the terminal as voice data.

[0402] Step 2:

[0403] The device receives voice input and converts this voice into text data using voice recognition technology. Specifically, it uses voice recognition technology (e.g., voice recognition API) to analyze the input voice and generate text data such as "I haven't been able to talk to anyone all day today and I've been really lonely." The input here is voice data and the output is text data.

[0404] Step 3:

[0405] The terminal sends the converted text data to the server. This transmission is done using an HTTP request, and the text data is sent to the server in JSON format. Here, the input is the text data, and the output is the request to the server.

[0406] Step 4:

[0407] The server analyzes the received text data and determines the emotion. Specifically, it uses natural language processing technology to analyze the text and extract the emotion (e.g., text analysis service). In this case, the emotion "loneliness" is extracted. The input here is text data, and the output is emotion data.

[0408] Step 5:

[0409] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., a generative AI model), it generates a response based on the emotion data: "That must have been lonely. I'm here, so please feel free to talk to me." The input here is emotion data, and the output is response data.

[0410] Step 6:

[0411] The server generates a response and sends it to the terminal. This is also done using an HTTP request, and the response data is sent to the terminal in JSON format. The input here is the response data, and the output is the request to the terminal.

[0412] Step 7:

[0413] The response received by the device is converted into voice data using voice synthesis technology. Specifically, the text data is analyzed, and voice synthesis technology (e.g., a voice synthesis service) is used to generate voice data that says, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is the response data, and the output is voice data.

[0414] Step 8:

[0415] The device speaks the generated voice data to the user. The device speaks in a gentle tone, saying, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is voice data, and the output is voice output to the user.

[0416] This series of processing steps helps users reduce feelings of loneliness and isolation and allows them to receive psychological care.The system provides a friendly conversational experience by understanding emotions from user input and generating and uttering responses based on those emotions.

[0417] (Application example 1)

[0418] 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."

[0419] In modern society, users often seek psychological support to alleviate everyday stress and anxiety. It is particularly important to provide a sense of security through appropriate dialogue tailored to individual situations and emotions. However, conventional systems have had difficulty accurately analyzing users' emotions and providing appropriate responses. Therefore, there is a need for an effective system that uses the user's voice input to analyze emotions and generate responses that induce a sense of security.

[0420] 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.

[0421] In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine emotions, means for generating an emotion-inducing response based on the determined emotions, means for outputting the generated response as voice, means including voice recognition technology for converting the voice input into text data, server means including an emotion analysis algorithm for analyzing emotions from the text data, and means including voice synthesis technology for converting the generated response into voice data. This makes it possible to accurately analyze the user's emotions and provide an appropriate response in voice format according to each individual situation.

[0422] The "means for receiving input from the user" is an interface that allows the user to input data to the system in the form of voice, text, or the like.

[0423] "Means for analyzing user input data to determine emotion" refers to the algorithms or software used to process the input data and identify the user's emotional state.

[0424] "Means for generating a response that elicits an emotion" refers to a technique or method for creating an appropriate response that is in tune with the user's emotions based on the determined user's emotions.

[0425] The "means for outputting the generated response as audio" is a technology for converting the generated text data response into audio that can be heard by humans and playing it back through an output device such as a speaker or earphones.

[0426] "Speech recognition technology for converting speech input into text data" refers to technology that captures a user's speech and converts that speech into digital text format.

[0427] The "server means including a sentiment analysis algorithm for analyzing sentiment from text data" refers to a server and an algorithm running on the server for analyzing the text input by the user and determining the emotional state.

[0428] The "speech synthesis technology for converting the generated response into voice data" is a technology for converting a text-format response into voice format and outputting it as natural speech.

[0429] The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs the response as voice. Specific embodiments will be described in detail below.

[0430] First, the user inputs voice using a smartphone. This voice is collected through a microphone and converted into text data using the device's voice recognition technology (e.g., speech_recognition library). This text data is then sent to a server via the Internet.

[0431] The server receives the text data and analyzes the content using natural language processing techniques (e.g., a sentiment analysis algorithm using the Transformers model) to determine the user's sentiment. A specific sentiment analysis algorithm (e.g., the BERT model using the Transformers library) is used for this analysis.

[0432] For example, if a user says, "I haven't been able to sleep lately and I'm worried," the server performs sentiment analysis on the text data and extracts the emotion "anxiety." Based on the emotion determined, a response that induces a sense of relief is generated. This response is generated using natural language generation technology using a transformer model (e.g., the transformers library). An example of a response might be, "That's terrible. Is there anything I can help you with?"

[0433] The generated response is sent back to the terminal from the server, and the terminal converts the response into voice data using speech synthesis technology (e.g., the pyttsx3 library) and speaks it to the user, allowing the user to receive appropriate psychological care in the form of voice.

[0434] This system allows users to receive appropriate support tailored to their individual circumstances to cope with everyday anxieties and stress, and it also improves their sense of security, especially in terms of security.

[0435] As a specific example, suppose the user says, "I haven't been able to sleep lately and I'm worried." In this case, the following process is performed.

[0436] 1. The user's voice is collected by the smartphone and converted into text.

[0437] 2. The converted text is sent to the server for sentiment analysis.

[0438] 3. The emotion is determined to be "anxiety" and an appropriate response is generated (e.g., "That's terrible. Is there anything I can help you with?").

[0439] 4. The response is sent back to the smartphone and returned to the user as audio.

[0440] Example prompt sentence:

[0441] When a user says, "I've been having trouble sleeping lately and I'm worried," the app analyzes the emotion and responds, "That's tough. Is there anything I can help you with?"

[0442] The above is a specific embodiment for carrying out the invention.

[0443] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0444] Step 1:

[0445] The user uses a smartphone to input voice data. Specifically, the user speaks into the microphone. This voice data becomes the input.

[0446] Step 2:

[0447] The device collects voice input and converts the voice data into text data using speech recognition technology (e.g., the speech_recognition library). Specifically, the speech recognition engine analyzes the voice waveform and converts it into corresponding text. This process converts the voice data into text data.

[0448] Step 3:

[0449] The terminal sends the converted text data to the server. Specifically, the data is sent to the server via internet communication. This input allows the server to receive the text data.

[0450] Step 4:

[0451] To analyze the received text data, the server applies a sentiment analysis algorithm using natural language processing techniques (e.g., Transformer model). Specifically, the server analyzes the text data and determines the emotional state (e.g., "anxiety"). Based on this input, the server identifies the user's emotion.

[0452] Step 5:

[0453] Based on the determined emotion, the server generates an appropriate response using a generative AI model (e.g., a Transformer model). Specifically, depending on the emotional state, it generates an appropriate response text (e.g., "That's terrible. Is there anything I can help you with?"). This input generates the response text.

[0454] Step 6:

[0455] The server sends the generated response text to the terminal. Specifically, data is sent to the terminal via Internet communication. This input allows the terminal to receive the response text.

[0456] Step 7:

[0457] The device uses speech synthesis technology (e.g., the pyttsx3 library) to convert the received response text into voice data. Specifically, the text data is analyzed by a speech synthesis engine and generated in a natural voice format. This process converts the response text into voice data.

[0458] Step 8:

[0459] The terminal outputs voice data and speaks to the user. Specifically, the voice is played back through a speaker. This process allows the user to receive an appropriate voice response generated by the terminal.

[0460] The above are the specific processing steps of the system that realizes the application example.

[0461] 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.

[0462] ---

[0463] A specific embodiment for carrying out the present invention will be described. This invention relates to a system that uses an emotion engine to recognize and determine a user's emotion, generates an appropriate response based on that emotion, and outputs it as voice.

[0464] First, the device receives voice input from the user. This input is in the form of voice, and the device converts it into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," the voice data is converted into text data.

[0465] This text data is then sent from the device to a server. The server receives the text data and uses an emotion engine to analyze its contents and recognize and determine the user's emotion. Specifically, the emotion engine uses machine learning technology to extract the emotion of "loneliness" from the text data.

[0466] If this emotion engine uses the user's past input data, it can achieve more accurate emotion recognition. It learns the user's tendencies and patterns from past data and accurately determines their current emotions based on that.

[0467] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "I'm sure that was lonely. I'm here, so please feel free to talk to me." This response is generated using Transformer-based natural language generation technology.

[0468] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0469] As a specific example, consider the case where a user makes the following statement:

[0470] User: "Today was a really tough day."

[0471] In this case, the device converts the voice into text and sends the text data to the server. The server then analyzes the text using its emotion engine and recognizes and determines that the user is expressing the emotion "tough." Based on the results of this analysis, the server then generates a response such as "That must be tough. Let me know if there's anything I can do to help you." This response is then transmitted to the user as voice from the device.

[0472] Through this series of processes, users can feel closer to the person and alleviate their feelings of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses. The system of the present invention is an effective means of cheering up and providing psychological support to users who feel lonely or isolated.

[0473] ---

[0474] The processing flow will be explained below.

[0475] ---

[0476] Step 1:

[0477] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[0478] Step 2:

[0479] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[0480] Step 3:

[0481] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[0482] Step 4:

[0483] The server analyzes the received text data using natural language processing techniques, first by tokenizing and parsing the text to analyze its context and structure.

[0484] Step 5:

[0485] The server uses an emotion engine to recognize and determine the user's emotions from the analyzed text data. For example, the emotion engine extracts the emotion "lonely." Furthermore, the accuracy of emotion recognition is improved by referencing the user's past input data.

[0486] Step 6:

[0487] The server generates an appropriate response based on the recognized emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[0488] Step 7:

[0489] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[0490] Step 8:

[0491] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[0492] Step 9:

[0493] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0494] ---

[0495] These are the specific processing steps from user input to the utterance of a response. This system allows users to feel closer to the person and alleviate their sense of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses.

[0496] Example 2

[0497] 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."

[0498] In modern society, the number of people feeling lonely and isolated is increasing, and measures to address this are needed. However, conventional systems have difficulty accurately recognizing users' emotions and providing appropriate responses. To solve this problem, a system is needed that can accurately recognize users' emotions and generate and output appropriate responses based on those emotions.

[0499] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice input from the user, means for converting the voice input into text data, means for analyzing the text data to determine the user's emotion, means for generating an appropriate response based on the determined emotion, and means for outputting the generated response as voice. This makes it possible to accurately recognize the user's emotion and provide a response based on that emotion, thereby reducing feelings of loneliness and sadness.

[0500] ---

[0501] "User" refers to any individual or entity that uses the System.

[0502] "Voice input" refers to voice data uttered by a user.

[0503] "Means" refers to a device, program, or method for performing a specified function.

[0504] "Text data" refers to text information converted from voice input.

[0505] "Analysis" refers to the process of analyzing data and extracting the information it contains.

[0506] "Emotion" represents the user's psychological state, and refers to states such as "lonely" or "difficult."

[0507] "Judgment" refers to the process of reaching a particular conclusion or evaluation based on the results of an analysis.

[0508] An "appropriate response" refers to a response content generated according to the user's emotions.

[0509] "Generation" refers to the process of creating new data or content.

[0510] "Speech" refers to auditory information that is output to the user.

[0511] "Output" refers to the process of providing generated information to the outside world.

[0512] MODE FOR CARRYING OUT THE INVENTION

[0513] The present invention is a system that analyzes a user's voice input, determines the emotion, generates an appropriate response, and outputs it as voice. Specific embodiments of this system are described in detail below.

[0514] First, the device receives voice input from the user. This voice input is recorded using a standard microphone. For example, if the user says, "I haven't been able to talk to anyone all day and I've been really lonely," the voice data is captured by the device.

[0515] The device converts the recorded voice data into text data using voice recognition technology (for example, general-purpose voice recognition software). Specifically, it converts the voice data into text data by using a voice recognition API such as Google Speech-to-Text. The converted text data will contain the following content: "I haven't been able to talk to anyone all day and I've been really lonely."

[0516] The device then sends this text data to a server over the Internet using the HTTPS protocol to ensure data security.

[0517] The server receives the text data and analyzes it using an emotion engine (e.g., emotion detection software using natural language processing technology). Specifically, it uses an API such as IBM Watson Natural Language Understanding to extract the user's emotion from the text data. In this case, the emotion engine recognizes the emotion of "loneliness."

[0518] Based on the analysis results, the server generates an appropriate response based on the user's emotions. Using a generative AI model (for example, Transformer-based natural language generation technology), the server generates a response such as, "That must have been lonely. I'm here, so please feel free to talk to me." Specifically, a generative AI model such as GPT-4 is used.

[0519] The generated response is then sent back to the device from the server, where it is converted into voice data using speech synthesis technology (for example, general-purpose speech synthesis software). This uses a speech synthesis API such as Amazon Polly. The converted voice data is then spoken to the user via the speaker.

[0520] As a concrete example, consider the following situation where a user says:

[0521] User: "Today was a really tough day."

[0522] In this case, the system performs the following process:

[0523] 1. The device receives the user's voice and converts the voice data into text data using Google Speech-to-Text.

[0524] 2. Send text data from the device to the server.

[0525] 3. The server uses an emotion engine to analyze the text data and recognize the emotion "difficult."

[0526] 4. The server uses the generative AI model to generate a text response saying, "That's tough work. Let me know if there's anything I can do to help."

[0527] 5. The server sends the generated response to the device.

[0528] 6. The device uses Amazon Polly to convert the response text into speech data and speaks it to the user.

[0529] This system allows users to receive friendly responses without feeling isolated or lonely. The combination of a generative AI model and an emotion engine makes it possible to accurately recognize the user's emotions and provide appropriate responses.

[0530] Prompt Sentence Examples

[0531] "Describe a system that accepts user input of emotionally charged speech and analyzes that speech to generate an appropriate response."

[0532] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0533] Processing Steps

[0534] Step 1:

[0535] The terminal receives the user's voice input.

[0536] Input: Voice data from the user Example: "I haven't had anyone to talk to all day today and I've been really lonely."

[0537] Output: Recorded audio data

[0538] Data processing: Audio is collected through a microphone.

[0539] Specific operation: The device's microphone records audio and stores it as data.

[0540] Step 2:

[0541] The device converts the recorded voice data into text data using voice recognition technology.

[0542] Input: Recorded audio data

[0543] Output: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0544] Data processing: Using speech recognition technology (e.g., Google Speech-to-Text), words are extracted while analyzing the audio data.

[0545] Specific operation: The device calls the voice recognition API and converts the voice data into text data.

[0546] Step 3:

[0547] The terminal transmits the converted text data to the server.

[0548] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0549] Output: Text data sent to the server

[0550] Data processing: Text data is sent to the server using the HTTP protocol.

[0551] Specific behavior: The device creates an HTTPS request, includes text data as a payload, and sends it to the specified server endpoint.

[0552] Step 4:

[0553] The server analyzes the text data and determines the user's emotions.

[0554] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0555] Output: User's emotion e.g. "loneliness"

[0556] Data Computing: Extract sentiment using natural language processing techniques (e.g., IBM Watson Natural Language Understanding).

[0557] Specific operation: The server calls the emotion engine API, analyzes the text data, and obtains emotion labels.

[0558] Step 5:

[0559] The server generates an appropriate response based on the user's emotions.

[0560] Input: User's emotion e.g. "loneliness"

[0561] Output: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0562] Data Computing: Use a generative AI model (e.g., GPT-4) to generate appropriate response text.

[0563] Specific operation: The server sends emotion labels and prompts to the generative AI model, which then generates an appropriate response.

[0564] Step 6:

[0565] The server sends the generated response text to the terminal.

[0566] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0567] Output: Response text sent to the terminal

[0568] Data processing: The response text is sent to the terminal using the HTTP protocol.

[0569] Specific behavior: The server includes the generated text response as the payload of an HTTPS request and sends it to the specified device endpoint.

[0570] Step 7:

[0571] The terminal converts the response text into voice data using speech synthesis technology and speaks it to the user.

[0572] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0573] Output: Audio data

[0574] Data processing: Convert text data into audio data using speech synthesis technology (e.g., Amazon Polly).

[0575] Specific operation: The device calls the speech synthesis API, converts the text data into speech data, and plays it back to the user through the speaker.

[0576] The above is a detailed flow of each processing step in this system, which allows users to receive friendly responses and reduce feelings of loneliness.

[0577] (Application example 2)

[0578] 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."

[0579] In modern brick-and-mortar stores, customer interaction is an important element. However, some customers find it difficult to express their emotions, and store clerks are often too busy to respond to each individual customer. This can result in customers being dissatisfied or not receiving appropriate service. The present invention solves these problems and realizes a system that provides emotional responses to customers.

[0580] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine the emotion, means for generating a response that induces the emotion based on the determined emotion, means for outputting the generated response as voice, and means for automatically generating an appropriate response according to the user's emotion and providing customer service in a physical store. This makes it possible to respond based on the individual emotions of customers.

[0581] A "user" is a person using the system.

[0582] "Input" refers to audio and text information provided by a user.

[0583] "Means" refer to the methods or techniques used to achieve a certain goal.

[0584] "Data" refers to text and audio information collected based on user input.

[0585] "Analysis" is the process of examining data in detail and extracting meaning and sentiment.

[0586] "Emotion" refers to the psychological state that a user is feeling.

[0587] "Judgment" is the process of drawing conclusions from analyzed data.

[0588] A "response" is a response generated by the system in response to a user's input.

[0589] "Generation" refers to the process by which a system creates new data or information.

[0590] "Voice" refers to vocal information produced by a user or a system.

[0591] "Output" refers to the process of providing generated data or responses to the user.

[0592] "Automatic generation" refers to the process by which a system generates a response without human intervention.

[0593] "Brick and mortar store" refers to a place that offers goods or services in a physical location.

[0594] "Customer service" refers to the process of providing service and guidance to customers who visit a physical store.

[0595] The present invention relates to a system for analyzing user emotions, generating appropriate responses, and providing customer service in a physical store. A specific embodiment for realizing this system is described below.

[0596] First, the system receives input from the user (customer). The input can be provided by voice or text. This process uses a microphone for voice input and speech recognition technology. The speech_recognition library is a suitable software for this.

[0597] Next, the user's input data is sent from the device to the server. The server receives this input data and analyzes it. Natural language processing technology is used for the analysis, and the user's emotions are analyzed and determined from the input data. A natural language processing library is used for this process.

[0598] The server then automatically generates a response based on the user's emotions based on the analysis results. The generative AI model uses the Transformers library and the GPT-2 model. This model is used to generate an appropriate response. For example, the following prompt sentence is input:

[0599] "Your customer is feeling tired. An appropriate response would be: Hello. Where are you from today? If you're tired, please rest on the couch here."

[0600] The generated response is sent from the server to the device, where it is converted into speech using speech synthesis technology using the pyttsx3 library, and the device then transmits the converted speech to the user through its speaker.

[0601] This process allows users to receive appropriate responses based on their emotions. For example, if a user says, "Today was a very difficult day," the system analyzes this input and determines the emotion "difficult." It then generates a response to the user, saying, "That must have been difficult. Let me know if there's anything I can do to help you," and delivers it to the user via voice.

[0602] The system of the present invention can dramatically improve customer service in brick-and-mortar stores by accurately analyzing the user's emotions and providing appropriate responses based on those emotions.

[0603] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0604] Step 1:

[0605] The user inputs voice into the device. The device recognizes the user's voice and converts the voice data into text data. At this time, the device uses the speech_recognition library. Voice input (voice data) is input and text data is output.

[0606] Step 2:

[0607] The terminal sends text data to the server. The server receives this text data and prepares it for analysis. The input is the text data sent from the terminal, and the output is the completed analysis preparation.

[0608] Step 3:

[0609] The server analyzes emotions based on text data. Here, natural language processing technology is used to extract meaning and emotions from the text. A natural language processing library is used for this process. The input is text data, and emotional data is obtained as the output.

[0610] Step 4:

[0611] The server generates an appropriate response based on the analyzed emotional data. This process uses a generative AI model using GPT-2. The generated prompt sentence includes the emotional data and is input to the generative model. The input is the emotional data and the prompt sentence, and the output is the generated response text.

[0612] Step 5:

[0613] The server sends the generated response text to the terminal, which converts the response text to speech. This process uses the pyttsx3 library. The input is the generated response text, and the output is speech data.

[0614] Step 6:

[0615] The terminal transmits the voice data to the user through a speaker, so that the user can hear the generated response. The input is voice data, and the output is the actual voice transmitted to the user.

[0616] This series of processing steps enables the system to accurately analyze the user's emotions and automatically generate an appropriate response in voice.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] [Third embodiment]

[0621] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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."

[0633] ---

[0634] A specific embodiment for carrying out the present invention will be described below. The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs it as voice.

[0635] First, the device receives input from the user. This input is often in the form of voice, which the device converts into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," this voice data is converted into text data.

[0636] This text data is then sent from the device to a server. The server receives the text data and uses natural language processing technology to analyze its content and determine the user's emotions. Specifically, it applies a sentiment analysis algorithm to extract the emotion "loneliness" from the text data.

[0637] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "That must have been lonely. I'm here, so please feel free to talk to me about anything." This response is generated using Transformer-based natural language generation technology.

[0638] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0639] Through this process, users will feel less lonely and isolated. For users who feel particularly lonely, providing emotional responses can provide psychological care.

[0640] As a specific example, consider the case where a user makes the following statement:

[0641] User: "Today was a really tough day."

[0642] In this case, the device converts the message into text and sends the text data to the server. The server determines from the text that the user is feeling "tough" and generates a response such as "That must be tough. Let me know if there's anything I can do to help you." Finally, this response is transmitted to the user as voice from the device.

[0643] This system is an effective means of cheering up and providing psychological support to users who feel lonely or isolated. By implementing this invention, users can enjoy conversations while feeling a sense of familiarity, and can help alleviate feelings of loneliness.

[0644] ---

[0645] The processing flow will be explained below.

[0646] ---

[0647] Step 1:

[0648] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[0649] Step 2:

[0650] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[0651] Step 3:

[0652] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[0653] Step 4:

[0654] The server uses natural language processing technology to analyze the received text data, extracting sentence structure, keywords, and sentiment.

[0655] Step 5:

[0656] The server applies a sentiment analysis algorithm to determine the user's sentiment, for example, "I felt lonely."

[0657] Step 6:

[0658] The server generates an appropriate response based on the determined emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[0659] Step 7:

[0660] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[0661] Step 8:

[0662] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[0663] Step 9:

[0664] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0665] ---

[0666] The above are the specific processing steps from the user's input to the utterance of a response. This system allows the user to feel closer to the person and alleviate their sense of loneliness.

[0667] Example 1

[0668] 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."

[0669] In recent years, the number of individuals experiencing feelings of loneliness and isolation has been increasing, but conventional systems have had difficulty accurately assessing the user's emotions and generating empathetic responses based on those emotions. Furthermore, while there is a need for a smooth process from voice input to voice response, previous systems have not been able to adequately address this issue. As a result, users' psychological care has not been effective, and this issue needs to be resolved.

[0670] 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.

[0671] In this invention, the server includes means for converting voice input into text data, means for analyzing the text data to determine emotions, and means for generating an appropriate response based on the determined emotions, thereby enabling the server to empathize with the user's emotions, generate an appropriate response, and output it as voice.

[0672] "User" refers to an individual, company, or other entity that operates or inputs data into the system.

[0673] "Voice input" refers to the act of a user providing information to a system using voice.

[0674] "Means" refers to a hardware or software method or technique for realizing a specific function within a system.

[0675] "Text data" refers to data that has been converted from voice input into text information.

[0676] "Voice recognition technology" refers to the technology that analyzes voice input and converts it into text data.

[0677] "Natural language processing technology" refers to all technologies that enable systems to understand and process human language.

[0678] "Judging emotions" refers to analyzing and recognizing user emotions from the content of text data using natural language processing technology.

[0679] "Generative AI models" refer to techniques and algorithms that use artificial intelligence to generate responses based on user input data and emotions.

[0680] "Response" refers to the verbal or audio output that a system generates and provides in response to user input.

[0681] "Speech synthesis technology" refers to the technology that converts text data into audible speech.

[0682] The present invention relates to a system that analyzes voice input from a user, determines the emotion, and generates an appropriate response based on that emotion and outputs it as voice. This system uses a combination of speech recognition technology, natural language processing technology, generative AI models, and speech synthesis technology.

[0683] First, the device receives voice input from the user. This device is a device that allows voice input, such as a smartphone, smart speaker, or PC. Once voice input is made, the device uses voice recognition technology to convert this voice into text data. Specifically, voice recognition services such as Google Speech-to-Text API and IBM Watson can be used. For example, if a user says, "I haven't had anyone to talk to all day, and I've been really lonely," this voice data is converted into text data that reads, "I haven't had anyone to talk to all day, and I've been really lonely."

[0684] Next, the device sends the converted text data to the server. This transmission process is carried out using an HTTP request, and the data is sent in JSON format. Once the server receives the text data, it uses natural language processing technology to analyze its content and determine the emotion. Specific implementations include the Google Natural Language API and Microsoft Azure text analysis services. In this step, emotions such as "loneliness" are extracted from the text data.

[0685] Based on the analysis results, the server uses a generative AI model (for example, OpenAI's GPT-3 or GPT-4) to generate an appropriate response. In this case, the response generated is something like, "I'm sure that was lonely. I'm here, so please feel free to talk to me." The generated response is then sent from the server to the device. This is also done via an HTTP request, with the response data being sent in JSON format.

[0686] The device converts the received response into speech using speech synthesis technology. Speech synthesis technologies available include Amazon Polly and Google Text-to-Speech. The converted speech is spoken to the user in a gentle tone. For example, the device might say, "That must have been lonely. I'm here, so please feel free to talk to me."

[0687] This system helps users feel less lonely and isolated, and provides psychological care. As a concrete example, we will explain the behavior when a user says, "Today was a very difficult day."

[0688] For example, a user might say, "Today was a really tough day." This speech is converted into text data and sent to a server. The server analyzes the text data and recognizes the emotion "tough." The server then uses a generative AI model to generate a response such as, "That must have been tough. Let me know if there's anything I can do to help you." This response is sent to the device, converted into speech, and spoken to the user.

[0689] The following are examples of prompt sentences:

[0690] "Please advise how to respond when a user feels lonely."

[0691] "If a user says, 'I had a really hard day today,' what would be an appropriate response?"

[0692] This invention allows users to enjoy conversation while feeling a sense of familiarity, and can alleviate feelings of loneliness.

[0693] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0694] Step 1:

[0695] The user inputs voice into the terminal. For example, the user says, "I haven't been able to talk to anyone all day and I've been really lonely." This input is passed to the terminal as voice data.

[0696] Step 2:

[0697] The device receives voice input and converts this voice into text data using voice recognition technology. Specifically, it uses voice recognition technology (e.g., voice recognition API) to analyze the input voice and generate text data such as "I haven't been able to talk to anyone all day today and I've been really lonely." The input here is voice data and the output is text data.

[0698] Step 3:

[0699] The terminal sends the converted text data to the server. This transmission is done using an HTTP request, and the text data is sent to the server in JSON format. Here, the input is the text data, and the output is the request to the server.

[0700] Step 4:

[0701] The server analyzes the received text data and determines the emotion. Specifically, it uses natural language processing technology to analyze the text and extract the emotion (e.g., text analysis service). In this case, the emotion "loneliness" is extracted. The input here is text data, and the output is emotion data.

[0702] Step 5:

[0703] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., a generative AI model), it generates a response based on the emotion data: "That must have been lonely. I'm here, so please feel free to talk to me." The input here is emotion data, and the output is response data.

[0704] Step 6:

[0705] The server generates a response and sends it to the terminal. This is also done using an HTTP request, and the response data is sent to the terminal in JSON format. The input here is the response data, and the output is the request to the terminal.

[0706] Step 7:

[0707] The response received by the device is converted into voice data using voice synthesis technology. Specifically, the text data is analyzed, and voice synthesis technology (e.g., a voice synthesis service) is used to generate voice data that says, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is the response data, and the output is voice data.

[0708] Step 8:

[0709] The device speaks the generated voice data to the user. The device speaks in a gentle tone, saying, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is voice data, and the output is voice output to the user.

[0710] This series of processing steps helps users reduce feelings of loneliness and isolation and allows them to receive psychological care.The system provides a friendly conversational experience by understanding emotions from user input and generating and uttering responses based on those emotions.

[0711] (Application example 1)

[0712] 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."

[0713] In modern society, users often seek psychological support to alleviate everyday stress and anxiety. It is particularly important to provide a sense of security through appropriate dialogue tailored to individual situations and emotions. However, conventional systems have had difficulty accurately analyzing users' emotions and providing appropriate responses. Therefore, there is a need for an effective system that uses the user's voice input to analyze emotions and generate responses that induce a sense of security.

[0714] 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.

[0715] In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine emotions, means for generating an emotion-inducing response based on the determined emotions, means for outputting the generated response as voice, means including voice recognition technology for converting the voice input into text data, server means including an emotion analysis algorithm for analyzing emotions from the text data, and means including voice synthesis technology for converting the generated response into voice data. This makes it possible to accurately analyze the user's emotions and provide an appropriate response in voice format according to each individual situation.

[0716] The "means for receiving input from the user" is an interface that allows the user to input data to the system in the form of voice, text, or the like.

[0717] "Means for analyzing user input data to determine emotion" refers to the algorithms or software used to process the input data and identify the user's emotional state.

[0718] "Means for generating a response that elicits an emotion" refers to a technique or method for creating an appropriate response that is in tune with the user's emotions based on the determined user's emotions.

[0719] The "means for outputting the generated response as audio" is a technology for converting the generated text data response into audio that can be heard by humans and playing it back through an output device such as a speaker or earphones.

[0720] "Speech recognition technology for converting speech input into text data" refers to technology that captures a user's speech and converts that speech into digital text format.

[0721] The "server means including a sentiment analysis algorithm for analyzing sentiment from text data" refers to a server and an algorithm running on the server for analyzing the text input by the user and determining the emotional state.

[0722] The "speech synthesis technology for converting the generated response into voice data" is a technology for converting a text-format response into voice format and outputting it as natural speech.

[0723] The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs the response as voice. Specific embodiments will be described in detail below.

[0724] First, the user inputs voice using a smartphone. This voice is collected through a microphone and converted into text data using the device's voice recognition technology (e.g., speech_recognition library). This text data is then sent to a server via the Internet.

[0725] The server receives the text data and analyzes the content using natural language processing techniques (e.g., a sentiment analysis algorithm using the Transformers model) to determine the user's sentiment. A specific sentiment analysis algorithm (e.g., the BERT model using the Transformers library) is used for this analysis.

[0726] For example, if a user says, "I haven't been able to sleep lately and I'm worried," the server performs sentiment analysis on the text data and extracts the emotion "anxiety." Based on the emotion determined, a response that induces a sense of relief is generated. This response is generated using natural language generation technology using a transformer model (e.g., the transformers library). An example of a response might be, "That's terrible. Is there anything I can help you with?"

[0727] The generated response is sent back to the terminal from the server, and the terminal converts the response into voice data using speech synthesis technology (e.g., the pyttsx3 library) and speaks it to the user, allowing the user to receive appropriate psychological care in the form of voice.

[0728] This system allows users to receive appropriate support tailored to their individual circumstances to cope with everyday anxieties and stress, and it also improves their sense of security, especially in terms of security.

[0729] As a specific example, suppose the user says, "I haven't been able to sleep lately and I'm worried." In this case, the following process is performed.

[0730] 1. The user's voice is collected by the smartphone and converted into text.

[0731] 2. The converted text is sent to the server for sentiment analysis.

[0732] 3. The emotion is determined to be "anxiety" and an appropriate response is generated (e.g., "That's terrible. Is there anything I can help you with?").

[0733] 4. The response is sent back to the smartphone and returned to the user as audio.

[0734] Example prompt sentence:

[0735] When a user says, "I've been having trouble sleeping lately and I'm worried," the app analyzes the emotion and responds, "That's tough. Is there anything I can help you with?"

[0736] The above is a specific embodiment for carrying out the invention.

[0737] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0738] Step 1:

[0739] The user uses a smartphone to input voice data. Specifically, the user speaks into the microphone. This voice data becomes the input.

[0740] Step 2:

[0741] The device collects voice input and converts the voice data into text data using speech recognition technology (e.g., the speech_recognition library). Specifically, the speech recognition engine analyzes the voice waveform and converts it into corresponding text. This process converts the voice data into text data.

[0742] Step 3:

[0743] The terminal sends the converted text data to the server. Specifically, the data is sent to the server via internet communication. This input allows the server to receive the text data.

[0744] Step 4:

[0745] To analyze the received text data, the server applies a sentiment analysis algorithm using natural language processing techniques (e.g., Transformer model). Specifically, the server analyzes the text data and determines the emotional state (e.g., "anxiety"). Based on this input, the server identifies the user's emotion.

[0746] Step 5:

[0747] Based on the determined emotion, the server generates an appropriate response using a generative AI model (e.g., a Transformer model). Specifically, depending on the emotional state, it generates an appropriate response text (e.g., "That's terrible. Is there anything I can help you with?"). This input generates the response text.

[0748] Step 6:

[0749] The server sends the generated response text to the terminal. Specifically, data is sent to the terminal via Internet communication. This input allows the terminal to receive the response text.

[0750] Step 7:

[0751] The device uses speech synthesis technology (e.g., the pyttsx3 library) to convert the received response text into voice data. Specifically, the text data is analyzed by a speech synthesis engine and generated in a natural voice format. This process converts the response text into voice data.

[0752] Step 8:

[0753] The terminal outputs voice data and speaks to the user. Specifically, the voice is played back through a speaker. This process allows the user to receive an appropriate voice response generated by the terminal.

[0754] The above are the specific processing steps of the system that realizes the application example.

[0755] 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.

[0756] ---

[0757] A specific embodiment for carrying out the present invention will be described. This invention relates to a system that uses an emotion engine to recognize and determine a user's emotion, generates an appropriate response based on that emotion, and outputs it as voice.

[0758] First, the device receives voice input from the user. This input is in the form of voice, and the device converts it into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," the voice data is converted into text data.

[0759] This text data is then sent from the device to a server. The server receives the text data and uses an emotion engine to analyze its contents and recognize and determine the user's emotion. Specifically, the emotion engine uses machine learning technology to extract the emotion of "loneliness" from the text data.

[0760] If this emotion engine uses the user's past input data, it can achieve more accurate emotion recognition. It learns the user's tendencies and patterns from past data and accurately determines their current emotions based on that.

[0761] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "I'm sure that was lonely. I'm here, so please feel free to talk to me." This response is generated using Transformer-based natural language generation technology.

[0762] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0763] As a specific example, consider the case where a user makes the following statement:

[0764] User: "Today was a really tough day."

[0765] In this case, the device converts the voice into text and sends the text data to the server. The server then analyzes the text using its emotion engine and recognizes and determines that the user is expressing the emotion "tough." Based on the results of this analysis, the server then generates a response such as "That must be tough. Let me know if there's anything I can do to help you." This response is then transmitted to the user as voice from the device.

[0766] Through this series of processes, users can feel closer to the person and alleviate their feelings of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses. The system of the present invention is an effective means of cheering up and providing psychological support to users who feel lonely or isolated.

[0767] ---

[0768] The processing flow will be explained below.

[0769] ---

[0770] Step 1:

[0771] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[0772] Step 2:

[0773] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[0774] Step 3:

[0775] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[0776] Step 4:

[0777] The server analyzes the received text data using natural language processing techniques, first by tokenizing and parsing the text to analyze its context and structure.

[0778] Step 5:

[0779] The server uses an emotion engine to recognize and determine the user's emotions from the analyzed text data. For example, the emotion engine extracts the emotion "lonely." Furthermore, the accuracy of emotion recognition is improved by referencing the user's past input data.

[0780] Step 6:

[0781] The server generates an appropriate response based on the recognized emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[0782] Step 7:

[0783] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[0784] Step 8:

[0785] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[0786] Step 9:

[0787] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0788] ---

[0789] These are the specific processing steps from user input to the utterance of a response. This system allows users to feel closer to the person and alleviate their sense of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses.

[0790] Example 2

[0791] 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."

[0792] In modern society, the number of people feeling lonely and isolated is increasing, and measures to address this are needed. However, conventional systems have difficulty accurately recognizing users' emotions and providing appropriate responses. To solve this problem, a system is needed that can accurately recognize users' emotions and generate and output appropriate responses based on those emotions.

[0793] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice input from the user, means for converting the voice input into text data, means for analyzing the text data to determine the user's emotion, means for generating an appropriate response based on the determined emotion, and means for outputting the generated response as voice. This makes it possible to accurately recognize the user's emotion and provide a response based on that emotion, thereby reducing feelings of loneliness and sadness.

[0794] ---

[0795] "User" refers to any individual or entity that uses the System.

[0796] "Voice input" refers to voice data uttered by a user.

[0797] "Means" refers to a device, program, or method for performing a specified function.

[0798] "Text data" refers to text information converted from voice input.

[0799] "Analysis" refers to the process of analyzing data and extracting the information it contains.

[0800] "Emotion" represents the user's psychological state, and refers to states such as "lonely" or "difficult."

[0801] "Judgment" refers to the process of reaching a particular conclusion or evaluation based on the results of an analysis.

[0802] An "appropriate response" refers to a response content generated according to the user's emotions.

[0803] "Generation" refers to the process of creating new data or content.

[0804] "Speech" refers to auditory information that is output to the user.

[0805] "Output" refers to the process of providing generated information to the outside world.

[0806] MODE FOR CARRYING OUT THE INVENTION

[0807] The present invention is a system that analyzes a user's voice input, determines the emotion, generates an appropriate response, and outputs it as voice. Specific embodiments of this system are described in detail below.

[0808] First, the device receives voice input from the user. This voice input is recorded using a standard microphone. For example, if the user says, "I haven't been able to talk to anyone all day and I've been really lonely," the voice data is captured by the device.

[0809] The device converts the recorded voice data into text data using voice recognition technology (for example, general-purpose voice recognition software). Specifically, it converts the voice data into text data by using a voice recognition API such as Google Speech-to-Text. The converted text data will contain the following content: "I haven't been able to talk to anyone all day and I've been really lonely."

[0810] The device then sends this text data to a server over the Internet using the HTTPS protocol to ensure data security.

[0811] The server receives the text data and analyzes it using an emotion engine (e.g., emotion detection software using natural language processing technology). Specifically, it uses an API such as IBM Watson Natural Language Understanding to extract the user's emotion from the text data. In this case, the emotion engine recognizes the emotion of "loneliness."

[0812] Based on the analysis results, the server generates an appropriate response based on the user's emotions. Using a generative AI model (for example, Transformer-based natural language generation technology), the server generates a response such as, "That must have been lonely. I'm here, so please feel free to talk to me." Specifically, a generative AI model such as GPT-4 is used.

[0813] The generated response is then sent back to the device from the server, where it is converted into voice data using speech synthesis technology (for example, general-purpose speech synthesis software). This uses a speech synthesis API such as Amazon Polly. The converted voice data is then spoken to the user via the speaker.

[0814] As a concrete example, consider the following situation where a user says:

[0815] User: "Today was a really tough day."

[0816] In this case, the system performs the following process:

[0817] 1. The device receives the user's voice and converts the voice data into text data using Google Speech-to-Text.

[0818] 2. Send text data from the device to the server.

[0819] 3. The server uses an emotion engine to analyze the text data and recognize the emotion "difficult."

[0820] 4. The server uses the generative AI model to generate a text response saying, "That's tough work. Let me know if there's anything I can do to help."

[0821] 5. The server sends the generated response to the device.

[0822] 6. The device uses Amazon Polly to convert the response text into speech data and speaks it to the user.

[0823] This system allows users to receive friendly responses without feeling isolated or lonely. The combination of a generative AI model and an emotion engine makes it possible to accurately recognize the user's emotions and provide appropriate responses.

[0824] Prompt Sentence Examples

[0825] "Describe a system that accepts user input of emotionally charged speech and analyzes that speech to generate an appropriate response."

[0826] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0827] Processing Steps

[0828] Step 1:

[0829] The terminal receives the user's voice input.

[0830] Input: Voice data from the user Example: "I haven't had anyone to talk to all day today and I've been really lonely."

[0831] Output: Recorded audio data

[0832] Data processing: Audio is collected through a microphone.

[0833] Specific operation: The device's microphone records audio and stores it as data.

[0834] Step 2:

[0835] The device converts the recorded voice data into text data using voice recognition technology.

[0836] Input: Recorded audio data

[0837] Output: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0838] Data processing: Using speech recognition technology (e.g., Google Speech-to-Text), words are extracted while analyzing the audio data.

[0839] Specific operation: The device calls the voice recognition API and converts the voice data into text data.

[0840] Step 3:

[0841] The terminal transmits the converted text data to the server.

[0842] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0843] Output: Text data sent to the server

[0844] Data processing: Text data is sent to the server using the HTTP protocol.

[0845] Specific behavior: The device creates an HTTPS request, includes text data as a payload, and sends it to the specified server endpoint.

[0846] Step 4:

[0847] The server analyzes the text data and determines the user's emotions.

[0848] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[0849] Output: User's emotion e.g. "loneliness"

[0850] Data Computing: Extract sentiment using natural language processing techniques (e.g., IBM Watson Natural Language Understanding).

[0851] Specific operation: The server calls the emotion engine API, analyzes the text data, and obtains emotion labels.

[0852] Step 5:

[0853] The server generates an appropriate response based on the user's emotions.

[0854] Input: User's emotion e.g. "loneliness"

[0855] Output: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0856] Data Computing: Use a generative AI model (e.g., GPT-4) to generate appropriate response text.

[0857] Specific operation: The server sends emotion labels and prompts to the generative AI model, which then generates an appropriate response.

[0858] Step 6:

[0859] The server sends the generated response text to the terminal.

[0860] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0861] Output: Response text sent to the terminal

[0862] Data processing: The response text is sent to the terminal using the HTTP protocol.

[0863] Specific behavior: The server includes the generated text response as the payload of an HTTPS request and sends it to the specified device endpoint.

[0864] Step 7:

[0865] The terminal converts the response text into voice data using speech synthesis technology and speaks it to the user.

[0866] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0867] Output: Audio data

[0868] Data processing: Convert text data into audio data using speech synthesis technology (e.g., Amazon Polly).

[0869] Specific operation: The device calls the speech synthesis API, converts the text data into speech data, and plays it back to the user through the speaker.

[0870] The above is a detailed flow of each processing step in this system, which allows users to receive friendly responses and reduce feelings of loneliness.

[0871] (Application example 2)

[0872] 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."

[0873] In modern brick-and-mortar stores, customer interaction is an important element. However, some customers find it difficult to express their emotions, and store clerks are often too busy to respond to each individual customer. This can result in customers being dissatisfied or not receiving appropriate service. The present invention solves these problems and realizes a system that provides emotional responses to customers.

[0874] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine the emotion, means for generating a response that induces the emotion based on the determined emotion, means for outputting the generated response as voice, and means for automatically generating an appropriate response according to the user's emotion and providing customer service in a physical store. This makes it possible to respond based on the individual emotions of customers.

[0875] A "user" is a person using the system.

[0876] "Input" refers to audio and text information provided by a user.

[0877] "Means" refer to the methods or techniques used to achieve a certain goal.

[0878] "Data" refers to text and audio information collected based on user input.

[0879] "Analysis" is the process of examining data in detail and extracting meaning and sentiment.

[0880] "Emotion" refers to the psychological state that a user is feeling.

[0881] "Judgment" is the process of drawing conclusions from analyzed data.

[0882] A "response" is a response generated by the system in response to a user's input.

[0883] "Generation" refers to the process by which a system creates new data or information.

[0884] "Voice" refers to vocal information produced by a user or a system.

[0885] "Output" refers to the process of providing generated data or responses to the user.

[0886] "Automatic generation" refers to the process by which a system generates a response without human intervention.

[0887] "Brick and mortar store" refers to a place that offers goods or services in a physical location.

[0888] "Customer service" refers to the process of providing service and guidance to customers who visit a physical store.

[0889] The present invention relates to a system for analyzing user emotions, generating appropriate responses, and providing customer service in a physical store. A specific embodiment for realizing this system is described below.

[0890] First, the system receives input from the user (customer). The input can be provided by voice or text. This process uses a microphone for voice input and speech recognition technology. The speech_recognition library is a suitable software for this.

[0891] Next, the user's input data is sent from the device to the server. The server receives this input data and analyzes it. Natural language processing technology is used for the analysis, and the user's emotions are analyzed and determined from the input data. A natural language processing library is used for this process.

[0892] The server then automatically generates a response based on the user's emotions based on the analysis results. The generative AI model uses the Transformers library and the GPT-2 model. This model is used to generate an appropriate response. For example, the following prompt sentence is input:

[0893] "Your customer is feeling tired. An appropriate response would be: Hello. Where are you from today? If you're tired, please rest on the couch here."

[0894] The generated response is sent from the server to the device, where it is converted into speech using speech synthesis technology using the pyttsx3 library, and the device then transmits the converted speech to the user through its speaker.

[0895] This process allows users to receive appropriate responses based on their emotions. For example, if a user says, "Today was a very difficult day," the system analyzes this input and determines the emotion "difficult." It then generates a response to the user, saying, "That must have been difficult. Let me know if there's anything I can do to help you," and delivers it to the user via voice.

[0896] The system of the present invention can dramatically improve customer service in brick-and-mortar stores by accurately analyzing the user's emotions and providing appropriate responses based on those emotions.

[0897] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0898] Step 1:

[0899] The user inputs voice into the device. The device recognizes the user's voice and converts the voice data into text data. At this time, the device uses the speech_recognition library. Voice input (voice data) is input and text data is output.

[0900] Step 2:

[0901] The terminal sends text data to the server. The server receives this text data and prepares it for analysis. The input is the text data sent from the terminal, and the output is the completed analysis preparation.

[0902] Step 3:

[0903] The server analyzes emotions based on text data. Here, natural language processing technology is used to extract meaning and emotions from the text. A natural language processing library is used for this process. The input is text data, and emotional data is obtained as the output.

[0904] Step 4:

[0905] The server generates an appropriate response based on the analyzed emotional data. This process uses a generative AI model using GPT-2. The generated prompt sentence includes the emotional data and is input to the generative model. The input is the emotional data and the prompt sentence, and the output is the generated response text.

[0906] Step 5:

[0907] The server sends the generated response text to the terminal, which converts the response text to speech. This process uses the pyttsx3 library. The input is the generated response text, and the output is speech data.

[0908] Step 6:

[0909] The terminal transmits the voice data to the user through a speaker, so that the user can hear the generated response. The input is voice data, and the output is the actual voice transmitted to the user.

[0910] This series of processing steps enables the system to accurately analyze the user's emotions and automatically generate an appropriate response in voice.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] [Fourth embodiment]

[0915] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0916] 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.

[0917] 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).

[0918] 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.

[0919] 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.

[0920] 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).

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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."

[0928] ---

[0929] A specific embodiment for carrying out the present invention will be described below. The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs it as voice.

[0930] First, the device receives input from the user. This input is often in the form of voice, which the device converts into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," this voice data is converted into text data.

[0931] This text data is then sent from the device to a server. The server receives the text data and uses natural language processing technology to analyze its content and determine the user's emotions. Specifically, it applies a sentiment analysis algorithm to extract the emotion "loneliness" from the text data.

[0932] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "That must have been lonely. I'm here, so please feel free to talk to me about anything." This response is generated using Transformer-based natural language generation technology.

[0933] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0934] Through this process, users will feel less lonely and isolated. For users who feel particularly lonely, providing emotional responses can provide psychological care.

[0935] As a specific example, consider the case where a user makes the following statement:

[0936] User: "Today was a really tough day."

[0937] In this case, the device converts the message into text and sends the text data to the server. The server determines from the text that the user is feeling "tough" and generates a response such as "That must be tough. Let me know if there's anything I can do to help you." Finally, this response is transmitted to the user as voice from the device.

[0938] This system is an effective means of cheering up and providing psychological support to users who feel lonely or isolated. By implementing this invention, users can enjoy conversations while feeling a sense of familiarity, and can help alleviate feelings of loneliness.

[0939] ---

[0940] The processing flow will be explained below.

[0941] ---

[0942] Step 1:

[0943] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[0944] Step 2:

[0945] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[0946] Step 3:

[0947] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[0948] Step 4:

[0949] The server uses natural language processing technology to analyze the received text data, extracting sentence structure, keywords, and sentiment.

[0950] Step 5:

[0951] The server applies a sentiment analysis algorithm to determine the user's sentiment, for example, "I felt lonely."

[0952] Step 6:

[0953] The server generates an appropriate response based on the determined emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[0954] Step 7:

[0955] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[0956] Step 8:

[0957] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[0958] Step 9:

[0959] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[0960] ---

[0961] The above are the specific processing steps from the user's input to the utterance of a response. This system allows the user to feel closer to the person and alleviate their sense of loneliness.

[0962] Example 1

[0963] 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."

[0964] In recent years, the number of individuals experiencing feelings of loneliness and isolation has been increasing, but conventional systems have had difficulty accurately assessing the user's emotions and generating empathetic responses based on those emotions. Furthermore, while there is a need for a smooth process from voice input to voice response, previous systems have not been able to adequately address this issue. As a result, users' psychological care has not been effective, and this issue needs to be resolved.

[0965] 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.

[0966] In this invention, the server includes means for converting voice input into text data, means for analyzing the text data to determine emotions, and means for generating an appropriate response based on the determined emotions, thereby enabling the server to empathize with the user's emotions, generate an appropriate response, and output it as voice.

[0967] "User" refers to an individual, company, or other entity that operates or inputs data into the system.

[0968] "Voice input" refers to the act of a user providing information to a system using voice.

[0969] "Means" refers to a hardware or software method or technique for realizing a specific function within a system.

[0970] "Text data" refers to data that has been converted from voice input into text information.

[0971] "Voice recognition technology" refers to the technology that analyzes voice input and converts it into text data.

[0972] "Natural language processing technology" refers to all technologies that enable systems to understand and process human language.

[0973] "Judging emotions" refers to analyzing and recognizing user emotions from the content of text data using natural language processing technology.

[0974] "Generative AI models" refer to techniques and algorithms that use artificial intelligence to generate responses based on user input data and emotions.

[0975] "Response" refers to the verbal or audio output that a system generates and provides in response to user input.

[0976] "Speech synthesis technology" refers to the technology that converts text data into audible speech.

[0977] The present invention relates to a system that analyzes voice input from a user, determines the emotion, and generates an appropriate response based on that emotion and outputs it as voice. This system uses a combination of speech recognition technology, natural language processing technology, generative AI models, and speech synthesis technology.

[0978] First, the device receives voice input from the user. This device is a device that allows voice input, such as a smartphone, smart speaker, or PC. Once voice input is made, the device uses voice recognition technology to convert this voice into text data. Specifically, voice recognition services such as Google Speech-to-Text API and IBM Watson can be used. For example, if a user says, "I haven't had anyone to talk to all day, and I've been really lonely," this voice data is converted into text data that reads, "I haven't had anyone to talk to all day, and I've been really lonely."

[0979] Next, the device sends the converted text data to the server. This transmission process is carried out using an HTTP request, and the data is sent in JSON format. Once the server receives the text data, it uses natural language processing technology to analyze its content and determine the emotion. Specific implementations include the Google Natural Language API and Microsoft Azure text analysis services. In this step, emotions such as "loneliness" are extracted from the text data.

[0980] Based on the analysis results, the server uses a generative AI model (for example, OpenAI's GPT-3 or GPT-4) to generate an appropriate response. In this case, the response generated is something like, "I'm sure that was lonely. I'm here, so please feel free to talk to me." The generated response is then sent from the server to the device. This is also done via an HTTP request, with the response data being sent in JSON format.

[0981] The device converts the received response into speech using speech synthesis technology. Speech synthesis technologies available include Amazon Polly and Google Text-to-Speech. The converted speech is spoken to the user in a gentle tone. For example, the device might say, "That must have been lonely. I'm here, so please feel free to talk to me."

[0982] This system helps users feel less lonely and isolated, and provides psychological care. As a concrete example, we will explain the behavior when a user says, "Today was a very difficult day."

[0983] For example, a user might say, "Today was a really tough day." This speech is converted into text data and sent to a server. The server analyzes the text data and recognizes the emotion "tough." The server then uses a generative AI model to generate a response such as, "That must have been tough. Let me know if there's anything I can do to help you." This response is sent to the device, converted into speech, and spoken to the user.

[0984] The following are examples of prompt sentences:

[0985] "Please advise how to respond when a user feels lonely."

[0986] "If a user says, 'I had a really hard day today,' what would be an appropriate response?"

[0987] This invention allows users to enjoy conversation while feeling a sense of familiarity, and can alleviate feelings of loneliness.

[0988] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0989] Step 1:

[0990] The user inputs voice into the terminal. For example, the user says, "I haven't been able to talk to anyone all day and I've been really lonely." This input is passed to the terminal as voice data.

[0991] Step 2:

[0992] The device receives voice input and converts this voice into text data using voice recognition technology. Specifically, it uses voice recognition technology (e.g., voice recognition API) to analyze the input voice and generate text data such as "I haven't been able to talk to anyone all day today and I've been really lonely." The input here is voice data and the output is text data.

[0993] Step 3:

[0994] The terminal sends the converted text data to the server. This transmission is done using an HTTP request, and the text data is sent to the server in JSON format. Here, the input is the text data, and the output is the request to the server.

[0995] Step 4:

[0996] The server analyzes the received text data and determines the emotion. Specifically, it uses natural language processing technology to analyze the text and extract the emotion (e.g., text analysis service). In this case, the emotion "loneliness" is extracted. The input here is text data, and the output is emotion data.

[0997] Step 5:

[0998] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., a generative AI model), it generates a response based on the emotion data: "That must have been lonely. I'm here, so please feel free to talk to me." The input here is emotion data, and the output is response data.

[0999] Step 6:

[1000] The server generates a response and sends it to the terminal. This is also done using an HTTP request, and the response data is sent to the terminal in JSON format. The input here is the response data, and the output is the request to the terminal.

[1001] Step 7:

[1002] The response received by the device is converted into voice data using voice synthesis technology. Specifically, the text data is analyzed, and voice synthesis technology (e.g., a voice synthesis service) is used to generate voice data that says, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is the response data, and the output is voice data.

[1003] Step 8:

[1004] The device speaks the generated voice data to the user. The device speaks in a gentle tone, saying, "That must have been lonely. I'm here, so please feel free to talk to me about anything." The input here is voice data, and the output is voice output to the user.

[1005] This series of processing steps helps users reduce feelings of loneliness and isolation and allows them to receive psychological care.The system provides a friendly conversational experience by understanding emotions from user input and generating and uttering responses based on those emotions.

[1006] (Application example 1)

[1007] 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."

[1008] In modern society, users often seek psychological support to alleviate everyday stress and anxiety. It is particularly important to provide a sense of security through appropriate dialogue tailored to individual situations and emotions. However, conventional systems have had difficulty accurately analyzing users' emotions and providing appropriate responses. Therefore, there is a need for an effective system that uses the user's voice input to analyze emotions and generate responses that induce a sense of security.

[1009] 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.

[1010] In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine emotions, means for generating an emotion-inducing response based on the determined emotions, means for outputting the generated response as voice, means including voice recognition technology for converting the voice input into text data, server means including an emotion analysis algorithm for analyzing emotions from the text data, and means including voice synthesis technology for converting the generated response into voice data. This makes it possible to accurately analyze the user's emotions and provide an appropriate response in voice format according to each individual situation.

[1011] The "means for receiving input from the user" is an interface that allows the user to input data to the system in the form of voice, text, or the like.

[1012] "Means for analyzing user input data to determine emotion" refers to the algorithms or software used to process the input data and identify the user's emotional state.

[1013] "Means for generating a response that elicits an emotion" refers to a technique or method for creating an appropriate response that is in tune with the user's emotions based on the determined user's emotions.

[1014] The "means for outputting the generated response as audio" is a technology for converting the generated text data response into audio that can be heard by humans and playing it back through an output device such as a speaker or earphones.

[1015] "Speech recognition technology for converting speech input into text data" refers to technology that captures a user's speech and converts that speech into digital text format.

[1016] The "server means including a sentiment analysis algorithm for analyzing sentiment from text data" refers to a server and an algorithm running on the server for analyzing the text input by the user and determining the emotional state.

[1017] The "speech synthesis technology for converting the generated response into voice data" is a technology for converting a text-format response into voice format and outputting it as natural speech.

[1018] The present invention relates to a system that analyzes input from a user, determines the emotion, generates an appropriate response based on the emotion, and outputs the response as voice. Specific embodiments will be described in detail below.

[1019] First, the user inputs voice using a smartphone. This voice is collected through a microphone and converted into text data using the device's voice recognition technology (e.g., speech_recognition library). This text data is then sent to a server via the Internet.

[1020] The server receives the text data and analyzes the content using natural language processing techniques (e.g., a sentiment analysis algorithm using the Transformers model) to determine the user's sentiment. A specific sentiment analysis algorithm (e.g., the BERT model using the Transformers library) is used for this analysis.

[1021] For example, if a user says, "I haven't been able to sleep lately and I'm worried," the server performs sentiment analysis on the text data and extracts the emotion "anxiety." Based on the emotion determined, a response that induces a sense of relief is generated. This response is generated using natural language generation technology using a transformer model (e.g., the transformers library). An example of a response might be, "That's terrible. Is there anything I can help you with?"

[1022] The generated response is sent back to the terminal from the server, and the terminal converts the response into voice data using speech synthesis technology (e.g., the pyttsx3 library) and speaks it to the user, allowing the user to receive appropriate psychological care in the form of voice.

[1023] This system allows users to receive appropriate support tailored to their individual circumstances to cope with everyday anxieties and stress, and it also improves their sense of security, especially in terms of security.

[1024] As a specific example, suppose the user says, "I haven't been able to sleep lately and I'm worried." In this case, the following process is performed.

[1025] 1. The user's voice is collected by the smartphone and converted into text.

[1026] 2. The converted text is sent to the server for sentiment analysis.

[1027] 3. The emotion is determined to be "anxiety" and an appropriate response is generated (e.g., "That's terrible. Is there anything I can help you with?").

[1028] 4. The response is sent back to the smartphone and returned to the user as audio.

[1029] Example prompt sentence:

[1030] When a user says, "I've been having trouble sleeping lately and I'm worried," the app analyzes the emotion and responds, "That's tough. Is there anything I can help you with?"

[1031] The above is a specific embodiment for carrying out the invention.

[1032] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1033] Step 1:

[1034] The user uses a smartphone to input voice data. Specifically, the user speaks into the microphone. This voice data becomes the input.

[1035] Step 2:

[1036] The device collects voice input and converts the voice data into text data using speech recognition technology (e.g., the speech_recognition library). Specifically, the speech recognition engine analyzes the voice waveform and converts it into corresponding text. This process converts the voice data into text data.

[1037] Step 3:

[1038] The terminal sends the converted text data to the server. Specifically, the data is sent to the server via internet communication. This input allows the server to receive the text data.

[1039] Step 4:

[1040] To analyze the received text data, the server applies a sentiment analysis algorithm using natural language processing techniques (e.g., Transformer model). Specifically, the server analyzes the text data and determines the emotional state (e.g., "anxiety"). Based on this input, the server identifies the user's emotion.

[1041] Step 5:

[1042] Based on the determined emotion, the server generates an appropriate response using a generative AI model (e.g., a Transformer model). Specifically, depending on the emotional state, it generates an appropriate response text (e.g., "That's terrible. Is there anything I can help you with?"). This input generates the response text.

[1043] Step 6:

[1044] The server sends the generated response text to the terminal. Specifically, data is sent to the terminal via Internet communication. This input allows the terminal to receive the response text.

[1045] Step 7:

[1046] The device uses speech synthesis technology (e.g., the pyttsx3 library) to convert the received response text into voice data. Specifically, the text data is analyzed by a speech synthesis engine and generated in a natural voice format. This process converts the response text into voice data.

[1047] Step 8:

[1048] The terminal outputs voice data and speaks to the user. Specifically, the voice is played back through a speaker. This process allows the user to receive an appropriate voice response generated by the terminal.

[1049] The above are the specific processing steps of the system that realizes the application example.

[1050] 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.

[1051] ---

[1052] A specific embodiment for carrying out the present invention will be described. This invention relates to a system that uses an emotion engine to recognize and determine a user's emotion, generates an appropriate response based on that emotion, and outputs it as voice.

[1053] First, the device receives voice input from the user. This input is in the form of voice, and the device converts it into text data using voice recognition technology. For example, if the user says, "I haven't been able to talk to anyone all day today and I've been really lonely," the voice data is converted into text data.

[1054] This text data is then sent from the device to a server. The server receives the text data and uses an emotion engine to analyze its contents and recognize and determine the user's emotion. Specifically, the emotion engine uses machine learning technology to extract the emotion of "loneliness" from the text data.

[1055] If this emotion engine uses the user's past input data, it can achieve more accurate emotion recognition. It learns the user's tendencies and patterns from past data and accurately determines their current emotions based on that.

[1056] Based on the analysis results, the server generates an appropriate response that matches the user's feelings. In this case, for example, the response generated might be, "I'm sure that was lonely. I'm here, so please feel free to talk to me." This response is generated using Transformer-based natural language generation technology.

[1057] The generated response is then sent back from the server to the device. The device then converts the received response into voice data using speech synthesis technology and speaks it to the user. For example, the device might say in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[1058] As a specific example, consider the case where a user makes the following statement:

[1059] User: "Today was a really tough day."

[1060] In this case, the device converts the voice into text and sends the text data to the server. The server then analyzes the text using its emotion engine and recognizes and determines that the user is expressing the emotion "tough." Based on the results of this analysis, the server then generates a response such as "That must be tough. Let me know if there's anything I can do to help you." This response is then transmitted to the user as voice from the device.

[1061] Through this series of processes, users can feel closer to the person and alleviate their feelings of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses. The system of the present invention is an effective means of cheering up and providing psychological support to users who feel lonely or isolated.

[1062] ---

[1063] The processing flow will be explained below.

[1064] ---

[1065] Step 1:

[1066] The user speaks to the device, for example, saying, "I haven't had anyone to talk to all day today and I've been really lonely."

[1067] Step 2:

[1068] The device converts the user's voice into text data using voice recognition technology. For example, the voice data "I haven't been able to talk to anyone all day today, and I've been really lonely" is converted into text data.

[1069] Step 3:

[1070] The terminal transmits the converted text data to the server. Specifically, the terminal transmits the text data "I couldn't talk to anyone all day today and I was really lonely."

[1071] Step 4:

[1072] The server analyzes the received text data using natural language processing techniques, first by tokenizing and parsing the text to analyze its context and structure.

[1073] Step 5:

[1074] The server uses an emotion engine to recognize and determine the user's emotions from the analyzed text data. For example, the emotion engine extracts the emotion "lonely." Furthermore, the accuracy of emotion recognition is improved by referencing the user's past input data.

[1075] Step 6:

[1076] The server generates an appropriate response based on the recognized emotion, for example, "That must have been lonely. I'm here, so please feel free to talk to me." using Transformer-based natural language generation technology.

[1077] Step 7:

[1078] The server sends the generated response to the terminal. Specifically, it sends text data saying, "I'm sure that was lonely. I'm here, so please feel free to talk to me about anything."

[1079] Step 8:

[1080] The terminal converts the received response text into voice data using a voice synthesis technology, for example, by executing a process for converting text data into voice data.

[1081] Step 9:

[1082] The device plays the generated voice data and speaks to the user, for example, saying in a gentle tone, "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[1083] ---

[1084] These are the specific processing steps from user input to the utterance of a response. This system allows users to feel closer to the person and alleviate their sense of loneliness. In particular, the addition of an emotion engine makes it possible to more accurately recognize the user's emotions and provide appropriate responses.

[1085] Example 2

[1086] 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."

[1087] In modern society, the number of people feeling lonely and isolated is increasing, and measures to address this are needed. However, conventional systems have difficulty accurately recognizing users' emotions and providing appropriate responses. To solve this problem, a system is needed that can accurately recognize users' emotions and generate and output appropriate responses based on those emotions.

[1088] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice input from the user, means for converting the voice input into text data, means for analyzing the text data to determine the user's emotion, means for generating an appropriate response based on the determined emotion, and means for outputting the generated response as voice. This makes it possible to accurately recognize the user's emotion and provide a response based on that emotion, thereby reducing feelings of loneliness and sadness.

[1089] ---

[1090] "User" refers to any individual or entity that uses the System.

[1091] "Voice input" refers to voice data uttered by a user.

[1092] "Means" refers to a device, program, or method for performing a specified function.

[1093] "Text data" refers to text information converted from voice input.

[1094] "Analysis" refers to the process of analyzing data and extracting the information it contains.

[1095] "Emotion" represents the user's psychological state, and refers to states such as "lonely" or "difficult."

[1096] "Judgment" refers to the process of reaching a particular conclusion or evaluation based on the results of an analysis.

[1097] An "appropriate response" refers to a response content generated according to the user's emotions.

[1098] "Generation" refers to the process of creating new data or content.

[1099] "Speech" refers to auditory information that is output to the user.

[1100] "Output" refers to the process of providing generated information to the outside world.

[1101] MODE FOR CARRYING OUT THE INVENTION

[1102] The present invention is a system that analyzes a user's voice input, determines the emotion, generates an appropriate response, and outputs it as voice. Specific embodiments of this system are described in detail below.

[1103] First, the device receives voice input from the user. This voice input is recorded using a standard microphone. For example, if the user says, "I haven't been able to talk to anyone all day and I've been really lonely," the voice data is captured by the device.

[1104] The device converts the recorded voice data into text data using voice recognition technology (for example, general-purpose voice recognition software). Specifically, it converts the voice data into text data by using a voice recognition API such as Google Speech-to-Text. The converted text data will contain the following content: "I haven't been able to talk to anyone all day and I've been really lonely."

[1105] The device then sends this text data to a server over the Internet using the HTTPS protocol to ensure data security.

[1106] The server receives the text data and analyzes it using an emotion engine (e.g., emotion detection software using natural language processing technology). Specifically, it uses an API such as IBM Watson Natural Language Understanding to extract the user's emotion from the text data. In this case, the emotion engine recognizes the emotion of "loneliness."

[1107] Based on the analysis results, the server generates an appropriate response based on the user's emotions. Using a generative AI model (for example, Transformer-based natural language generation technology), the server generates a response such as, "That must have been lonely. I'm here, so please feel free to talk to me." Specifically, a generative AI model such as GPT-4 is used.

[1108] The generated response is then sent back to the device from the server, where it is converted into voice data using speech synthesis technology (for example, general-purpose speech synthesis software). This uses a speech synthesis API such as Amazon Polly. The converted voice data is then spoken to the user via the speaker.

[1109] As a concrete example, consider the following situation where a user says:

[1110] User: "Today was a really tough day."

[1111] In this case, the system performs the following process:

[1112] 1. The device receives the user's voice and converts the voice data into text data using Google Speech-to-Text.

[1113] 2. Send text data from the device to the server.

[1114] 3. The server uses an emotion engine to analyze the text data and recognize the emotion "difficult."

[1115] 4. The server uses the generative AI model to generate a text response saying, "That's tough work. Let me know if there's anything I can do to help."

[1116] 5. The server sends the generated response to the device.

[1117] 6. The device uses Amazon Polly to convert the response text into speech data and speaks it to the user.

[1118] This system allows users to receive friendly responses without feeling isolated or lonely. The combination of a generative AI model and an emotion engine makes it possible to accurately recognize the user's emotions and provide appropriate responses.

[1119] Prompt Sentence Examples

[1120] "Describe a system that accepts user input of emotionally charged speech and analyzes that speech to generate an appropriate response."

[1121] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1122] Processing Steps

[1123] Step 1:

[1124] The terminal receives the user's voice input.

[1125] Input: Voice data from the user Example: "I haven't had anyone to talk to all day today and I've been really lonely."

[1126] Output: Recorded audio data

[1127] Data processing: Audio is collected through a microphone.

[1128] Specific operation: The device's microphone records audio and stores it as data.

[1129] Step 2:

[1130] The device converts the recorded voice data into text data using voice recognition technology.

[1131] Input: Recorded audio data

[1132] Output: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[1133] Data processing: Using speech recognition technology (e.g., Google Speech-to-Text), words are extracted while analyzing the audio data.

[1134] Specific operation: The device calls the voice recognition API and converts the voice data into text data.

[1135] Step 3:

[1136] The terminal transmits the converted text data to the server.

[1137] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[1138] Output: Text data sent to the server

[1139] Data processing: Text data is sent to the server using the HTTP protocol.

[1140] Specific behavior: The device creates an HTTPS request, includes text data as a payload, and sends it to the specified server endpoint.

[1141] Step 4:

[1142] The server analyzes the text data and determines the user's emotions.

[1143] Input: Text data Example: "I couldn't talk to anyone all day today and I was really lonely."

[1144] Output: User's emotion e.g. "loneliness"

[1145] Data Computing: Extract sentiment using natural language processing techniques (e.g., IBM Watson Natural Language Understanding).

[1146] Specific operation: The server calls the emotion engine API, analyzes the text data, and obtains emotion labels.

[1147] Step 5:

[1148] The server generates an appropriate response based on the user's emotions.

[1149] Input: User's emotion e.g. "loneliness"

[1150] Output: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[1151] Data Computing: Use a generative AI model (e.g., GPT-4) to generate appropriate response text.

[1152] Specific operation: The server sends emotion labels and prompts to the generative AI model, which then generates an appropriate response.

[1153] Step 6:

[1154] The server sends the generated response text to the terminal.

[1155] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[1156] Output: Response text sent to the terminal

[1157] Data processing: The response text is sent to the terminal using the HTTP protocol.

[1158] Specific behavior: The server includes the generated text response as the payload of an HTTPS request and sends it to the specified device endpoint.

[1159] Step 7:

[1160] The terminal converts the response text into voice data using speech synthesis technology and speaks it to the user.

[1161] Input: Response text Example: "That must have been lonely. I'm here, so please feel free to talk to me about anything."

[1162] Output: Audio data

[1163] Data processing: Convert text data into audio data using speech synthesis technology (e.g., Amazon Polly).

[1164] Specific operation: The device calls the speech synthesis API, converts the text data into speech data, and plays it back to the user through the speaker.

[1165] The above is a detailed flow of each processing step in this system, which allows users to receive friendly responses and reduce feelings of loneliness.

[1166] (Application example 2)

[1167] 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."

[1168] In modern brick-and-mortar stores, customer interaction is an important element. However, some customers find it difficult to express their emotions, and store clerks are often too busy to respond to each individual customer. This can result in customers being dissatisfied or not receiving appropriate service. The present invention solves these problems and realizes a system that provides emotional responses to customers.

[1169] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input from a user, means for analyzing the user's input data to determine the emotion, means for generating a response that induces the emotion based on the determined emotion, means for outputting the generated response as voice, and means for automatically generating an appropriate response according to the user's emotion and providing customer service in a physical store. This makes it possible to respond based on the individual emotions of customers.

[1170] A "user" is a person using the system.

[1171] "Input" refers to audio and text information provided by a user.

[1172] "Means" refer to the methods or techniques used to achieve a certain goal.

[1173] "Data" refers to text and audio information collected based on user input.

[1174] "Analysis" is the process of examining data in detail and extracting meaning and sentiment.

[1175] "Emotion" refers to the psychological state that a user is feeling.

[1176] "Judgment" is the process of drawing conclusions from analyzed data.

[1177] A "response" is a response generated by the system in response to a user's input.

[1178] "Generation" refers to the process by which a system creates new data or information.

[1179] "Voice" refers to vocal information produced by a user or a system.

[1180] "Output" refers to the process of providing generated data or responses to the user.

[1181] "Automatic generation" refers to the process by which a system generates a response without human intervention.

[1182] "Brick and mortar store" refers to a place that offers goods or services in a physical location.

[1183] "Customer service" refers to the process of providing service and guidance to customers who visit a physical store.

[1184] The present invention relates to a system for analyzing user emotions, generating appropriate responses, and providing customer service in a physical store. A specific embodiment for realizing this system is described below.

[1185] First, the system receives input from the user (customer). The input can be provided by voice or text. This process uses a microphone for voice input and speech recognition technology. The speech_recognition library is a suitable software for this.

[1186] Next, the user's input data is sent from the device to the server. The server receives this input data and analyzes it. Natural language processing technology is used for the analysis, and the user's emotions are analyzed and determined from the input data. A natural language processing library is used for this process.

[1187] The server then automatically generates a response based on the user's emotions based on the analysis results. The generative AI model uses the Transformers library and the GPT-2 model. This model is used to generate an appropriate response. For example, the following prompt sentence is input:

[1188] "Your customer is feeling tired. An appropriate response would be: Hello. Where are you from today? If you're tired, please rest on the couch here."

[1189] The generated response is sent from the server to the device, where it is converted into speech using speech synthesis technology using the pyttsx3 library, and the device then transmits the converted speech to the user through its speaker.

[1190] This process allows users to receive appropriate responses based on their emotions. For example, if a user says, "Today was a very difficult day," the system analyzes this input and determines the emotion "difficult." It then generates a response to the user, saying, "That must have been difficult. Let me know if there's anything I can do to help you," and delivers it to the user via voice.

[1191] The system of the present invention can dramatically improve customer service in brick-and-mortar stores by accurately analyzing the user's emotions and providing appropriate responses based on those emotions.

[1192] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1193] Step 1:

[1194] The user inputs voice into the device. The device recognizes the user's voice and converts the voice data into text data. At this time, the device uses the speech_recognition library. Voice input (voice data) is input and text data is output.

[1195] Step 2:

[1196] The terminal sends text data to the server. The server receives this text data and prepares it for analysis. The input is the text data sent from the terminal, and the output is the completed analysis preparation.

[1197] Step 3:

[1198] The server analyzes emotions based on text data. Here, natural language processing technology is used to extract meaning and emotions from the text. A natural language processing library is used for this process. The input is text data, and emotional data is obtained as the output.

[1199] Step 4:

[1200] The server generates an appropriate response based on the analyzed emotional data. This process uses a generative AI model using GPT-2. The generated prompt sentence includes the emotional data and is input to the generative model. The input is the emotional data and the prompt sentence, and the output is the generated response text.

[1201] Step 5:

[1202] The server sends the generated response text to the terminal, which converts the response text to speech. This process uses the pyttsx3 library. The input is the generated response text, and the output is speech data.

[1203] Step 6:

[1204] The terminal transmits the voice data to the user through a speaker, so that the user can hear the generated response. The input is voice data, and the output is the actual voice transmitted to the user.

[1205] This series of processing steps enables the system to accurately analyze the user's emotions and automatically generate an appropriate response in voice.

[1206] 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.

[1207] 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.

[1208] 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.

[1209] 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.

[1210] 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.

[1211] 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.

[1212] 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).

[1213] 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.

[1214] 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."

[1215] 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.

[1216] 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).

[1217] 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.

[1218] 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.

[1219] 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.

[1220] 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.

[1221] 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.

[1222] 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.

[1223] 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.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] The following is further disclosed regarding the above embodiment.

[1228] (Claim 1)

[1229] means for receiving input from a user;

[1230] A means for analyzing user input data to determine emotions;

[1231] means for generating an emotion-eliciting response based on the determined emotion;

[1232] means for outputting the generated response as speech;

[1233] A system including:

[1234] (Claim 2)

[1235] 10. The system of claim 1, wherein the means for determining sentiment uses natural language processing techniques to analyze the user input data.

[1236] (Claim 3)

[1237] 10. The system of claim 1, wherein the means for generating an emotion-eliciting response includes voice synthesis technology for outputting the generated response as speech.

[1238] "Example 1"

[1239] (Claim 1)

[1240] means for receiving voice input from a user;

[1241] means for converting voice input into text data;

[1242] A means for analyzing text data to determine emotions;

[1243] means for generating an appropriate response based on the determined emotion;

[1244] means for converting the generated response into voice data and outputting the voice data;

[1245] A system including:

[1246] (Claim 2)

[1247] 2. The system of claim 1, wherein the means for analyzing the text data to determine the emotion uses natural language processing techniques to analyze the text data and extract the emotion.

[1248] (Claim 3)

[1249] 10. The system of claim 1, wherein the means for generating an appropriate response uses a generative AI model to generate a response based on the user's emotions.

[1250] "Application Example 1"

[1251] (Claim 1)

[1252] means for receiving input from a user;

[1253] A means for analyzing user input data to determine emotions;

[1254] means for generating an emotion-eliciting response based on the determined emotion;

[1255] means for outputting the generated response as speech;

[1256] means including speech recognition technology for converting speech input into text data;

[1257] a server means including a sentiment analysis algorithm for analyzing sentiment from text data;

[1258] means including speech synthesis technology for converting the generated response into speech data;

[1259] A system including:

[1260] (Claim 2)

[1261] 10. The system of claim 1, wherein the means for determining sentiment uses natural language processing techniques to analyze the user input data.

[1262] (Claim 3)

[1263] 10. The system of claim 1, wherein the means for generating an emotion-eliciting response includes voice synthesis technology for outputting the generated response as speech.

[1264] "Example 2: Combining Emotion Engines"

[1265] (Claim 1)

[1266] means for receiving voice input from a user;

[1267] means for converting voice input into text data;

[1268] a means for analyzing the text data to determine the user's emotions;

[1269] means for generating an appropriate response based on the determined emotion;

[1270] means for outputting the generated response as speech;

[1271] A system including:

[1272] (Claim 2)

[1273] 2. The system according to claim 1, wherein the means for determining the user's emotions analyzes the text data using natural language processing technology to analyze the emotions.

[1274] (Claim 3)

[1275] 10. The system of claim 1, wherein the means for generating an appropriate response uses speech synthesis technology to output the generated response as speech.

[1276] "Application example 2 when combining emotion engines"

[1277] (Claim 1)

[1278] means for receiving input from a user;

[1279] A means for analyzing user input data to determine emotions;

[1280] means for generating an emotion-eliciting response based on the determined emotion;

[1281] means for outputting the generated response as speech;

[1282] A means for automatically generating an appropriate response according to the user's emotions and providing customer service in a physical store;

[1283] A system including:

[1284] (Claim 2)

[1285] 10. The system of claim 1, wherein the means for determining sentiment uses natural language processing techniques to analyze the user input data.

[1286] (Claim 3)

[1287] 10. The system of claim 1, wherein the means for generating an emotion-eliciting response includes voice synthesis technology for outputting the generated response as speech. [Explanation of symbols]

[1288] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving input from a user; A means for analyzing user input data to determine emotions; means for generating an emotion-eliciting response based on the determined emotion; means for outputting the generated response as speech; A system including:

2. 10. The system of claim 1, wherein the means for determining sentiment uses natural language processing techniques to analyze the user input data.

3. 10. The system of claim 1, wherein the means for generating an emotion-eliciting response includes voice synthesis technology for outputting the generated response as speech.

Images