system

Abstract

Provide a system. A telephone communication means; a voice input means for collecting a user's voice; a speech recognition means for converting collected speech into text; emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the emotional state of the user; a response generating means for generating an appropriate response based on the emotional state of the user estimated by the emotion analyzing means; a speech synthesis means for converting the generated response into speech form; a voice output means for reproducing the voice response generated by the voice synthesis means to the user; emergency detection means for detecting an emergency; an emergency response means for automatically notifying a specialized agency when the emergency detection means determines that an emergency has occurred; A system including:

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] There are many suicides in Japan every year, and many of these are caused by being isolated at night and having no one to talk to. It is also difficult to secure counselors at night, meaning that adequate support cannot be provided. Furthermore, the COVID-19 pandemic is causing an increase in the number of people facing economic hardship, and with the rise in unemployment in the future, the risk of suicide is expected to increase further. In response to these circumstances, there is a need for a system that can provide 24-hour support, quickly and appropriately. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a telephone communication means, a voice input means for collecting a user's voice, a voice recognition means for converting the collected voice into text, an emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and inferring the user's emotional state, a response generation means for generating an appropriate response based on the user's emotional state inferred by the emotion analysis means, a voice synthesis means for converting the generated response into a voice format, a voice output means for playing back to the user the voice response generated by the voice synthesis means, an emergency detection means for detecting an emergency, and an emergency response means for automatically notifying a specialist institution when the emergency detection means determines that an emergency has occurred.

[0006] "Telephone communication means" refers to a means by which a user can call a dedicated consultation number via a telephone line and engage in two-way communication with the system.

[0007] The "voice input means" is a means for receiving the user's speech and collecting the speech data.

[0008] The "voice recognition means" is a means for converting voice data into text data.

[0009] The "emotion analysis means" is a means for analyzing text data and estimating the user's emotional state (for example, sadness, anger, despair, etc.).

[0010] The "response generation means" is a means for generating an appropriate response content based on the estimated emotional state.

[0011] The "voice synthesis means" is a means for converting the generated response content into voice format.

[0012] The "audio output means" is a means for reproducing the audio response generated by the audio synthesis means to the user.

[0013] The "emergency detection means" is a means for detecting an emergency based on the content of a user's speech and emotional state.

[0014] The "emergency response means" is a means for automatically notifying a specialized agency when an emergency is determined by the emergency detection means. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is implemented by a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[0037] System Overview

[0038] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using speech recognition means. The text data is analyzed for the user's emotions using emotion analysis means, and an appropriate response is generated by generative AI. The generated response is converted into voice using speech synthesis means and played back to the user. Furthermore, if the user's comments or emotional state indicate an emergency, the server notifies specialized agencies.

[0039] Natural language explanation of program processing

[0040] Users Place Calls

[0041] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[0042] Playing a welcome message

[0043] The server automatically plays an IVR message such as, "Welcome to the suicide prevention consultation center. Please tell us your concerns."

[0044] User input of consultation content

[0045] The user speaks about their worries. For example, the user might say, "My work isn't going well, and life is hard."

[0046] Receives the user's voice data

[0047] The server collects the user's speech and obtains it as voice data in real time.

[0048] Speech to text

[0049] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[0050] Emotion analysis using emotion analysis tools

[0051] The server sends the text data to the emotion analysis engine, which analyzes the user's emotions. The emotion generation engine then reads the emotion "despair" from the text.

[0052] Appropriate response generation by response generation means

[0053] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[0054] Response synthesis and playback

[0055] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0056] Continue or end the user interaction

[0057] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[0058] Emergency detection and response

[0059] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[0060] The above is an embodiment of the present invention. To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer the call. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and convey it to the user via voice. If the situation is deemed an emergency, the system will immediately notify a specialist institution. In this way, the present invention provides 24-hour support for users' mental anxieties.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user calls the consultation number using a smartphone or landline.

[0064] Step 2:

[0065] The server receives the call and establishes the connection.

[0066] Step 3:

[0067] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[0068] Step 4:

[0069] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[0070] Step 5:

[0071] The server collects the user's voice data in real time.

[0072] Step 6:

[0073] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[0074] Step 7:

[0075] The server transmits the text data to the emotion analysis means.

[0076] Step 8:

[0077] A sentiment analysis means analyzes the text data and estimates the user's emotional state (e.g., despair, sadness).

[0078] Step 9:

[0079] The server sends the emotion analysis results to the generative AI.

[0080] Step 10:

[0081] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[0082] Step 11:

[0083] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[0084] Step 12:

[0085] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[0086] Step 13:

[0087] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[0088] Step 14:

[0089] The server collects the audio data again and repeats the process from step 5 to step 12.

[0090] Step 15:

[0091] The server detects emergencies based on the user's speech and emotional state.

[0092] Step 16:

[0093] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[0094] Step 17:

[0095] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[0096] Example 1

[0097] 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."

[0098] In today's world, many people are experiencing stress and worries in their daily lives and at work, and this is increasingly leading to mental health problems. Conventional systems have limitations in providing prompt and appropriate responses to these problems. In particular, it has been difficult to notify specialized agencies in a timely manner for users at risk of suicide or experiencing serious mental difficulties.

[0099] 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.

[0100] In this invention, the server includes telephone communication means, voice input means for collecting a user's voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state, response generation means for generating an appropriate response based on the user's emotional state estimated by the emotion analysis means, voice synthesis means for converting the generated response into voice format, voice output means for playing back to the user the voice response generated by the voice synthesis means, means for collecting user utterances in real time and acquiring them as voice data, means for analyzing the voice data in real time and generating an appropriate response, means for verifying the generated response before playing it back, emergency detection means for detecting an emergency, and emergency response means for automatically notifying a specialist institution if the emergency detection means determines that an emergency has occurred, thereby enabling a prompt and appropriate response to the user's mental health problem.

[0101] "Telephone communication means" refers to the means by which users connect to the system using smartphones or landlines.

[0102] The "voice input means" refers to a device or program for collecting user utterances as voice data.

[0103] "Speech recognition means" refers to a technology or program for converting collected voice data into text data.

[0104] "Emotion analysis means" refers to analytical techniques or programs for estimating a user's emotional state from text data.

[0105] The "response generation means" refers to a technique or program for generating an appropriate response based on the estimated emotional state of the user.

[0106] A "speech synthesis means" is a technology or program that converts the generated text response into speech form.

[0107] The "audio output means" refers to a device or program for playing back the generated audio response to the user.

[0108] The "means for acquiring as voice data" is a means for collecting user utterances in real time and saving them in voice data format.

[0109] "Means for analyzing in real time" refers to technology or programs for analyzing collected voice data in real time and generating a response.

[0110] The "means for verifying" is a means for checking the content of the generated response to ensure that it is appropriate before playing it back.

[0111] "Emergency detection means" refers to technology or programs for detecting an emergency from the content of a user's statements and emotional state.

[0112] "Emergency response tools" are technologies or programs that automatically notify specialized agencies in response to a detected emergency.

[0113] The present invention is embodied in a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[0114] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays a welcome message to the user using an interactive voice response system (IVR), while identifying the user's phone number and establishing a line to accept the call.

[0115] The initial message played by the server is something like, "Welcome to the Suicide Prevention Counseling Center. Please tell us about your worries." This is achieved by loading and playing an audio file from the automated voice response system. When a user speaks about their worries, the audio is collected using a microphone and temporarily saved as digital audio data. For example, a user might say, "My work isn't going well, and life is hard."

[0116] Next, the server uses a speech recognition engine such as Google® Cloud Speech-to-Text API to convert the collected voice data into text data. For example, a statement such as "My work isn't going well, and life is hard" is converted into text. This text data is sent to the Microsoft® Azure® Text Analytics sentiment analysis engine, which analyzes the user's emotions. The sentiment analysis engine then reads the emotion "despair" from the text.

[0117] Based on the results of the sentiment analysis, the server generates an appropriate response using a generative AI such as an OpenAI (registered trademark) model. For example, it generates a response such as, "That must be a very painful experience. I understand how you feel." This response generation process includes embedding the user's statement and the sentiment analysis results into a prompt sentence and sending it to the AI ​​model. An example of a prompt sentence to input to the generative AI model is, "The user said, 'I've been having a lot of failures at work recently, and life is hard.' Please generate a sympathetic and supportive response for this user."

[0118] The server converts the generated text response into speech using a speech synthesis engine such as Amazon Polly and plays it back to the user, so that if the user wants to continue speaking, the system can collect the audio again and repeat the process of generating a response.

[0119] The system also detects and responds to emergencies. The server determines whether an emergency has occurred based on the user's emotional state and the content of their comments, and automatically contacts specialized agencies if necessary. For example, if a user makes a comment indicating a risk of suicide, the server will automatically notify the emergency response team, enabling a more rapid response.

[0120] With the above configuration, the present invention can provide a prompt and appropriate response to a user's mental health problems.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1:

[0123] A user calls a specific consultation number using a smartphone or landline.

[0124] Input: User's phone number, call start signal

[0125] Output: Call connection establishment signal

[0126] What happens: The server identifies the user's phone number and establishes a line to accept the call.

[0127] Step 2:

[0128] The server plays a welcome message using an interactive voice response system (IVR).

[0129] Input: Call connection establishment signal

[0130] Output: Initial message sent

[0131] Specific operation: The server plays an IVR message such as "Welcome to the suicide prevention consultation center. Please tell us your concerns," so it loads an audio file in the IVR system and plays it to the user.

[0132] Step 3:

[0133] The user speaks out their concerns.

[0134] Input: User's voice data

[0135] Output: Collected audio data

[0136] Specific operation: A microphone collects the user's speech and temporarily stores it as digital audio data. For example, the user might say, "My work isn't going well, and life is hard."

[0137] Step 4:

[0138] The server captures the user's voice utterances in real time.

[0139] Input: Collected audio data

[0140] Output: Temporarily saved audio data

[0141] Specific operation: The collected voice data is stored on the server in a specific format (e.g., WAV format) for subsequent voice recognition processing.

[0142] Step 5:

[0143] The server uses a speech recognition engine to convert the collected voice data into text data.

[0144] Input: Temporarily saved audio data

[0145] Output: Text data

[0146] Specific operation: Voice data is sent to a speech recognition engine such as Google Cloud Speech-to-Text API, and the server retrieves the returned text data. For example, it may obtain text data such as "My work is not going well, and life is hard."

[0147] Step 6:

[0148] The server sends the text data to an emotion analysis engine to analyze the user's emotions.

[0149] Input: Text data

[0150] Output: Emotion analysis data (e.g., despair)

[0151] Specific operation: Text data is sent to the Microsoft Azure Text Analytics sentiment analysis engine, and the server retrieves the returned sentiment data (e.g., "despair").

[0152] Step 7:

[0153] The server uses generative AI to generate a response appropriate for the user based on the results of emotion analysis.

[0154] Input: Sentiment analysis data, text data

[0155] Output: Response text data

[0156] Specific operation: The results of the emotion analysis and the user's statement are embedded in a prompt, which is then sent to a generative AI model to generate a response. For example, the model can generate a response such as, "That must be a very painful experience. I understand how you feel."

[0157] Step 8:

[0158] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0159] Input: Response text data

[0160] Output: Audio data

[0161] Specific operation: Sends the response text data to a speech synthesis engine such as Amazon Polly, and plays the returned audio data to the user.

[0162] Step 9:

[0163] If the user wishes to continue speaking, the system repeats the process from voice collection to response generation.

[0164] Input: The user's new voice data

[0165] Output: New collected voice data, response data

[0166] Specific operation: Repeatedly executes steps from collecting new voice to generating a response, continuing the dialogue with the user.

[0167] Step 10:

[0168] The server detects emergencies and automatically notifies specialist agencies if necessary.

[0169] Input: User sentiment analysis data or statements

[0170] Output: Emergency notification signal

[0171] What it does: If a user makes a statement that indicates a risk of suicide, it automatically notifies emergency response teams.

[0172] (Application example 1)

[0173] 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."

[0174] In modern society, it is important for individuals facing emergencies to receive prompt and appropriate responses. However, conventional systems that rely only on telephones or voice input are unable to adequately analyze emotions or detect emergencies, making it difficult to provide individuals with appropriate responses or emergency measures. For this reason, there is a demand for systems that can respond promptly and appropriately to users' concerns and emergencies.

[0175] 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.

[0176] In this invention, the server includes a telephone communication means, a voice input means for collecting a user's voice, a voice recognition means for converting the collected voice into text, a feeling analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, a response generation means for generating an appropriate response based on the estimated user's emotional state, a voice synthesis means for converting the generated response into a voice format, a voice output means for playing the generated voice response to the user, an emergency detection means for detecting an emergency, an emergency response means for automatically notifying a specialist institution when an emergency is determined, and a means for operating as a device including a program for processing information obtained from the voice input and generating an appropriate response. This makes it possible to analyze the user's emotional state and provide a prompt and appropriate response, as well as to respond quickly to emergencies by automatically notifying a specialist institution.

[0177] "Telephone communication means" is a general term for devices and methods for making voice calls using telephone lines.

[0178] "Voice input means" refers to hardware or software for capturing the user's voice and converting it into digital data.

[0179] "Speech recognition means" is a technology that analyzes collected voice data and converts it into text data.

[0180] "Emotion analysis means" refers to algorithms and technologies for estimating a user's emotional state based on text data.

[0181] A "response generation means" refers to a technology or system for generating an appropriate response based on the results of sentiment analysis.

[0182] A "speech synthesis means" is a technology that converts the generated text response into speech form.

[0183] The "audio output means" refers to a device or technology for playing back the voice generated by the voice synthesis means to the user.

[0184] "Emergency detection means" refers to systems or technologies for detecting emergencies from the content of user comments and emotional state.

[0185] "Emergency response measures" refer to methods or systems that automatically notify specialized agencies when an emergency is deemed to have occurred.

[0186] "Means operating as a device containing a program" means a system or apparatus that allows a device containing a program to process information obtained from voice input and generate an appropriate response to perform the function of the device.

[0187] This invention relates to a system in which, when a user calls a specific consultation number using a smartphone or landline, a server receives the call and analyzes the user's emotions and emergency situations from the voice. This system uses voice recognition and emotion analysis to understand the user's concerns, generate an appropriate response, and, if necessary, take emergency action.

[0188] First, when a user calls the dedicated consultation number using a smartphone or landline, the server receives the call and establishes a connection. The server automatically plays an opening message such as "Welcome to the consultation center. Please tell us about your concerns." When the user speaks about their concerns, the audio is collected by the server.

[0189] The collected voice is converted into text data by a speech recognition means. This speech recognition uses the widely used "speech_recognition" library. The converted text data is sent to an emotion analysis means, where the user's emotions are analyzed. This emotion analysis uses the "EmotionAnalyzer" API.

[0190] Based on the results of the sentiment analysis, the server generates an appropriate response using the response generation means. This response generation uses the "TextGenerator" API. The generated response is converted into audio format by the speech synthesis means and played back to the user. This speech synthesis uses the "pyttsx3" library.

[0191] Furthermore, an emergency detection mechanism is implemented to detect emergencies from the user's speech and emotional state. If an emergency is detected, the server automatically notifies specialized agencies using emergency response mechanisms. This notification is performed using the "EmergencyNotifier" class.

[0192] For example, if a user says, "I've been having a lot of failures at work lately, and life is tough," the system will analyze the statement and generate an appropriate response, such as, "That must be a very tough experience. I understand how you feel." Similarly, if a user says, "I'm scared someone is breaking into my house right now," the system will immediately notify specialized authorities using emergency response methods.

[0193] An example of a prompt sentence is: "User: 'I'm scared that someone is breaking into my house. What should I do?' System response: 'Please move to the nearest safe place immediately and call the police. I will also call emergency services.'"

[0194] The above is an embodiment of the present invention. This system can provide safety and a sense of security by quickly and appropriately responding to user concerns and emergencies.

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] The user calls the consultation number using a smartphone or landline.

[0198] Input: User's voice data

[0199] Output: Connected call session

[0200] Specific operation: When a user makes a call, the server receives the call and establishes a call connection.

[0201] Step 2:

[0202] The server plays a welcome message.

[0203] Input: Connected call session

[0204] Output: The initial message that is played

[0205] Specific operation: The server plays an automated voice message such as "Welcome to the consultation center. Please tell us your concerns."

[0206] Step 3:

[0207] The user speaks out their concerns.

[0208] Input: Voice input by the user after receiving the initial message from the server

[0209] Output: User's voice data

[0210] Specific operation: The user speaks about their concerns into the microphone, and the voice data is collected by the server.

[0211] Step 4:

[0212] The server collects the voice data and converts it into text using a voice recognition means.

[0213] Input: User's voice data

[0214] Output: Text data

[0215] Specific operation: The server uses the "speech_recognition" library to convert the user's voice data into text format.

[0216] Step 5:

[0217] The server analyzes the text data using emotion analysis means.

[0218] Input: Text data

[0219] Output: Sentiment analysis result data

[0220] Specific operation: The server uses the "EmotionAnalyzer" API to analyze text data and estimate the user's emotional state.

[0221] Step 6:

[0222] The server generates a response based on the sentiment analysis results.

[0223] Input: Sentiment analysis result data

[0224] Output: The generated response text

[0225] Specific operation: The server uses the "TextGenerator" API to generate a response appropriate for the user based on the results of sentiment analysis.

[0226] Step 7:

[0227] The server converts the generated response into speech using a speech synthesis means.

[0228] Input: Generated response text

[0229] Output: Audio data

[0230] What it does: The server uses the "pyttsx3" library to convert the generated text response into audio format.

[0231] Step 8:

[0232] The server plays the audio data to the user.

[0233] Input: Audio data

[0234] Output: The audio message the user hears

[0235] What happens: The server streams the audio data to the user's phone and plays it back.

[0236] Step 9:

[0237] The server detects emergencies based on the user's speech and emotional state.

[0238] Input: Sentiment analysis results and speech content

[0239] Output: Emergency detection result

[0240] Specific operation: The server uses the emergency detection means to analyze the user's comments and emotional state and determine whether or not an emergency has occurred.

[0241] Step 10:

[0242] If the server is determined to be in an emergency, emergency response measures will be used to automatically notify specialized agencies.

[0243] Input: Emergency detection result

[0244] Output: Automatic notification message

[0245] Specific operation: The server uses the "EmergencyNotifier" class to automatically send a notification to specialized agencies when an emergency response is required.

[0246] This series of processes allows users to receive a prompt and appropriate response simply by consulting using a smartphone or landline, and in the event of an emergency, appropriate emergency response will be provided.

[0247] 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.

[0248] The present invention is embodied by a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine that recognizes the emotions of a user.

[0249] System Overview

[0250] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using a speech recognition system. The text data is analyzed for the user's emotions using an emotion analysis system and an emotion engine, and an appropriate response is generated by a generative AI system. The generated response is converted into speech using a speech synthesis system and played back to the user. Furthermore, if the content of the user's comments or emotional state indicates an emergency, the server notifies a specialist agency. The emotion engine also works in cooperation with the emotion analysis system to more accurately recognize emotions by referring not only to the text data but also to the tone and speed of the voice and past consultation history, thereby optimizing the response.

[0251] Natural language explanation of program processing

[0252] Users Place Calls

[0253] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[0254] Playing a welcome message

[0255] The server automatically plays an IVR message saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[0256] User input of consultation content

[0257] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[0258] Receives the user's voice data

[0259] The server collects the user's speech and obtains it as voice data in real time.

[0260] Speech to text

[0261] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[0262] Emotion analysis using emotion analysis methods and emotion engines

[0263] The server sends both text and voice data to the emotion analysis means and emotion engine to analyze the user's emotions. The emotion engine analyzes not only the text data but also the tone and speed of the voice to improve the accuracy of emotion recognition. It also references past consultation history to optimize responses.

[0264] Appropriate response generation by response generation means

[0265] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[0266] Response synthesis and playback

[0267] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0268] Continue or end the user interaction

[0269] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[0270] Emergency detection and response

[0271] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[0272] To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and deliver it to the user via voice. The emotion engine will also analyze the tone and speed of the user's voice to provide a more accurate response. If it is determined to be an emergency, the system will immediately notify a specialist agency. In this way, the present invention provides 24-hour support for users' mental anxieties.

[0273] The processing flow will be explained below.

[0274] Step 1:

[0275] The user calls the consultation number using a smartphone or landline.

[0276] Step 2:

[0277] The server receives the call and establishes the connection.

[0278] Step 3:

[0279] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[0280] Step 4:

[0281] The user talks about their worries over the phone. For example, they might say, "My work isn't going well, and life is hard."

[0282] Step 5:

[0283] The server collects the user's voice data in real time.

[0284] Step 6:

[0285] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[0286] Step 7:

[0287] The server sends the text data to the emotion analysis means and the emotion engine.

[0288] Step 8:

[0289] An emotion analyzer and emotion engine analyzes the text and audio data (including tone and rate) to infer the user's emotional state (e.g., despair, sadness).

[0290] Step 9:

[0291] The emotion engine refers to past consultation history to recognize emotional states and optimize responses.

[0292] Step 10:

[0293] The server sends the emotion analysis results to the generative AI.

[0294] Step 11:

[0295] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[0296] Step 12:

[0297] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[0298] Step 13:

[0299] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[0300] Step 14:

[0301] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[0302] Step 15:

[0303] The server collects the audio data again and repeats the process from step 5 to step 13.

[0304] Step 16:

[0305] The server detects emergencies based on the user's speech and emotional state.

[0306] Step 17:

[0307] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[0308] Step 18:

[0309] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[0310] Example 2

[0311] 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."

[0312] In modern society, the number of people suffering from mental distress is increasing, and in particularly serious cases, many people consider suicide. It is important to provide a system that can respond quickly and appropriately to such emergencies. However, conventional consultation systems often have difficulty accurately grasping the user's emotional state and are unable to generate an appropriate response. They also lack the functionality to detect emergencies and notify specialized institutions. This presents a challenge in that they are unable to effectively support users with their concerns.

[0313] 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 a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine. This enables highly accurate emotion recognition by collecting the user's voice in real time, converting it into text by voice recognition, and analyzing the text and the tone and speed of the voice. Furthermore, the emotion engine can also refer to past consultation history to improve the appropriateness of the response. Furthermore, by detecting an emergency and automatically notifying a specialized institution, the user's safety can be quickly ensured.

[0314] "Telephone communication means" is a general term for hardware and software for making voice calls, and is compatible with both mobile terminals and landlines.

[0315] "Audio input means" refers to a microphone and associated hardware and software for collecting the user's voice.

[0316] "Speech recognition means" refers to technology or devices for converting collected voice data into text, and includes, for example, a voice recognition engine.

[0317] The "emotion analysis means" refers to a technique or device for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state.

[0318] The "response generation means" refers to a technique or device for generating an appropriate response based on the emotional state of the user estimated by the emotion analysis means, and includes generative artificial intelligence.

[0319] A "voice synthesis means" is a technique or device for converting a generated response into voice form.

[0320] "Audio output means" refers to a speaker and associated hardware and software for playing back to the user the audio response generated by the speech synthesis means.

[0321] "Emergency detection means" refers to technology or devices for determining an emergency from the user's statements and emotional state.

[0322] "Emergency response means" refers to technology or devices that automatically notify specialized agencies when an emergency is determined by the emergency detection means.

[0323] An "emotion engine" is a technology or device that analyzes the tone and speed of speech to improve the accuracy of emotion analysis, and also references past consultation history.

[0324] The present invention is embodied in a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, an emotion engine, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means.

[0325] First, when a user calls a specific consultation number using a smartphone or landline, the server receives the call and establishes a connection. The telephone communication method used here is a general term for the hardware and software used to make voice calls, and is compatible with both mobile devices and landlines.

[0326] Next, the server automatically plays an initial message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us about your concerns." This is done using a voice output means.

[0327] When a user speaks about their concerns, the voice is collected in real time by the server via a voice input means, i.e., a microphone and associated hardware and software.

[0328] The voice data collected by the server is converted into text data using a voice recognition tool (e.g., Google Cloud Speech-to-Text). The voice recognition tool here refers to technology or equipment for converting voice data into text.

[0329] Next, the server sends both the text data and the voice data to an emotion analysis means (e.g., IBM Watson (registered trademark) Tone Analyzer) and an emotion engine to analyze the user's emotions. The emotion analysis means refers to technology or equipment that analyzes the consultation content converted into text by the voice recognition means and estimates the user's emotional state. The emotion engine analyzes the tone and speed of the voice and also refers to past consultation history to improve the accuracy of emotion recognition.

[0330] Once the sentiment analysis is complete, the server generates an appropriate response using generative AI (e.g., OpenAI GPT-4 (registered trademark)). The response generation means is a technology or device for generating an appropriate response based on the sentiment analysis results. The generated response is converted into speech using a speech synthesis means (e.g., Amazon Polly). The speech synthesis means is a technology or device for converting the generated response into speech format.

[0331] The server then plays back the generated voice response. If the user wants to continue speaking, the system collects the voice again and repeats the same process. If an emergency is detected, the server automatically notifies a specialist agency. Emergency detection means are technologies or devices that determine an emergency from the user's speech or emotional state, and emergency response means are technologies or devices that automatically notify a specialist agency when an emergency is determined by the emergency detection means.

[0332] As a concrete example, consider the case where a user calls a dedicated consultation number from their smartphone late at night. The system automatically answers the call, and if the user says, "I've been having a lot of failures at work lately, and life is hard," the server analyzes the statement and generates an appropriate response, "That must be a very painful experience. I understand how you feel," which is conveyed to the user via voice. Furthermore, the emotion engine analyzes the tone and speed of the user's voice to provide a more accurate response. If the call is deemed an emergency, specialized agencies are immediately notified.

[0333] Examples of prompt sentences are as follows:

[0334] User: "I've been failing at work lately and life is hard."

[0335] Server: "That must be a very painful experience. I understand how you feel."

[0336] This allows the system of the present invention to provide support for users' mental anxieties 24 hours a day.

[0337] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0338] Step 1:

[0339] The user calls a specific consultation number using a smartphone or landline. The server receives the call and establishes a connection. The input is the user's call operation, and the output is the establishment of the call. At this time, the server initializes the communication protocol using the telephone communication means.

[0340] Step 2:

[0341] The server plays a welcome message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns." The input is the establishment of a telephone connection, and the output is the audio playback of the welcome message. The server plays a pre-recorded message from the audio output means.

[0342] Step 3:

[0343] The user talks about their worries over the phone, for example, "I've been failing at work a lot lately, and life is hard." The input is the user's voice, and the output is passed to the server as voice data. At this stage, the voice input means captures what the user is saying.

[0344] Step 4:

[0345] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text) to convert the collected voice data into text data. The input is voice data, and the output is text data. The server performs the text conversion of the voice data through an API call.

[0346] Step 5:

[0347] The server sends both text and voice data to an emotion analysis tool (e.g., IBM Watson Tone Analyzer) and an emotion engine to analyze the user's emotions. The input is text and voice data, and the output is data indicating the user's emotional state. The emotion engine improves the accuracy of the analysis by referring to the tone and speed of the voice and past consultation history.

[0348] Step 6:

[0349] The server uses a generative AI (e.g., OpenAI GPT-4) to generate an appropriate response based on the results of emotion analysis. The input is data indicating the emotional state, and the output is a response text. An API is used to send prompts to the generative AI and receive the response text.

[0350] Step 7:

[0351] The server converts the generated response text into speech using a speech synthesis engine (e.g., Amazon Polly) and plays it back to the user. The input is the response text and the output is audio data. The speech synthesis engine converts the text into audio format, which the server plays back.

[0352] Step 8:

[0353] If the user wants to continue speaking, the server collects the audio again and repeats the process to generate a new response: the input is new audio data, the output is a new response, and the system continues the loop.

[0354] Step 9:

[0355] The server determines an emergency based on the user's emotional state and speech content, and automatically notifies specialized organizations. The input is emotion analysis data, and the output is an emergency notification. The server makes a judgment using emergency detection means, and contacts specialized organizations via emergency response means.

[0356] (Application example 2)

[0357] 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."

[0358] Conventional customer support systems often struggle to provide prompt and appropriate responses when users have specific problems or questions. They also struggle to accurately grasp the user's emotional state and provide appropriate responses. Furthermore, it is difficult to quickly detect emergencies and respond appropriately, which prevents users from achieving satisfactory results or a sense of security.

[0359] 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 telephone communication means, voice input means for collecting user voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, response generation means for generating an appropriate response based on the estimated user's emotional state, voice synthesis means for converting the generated response into voice format, voice output means for playing the voice response to the user, emergency detection means for detecting an emergency, emergency response means for automatically notifying a specialist agency when an emergency is determined, means installed on a smart device for providing customer support through voice dialogue in a physical store, response generation means for generating appropriate responses to user questions, voice synthesis means for converting the responses into voice format, and means designed for use in a physical store. This makes it possible to quickly and appropriately respond to user problems and questions, accurately grasp the user's emotional state, provide appropriate responses, and quickly respond to emergencies.

[0360] "Telephone communication means" refers to a means by which a user communicates with a server via voice, and includes mobile terminals and landlines.

[0361] "Voice input means" refers to a device or function for collecting the user's spoken voice.

[0362] "Speech recognition means" refers to a technology or device that analyzes collected speech and converts it into text data.

[0363] "Emotion analysis means" refers to a technique or device for analyzing text data and voice data and estimating the user's emotional state.

[0364] A "response generator" is a system or device that generates an appropriate response based on the estimated emotional state and text data.

[0365] A "speech synthesis means" is a technique or device for converting a generated text response into speech.

[0366] The "audio output means" refers to a technique or device for playing back the voice generated by the voice synthesis means to the user.

[0367] The "emergency detection means" is a technology or device for determining an emergency based on the content of a user's speech or emotional state.

[0368] "Emergency response means" refers to a technology or device that automatically notifies a specialized agency when an emergency is determined by the emergency detection means.

[0369] The "means installed on the smart device" refers to an application or system that provides customer support in a physical store through voice interaction in response to user questions.

[0370] "Means designed for use in a physical store" refers to a system or application with various support functions intended for use in a physical store.

[0371] An embodiment of the present invention will be described.

[0372] System Overview

[0373] The server includes a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means. The system is installed on a smart device and provides customer support through voice dialogue in a physical store. The system is designed for use in a physical store.

[0374] Hardware used

[0375] Smartphone: Works with ANDROID(R) and iOS.

[0376] Server: Operated in a cloud or on-premise environment.

[0377] Software used

[0378] Speech recognition library: Google Cloud Speech-to-Text API

[0379] Sentiment analysis engine: IBM Watson Tone Analyzer

[0380] Response generation means: GPT-3 (registered trademark) (OpenAI)

[0381] Speech synthesis engine: Google Cloud Text-to-Speech API

[0382] Data processing and calculation

[0383] The operation of this system is configured as follows.

[0384] 1. User voice input

[0385] The user speaks their question or inquiry into their smartphone, for example, "Please tell me about the features of this product."

[0386] 2. Voice Recognition

[0387] Voice input is captured through the smartphone's microphone and converted to text data using the Google Cloud Speech-to-Text API.

[0388] 3. Emotion analysis

[0389] The resulting text data is sent to IBM Watson Tone Analyzer, which analyzes the user's emotional state and assigns emotion tags such as "excitement" or "interest."

[0390] 4. Response Generation

[0391] Based on the results of sentiment analysis and text data, GPT-3 generates an appropriate response. The following prompt sentence is input into the generative AI model:

[0392] User's query: "What are the features of this product?"

[0393] Detected emotion: "Excited"

[0394] Generate a suitable response for the user considering their emotional state.

[0395] 5. Speech Synthesis

[0396] The generated text response is converted to audio using the Google Cloud Text-to-Speech API.

[0397] 6. Audio Output

[0398] The synthesized response is played back through the smartphone's speaker and conveyed to the user.

[0399] Specific examples

[0400] scenario

[0401] If a user is using their smartphone in a physical store, opens the "Customer Support Concierge" application, and asks, "What are the features of this product?"

[0402] The server uses a speech recognition engine to convert this question into text,

[0403] Using a sentiment analysis engine, it detects when a user is feeling "excited" and

[0404] GPT-3 generates a response like, "This product is the latest model and has the following features..."

[0405] Finally, the speech synthesis engine converts the response into speech and responds to the user on their smartphone.

[0406] In this way, the "Customer Support Concierge" based on the technology of this invention combines voice recognition and emotion analysis to provide high-quality customer support in real time within the store. This allows for quick and appropriate responses to user problems and questions, while also accurately grasping the user's emotional state and providing an appropriate response. It also allows for quick response in emergencies. These functions allow users to use the service with peace of mind.

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] The user speaks their question or inquiry into the smartphone. For example, the user might say, "Please tell me about the features of this product." This voice input is captured by the smartphone's microphone.

[0410] Step 2:

[0411] The captured voice data is sent to the server, which then uses the Google Cloud Speech-to-Text API to convert the voice data into text. The input is voice data, and the output is text data. The specific operation of this conversion is to analyze the voice waveform and output the corresponding text.

[0412] Step 3:

[0413] The converted text data is sent to IBM Watson Tone Analyzer. The server uses this API to analyze the emotional state from the text data. The input is text data, and the output is an emotion tag. Specifically, it analyzes the content, context, and wording of the text, and assigns emotion tags such as "excitement," "interest," and "anxiety."

[0414] Step 4:

[0415] The server passes the results of the sentiment analysis and text data to GPT-3, which then generates an appropriate response. The input is the sentiment tag and text data, and the output is the response text. At this stage, the following prompt sentence is input to the generative AI model:

[0416] User's query: "What are the features of this product?"

[0417] Detected emotion: "Excited"

[0418] Generate a suitable response for the user considering their emotional state.

[0419] Specifically, GPT-3 generates the optimal response in natural language based on this prompt.

[0420] Step 5:

[0421] The server sends the generated response text to the Google Cloud Text-to-Speech API. The input is the response text, and the output is the audio data. Specifically, the API analyzes the text and synthesizes a natural-sounding voice corresponding to it.

[0422] Step 6:

[0423] The generated voice data is sent to the smartphone, which then plays the voice through its speaker, allowing the user to hear the appropriate response.

[0424] Step 7:

[0425] If the user has any further questions or inquiries, the process is repeated again from step 1.

[0426] This allows the system to respond to user problems and questions in real time, analyze their emotional state to provide appropriate responses, and take appropriate action in emergency situations.

[0427] 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.

[0428] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0429] 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.

[0430] [Second embodiment]

[0431] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] In the smart glasses 214, 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.

[0442] 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."

[0443] The present invention is implemented by a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[0444] System Overview

[0445] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using speech recognition means. The text data is analyzed for the user's emotions using emotion analysis means, and an appropriate response is generated by generative AI. The generated response is converted into voice using speech synthesis means and played back to the user. Furthermore, if the user's comments or emotional state indicate an emergency, the server notifies specialized agencies.

[0446] Natural language explanation of program processing

[0447] Users Place Calls

[0448] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[0449] Playing a welcome message

[0450] The server automatically plays an IVR message such as, "Welcome to the suicide prevention consultation center. Please tell us your concerns."

[0451] User input of consultation content

[0452] The user speaks about their worries. For example, the user might say, "My work isn't going well, and life is hard."

[0453] Receives the user's voice data

[0454] The server collects the user's speech and obtains it as voice data in real time.

[0455] Speech to text

[0456] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[0457] Emotion analysis using emotion analysis tools

[0458] The server sends the text data to the emotion analysis engine, which analyzes the user's emotions. The emotion generation engine then reads the emotion "despair" from the text.

[0459] Appropriate response generation by response generation means

[0460] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[0461] Response synthesis and playback

[0462] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0463] Continue or end the user interaction

[0464] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[0465] Emergency detection and response

[0466] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[0467] The above is an embodiment of the present invention. To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer the call. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and convey it to the user via voice. If the situation is deemed an emergency, the system will immediately notify a specialist institution. In this way, the present invention provides 24-hour support for users' mental anxieties.

[0468] The processing flow will be explained below.

[0469] Step 1:

[0470] The user calls the consultation number using a smartphone or landline.

[0471] Step 2:

[0472] The server receives the call and establishes the connection.

[0473] Step 3:

[0474] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[0475] Step 4:

[0476] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[0477] Step 5:

[0478] The server collects the user's voice data in real time.

[0479] Step 6:

[0480] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[0481] Step 7:

[0482] The server transmits the text data to the emotion analysis means.

[0483] Step 8:

[0484] A sentiment analysis means analyzes the text data and estimates the user's emotional state (e.g., despair, sadness).

[0485] Step 9:

[0486] The server sends the emotion analysis results to the generative AI.

[0487] Step 10:

[0488] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[0489] Step 11:

[0490] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[0491] Step 12:

[0492] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[0493] Step 13:

[0494] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[0495] Step 14:

[0496] The server collects the audio data again and repeats the process from step 5 to step 12.

[0497] Step 15:

[0498] The server detects emergencies based on the user's speech and emotional state.

[0499] Step 16:

[0500] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[0501] Step 17:

[0502] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[0503] Example 1

[0504] 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."

[0505] In today's world, many people are experiencing stress and worries in their daily lives and at work, and this is increasingly leading to mental health problems. Conventional systems have limitations in providing prompt and appropriate responses to these problems. In particular, it has been difficult to notify specialized agencies in a timely manner for users at risk of suicide or experiencing serious mental difficulties.

[0506] 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.

[0507] In this invention, the server includes telephone communication means, voice input means for collecting a user's voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state, response generation means for generating an appropriate response based on the user's emotional state estimated by the emotion analysis means, voice synthesis means for converting the generated response into voice format, voice output means for playing back to the user the voice response generated by the voice synthesis means, means for collecting user utterances in real time and acquiring them as voice data, means for analyzing the voice data in real time and generating an appropriate response, means for verifying the generated response before playing it back, emergency detection means for detecting an emergency, and emergency response means for automatically notifying a specialist institution if the emergency detection means determines that an emergency has occurred, thereby enabling a prompt and appropriate response to the user's mental health problem.

[0508] "Telephone communication means" refers to the means by which users connect to the system using smartphones or landlines.

[0509] The "voice input means" refers to a device or program for collecting user utterances as voice data.

[0510] "Speech recognition means" refers to a technology or program for converting collected voice data into text data.

[0511] "Emotion analysis means" refers to analytical techniques or programs for estimating a user's emotional state from text data.

[0512] The "response generation means" refers to a technique or program for generating an appropriate response based on the estimated emotional state of the user.

[0513] A "speech synthesis means" is a technology or program that converts the generated text response into speech form.

[0514] The "audio output means" refers to a device or program for playing back the generated audio response to the user.

[0515] The "means for acquiring as voice data" is a means for collecting user utterances in real time and saving them in voice data format.

[0516] "Means for analyzing in real time" refers to technology or programs for analyzing collected voice data in real time and generating a response.

[0517] The "means for verifying" is a means for checking the content of the generated response to ensure that it is appropriate before playing it back.

[0518] "Emergency detection means" refers to technology or programs for detecting an emergency from the content of a user's statements and emotional state.

[0519] "Emergency response tools" are technologies or programs that automatically notify specialized agencies in response to a detected emergency.

[0520] The present invention is embodied in a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[0521] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays a welcome message to the user using an interactive voice response system (IVR), while identifying the user's phone number and establishing a line to accept the call.

[0522] The initial message played by the server is something like, "Welcome to the Suicide Prevention Counseling Center. Please tell us about your worries." This is achieved by loading and playing an audio file from the automated voice response system. When a user speaks about their worries, the audio is collected using a microphone and temporarily saved as digital audio data. For example, a user might say, "My work isn't going well, and life is hard."

[0523] Next, the server uses a speech recognition engine such as Google Cloud Speech-to-Text API to convert the collected voice data into text data. For example, a statement such as "My job isn't going well, and life is hard" is converted into text. This text data is sent to the Microsoft Azure Text Analytics sentiment analysis engine, which analyzes the user's emotions. The sentiment analysis engine then identifies the emotion "despair" from the text.

[0524] Based on the results of the sentiment analysis, the server generates an appropriate response using a generative AI such as an OpenAI model. For example, it generates a response such as, "That must be a very painful experience. I understand how you feel." This response generation process includes embedding the user's statement and the sentiment analysis results into a prompt sentence and sending it to the AI ​​model. An example of a prompt sentence to input to the generative AI model is, "The user said, 'I've been having a lot of failures at work recently, and life is hard.' Please generate a sympathetic and supportive response for this user."

[0525] The server converts the generated text response into speech using a speech synthesis engine such as Amazon Polly and plays it back to the user, so that if the user wants to continue speaking, the system can collect the audio again and repeat the process of generating a response.

[0526] The system also detects and responds to emergencies. The server determines whether an emergency has occurred based on the user's emotional state and the content of their comments, and automatically contacts specialized agencies if necessary. For example, if a user makes a comment indicating a risk of suicide, the server will automatically notify the emergency response team, enabling a more rapid response.

[0527] With the above configuration, the present invention can provide a prompt and appropriate response to a user's mental health problems.

[0528] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0529] Step 1:

[0530] A user calls a specific consultation number using a smartphone or landline.

[0531] Input: User's phone number, call start signal

[0532] Output: Call connection establishment signal

[0533] What happens: The server identifies the user's phone number and establishes a line to accept the call.

[0534] Step 2:

[0535] The server plays a welcome message using an interactive voice response system (IVR).

[0536] Input: Call connection establishment signal

[0537] Output: Initial message sent

[0538] Specific operation: The server plays an IVR message such as "Welcome to the suicide prevention consultation center. Please tell us your concerns," so it loads an audio file in the IVR system and plays it to the user.

[0539] Step 3:

[0540] The user speaks out their concerns.

[0541] Input: User's voice data

[0542] Output: Collected audio data

[0543] Specific operation: A microphone collects the user's speech and temporarily stores it as digital audio data. For example, the user might say, "My work isn't going well, and life is hard."

[0544] Step 4:

[0545] The server captures the user's voice utterances in real time.

[0546] Input: Collected audio data

[0547] Output: Temporarily saved audio data

[0548] Specific operation: The collected voice data is stored on the server in a specific format (e.g., WAV format) for subsequent voice recognition processing.

[0549] Step 5:

[0550] The server uses a speech recognition engine to convert the collected voice data into text data.

[0551] Input: Temporarily saved audio data

[0552] Output: Text data

[0553] Specific operation: Voice data is sent to a speech recognition engine such as Google Cloud Speech-to-Text API, and the server retrieves the returned text data. For example, it may obtain text data such as "My work is not going well, and life is hard."

[0554] Step 6:

[0555] The server sends the text data to an emotion analysis engine to analyze the user's emotions.

[0556] Input: Text data

[0557] Output: Emotion analysis data (e.g., despair)

[0558] Specific operation: Text data is sent to the Microsoft Azure Text Analytics sentiment analysis engine, and the server retrieves the returned sentiment data (e.g., "despair").

[0559] Step 7:

[0560] The server uses generative AI to generate a response appropriate for the user based on the results of emotion analysis.

[0561] Input: Sentiment analysis data, text data

[0562] Output: Response text data

[0563] Specific operation: The results of the emotion analysis and the user's statement are embedded in a prompt, which is then sent to a generative AI model to generate a response. For example, the model can generate a response such as, "That must be a very painful experience. I understand how you feel."

[0564] Step 8:

[0565] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0566] Input: Response text data

[0567] Output: Audio data

[0568] Specific operation: Sends the response text data to a speech synthesis engine such as Amazon Polly, and plays the returned audio data to the user.

[0569] Step 9:

[0570] If the user wishes to continue speaking, the system repeats the process from voice collection to response generation.

[0571] Input: The user's new voice data

[0572] Output: New collected voice data, response data

[0573] Specific operation: Repeatedly executes steps from collecting new voice to generating a response, continuing the dialogue with the user.

[0574] Step 10:

[0575] The server detects emergencies and automatically notifies specialist agencies if necessary.

[0576] Input: User sentiment analysis data or statements

[0577] Output: Emergency notification signal

[0578] What it does: If a user makes a statement that indicates a risk of suicide, it automatically notifies emergency response teams.

[0579] (Application example 1)

[0580] 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."

[0581] In modern society, it is important for individuals facing emergencies to receive prompt and appropriate responses. However, conventional systems that rely only on telephones or voice input are unable to adequately analyze emotions or detect emergencies, making it difficult to provide individuals with appropriate responses or emergency measures. For this reason, there is a demand for systems that can respond promptly and appropriately to users' concerns and emergencies.

[0582] 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.

[0583] In this invention, the server includes a telephone communication means, a voice input means for collecting a user's voice, a voice recognition means for converting the collected voice into text, a feeling analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, a response generation means for generating an appropriate response based on the estimated user's emotional state, a voice synthesis means for converting the generated response into a voice format, a voice output means for playing the generated voice response to the user, an emergency detection means for detecting an emergency, an emergency response means for automatically notifying a specialist institution when an emergency is determined, and a means for operating as a device including a program for processing information obtained from the voice input and generating an appropriate response. This makes it possible to analyze the user's emotional state and provide a prompt and appropriate response, as well as to respond quickly to emergencies by automatically notifying a specialist institution.

[0584] "Telephone communication means" is a general term for devices and methods for making voice calls using telephone lines.

[0585] "Voice input means" refers to hardware or software for capturing the user's voice and converting it into digital data.

[0586] "Speech recognition means" is a technology that analyzes collected voice data and converts it into text data.

[0587] "Emotion analysis means" refers to algorithms and technologies for estimating a user's emotional state based on text data.

[0588] A "response generation means" refers to a technology or system for generating an appropriate response based on the results of sentiment analysis.

[0589] A "speech synthesis means" is a technology that converts the generated text response into speech form.

[0590] The "audio output means" refers to a device or technology for playing back the voice generated by the voice synthesis means to the user.

[0591] "Emergency detection means" refers to systems or technologies for detecting emergencies from the content of user comments and emotional state.

[0592] "Emergency response measures" refer to methods or systems that automatically notify specialized agencies when an emergency is deemed to have occurred.

[0593] "Means operating as a device containing a program" means a system or apparatus that allows a device containing a program to process information obtained from voice input and generate an appropriate response to perform the function of the device.

[0594] This invention relates to a system in which, when a user calls a specific consultation number using a smartphone or landline, a server receives the call and analyzes the user's emotions and emergency situations from the voice. This system uses voice recognition and emotion analysis to understand the user's concerns, generate an appropriate response, and, if necessary, take emergency action.

[0595] First, when a user calls the dedicated consultation number using a smartphone or landline, the server receives the call and establishes a connection. The server automatically plays an opening message such as "Welcome to the consultation center. Please tell us about your concerns." When the user speaks about their concerns, the audio is collected by the server.

[0596] The collected voice is converted into text data by a speech recognition means. This speech recognition uses the widely used "speech_recognition" library. The converted text data is sent to an emotion analysis means, where the user's emotions are analyzed. This emotion analysis uses the "EmotionAnalyzer" API.

[0597] Based on the results of the sentiment analysis, the server generates an appropriate response using the response generation means. This response generation uses the "TextGenerator" API. The generated response is converted into audio format by the speech synthesis means and played back to the user. This speech synthesis uses the "pyttsx3" library.

[0598] Furthermore, an emergency detection mechanism is implemented to detect emergencies from the user's speech and emotional state. If an emergency is detected, the server automatically notifies specialized agencies using emergency response mechanisms. This notification is performed using the "EmergencyNotifier" class.

[0599] For example, if a user says, "I've been having a lot of failures at work lately, and life is tough," the system will analyze the statement and generate an appropriate response, such as, "That must be a very tough experience. I understand how you feel." Similarly, if a user says, "I'm scared someone is breaking into my house right now," the system will immediately notify specialized authorities using emergency response methods.

[0600] An example of a prompt sentence is: "User: 'I'm scared that someone is breaking into my house. What should I do?' System response: 'Please move to the nearest safe place immediately and call the police. I will also call emergency services.'"

[0601] The above is an embodiment of the present invention. This system can provide safety and a sense of security by quickly and appropriately responding to user concerns and emergencies.

[0602] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0603] Step 1:

[0604] The user calls the consultation number using a smartphone or landline.

[0605] Input: User's voice data

[0606] Output: Connected call session

[0607] Specific operation: When a user makes a call, the server receives the call and establishes a call connection.

[0608] Step 2:

[0609] The server plays a welcome message.

[0610] Input: Connected call session

[0611] Output: The initial message that is played

[0612] Specific operation: The server plays an automated voice message such as "Welcome to the consultation center. Please tell us your concerns."

[0613] Step 3:

[0614] The user speaks out their concerns.

[0615] Input: Voice input by the user after receiving the initial message from the server

[0616] Output: User's voice data

[0617] Specific operation: The user speaks about their concerns into the microphone, and the voice data is collected by the server.

[0618] Step 4:

[0619] The server collects the voice data and converts it into text using a voice recognition means.

[0620] Input: User's voice data

[0621] Output: Text data

[0622] Specific operation: The server uses the "speech_recognition" library to convert the user's voice data into text format.

[0623] Step 5:

[0624] The server analyzes the text data using emotion analysis means.

[0625] Input: Text data

[0626] Output: Sentiment analysis result data

[0627] Specific operation: The server uses the "EmotionAnalyzer" API to analyze text data and estimate the user's emotional state.

[0628] Step 6:

[0629] The server generates a response based on the sentiment analysis results.

[0630] Input: Sentiment analysis result data

[0631] Output: The generated response text

[0632] Specific operation: The server uses the "TextGenerator" API to generate a response appropriate for the user based on the results of sentiment analysis.

[0633] Step 7:

[0634] The server converts the generated response into speech using a speech synthesis means.

[0635] Input: Generated response text

[0636] Output: Audio data

[0637] What it does: The server uses the "pyttsx3" library to convert the generated text response into audio format.

[0638] Step 8:

[0639] The server plays the audio data to the user.

[0640] Input: Audio data

[0641] Output: The audio message the user hears

[0642] What happens: The server streams the audio data to the user's phone and plays it back.

[0643] Step 9:

[0644] The server detects emergencies based on the user's speech and emotional state.

[0645] Input: Sentiment analysis results and speech content

[0646] Output: Emergency detection result

[0647] Specific operation: The server uses the emergency detection means to analyze the user's comments and emotional state and determine whether or not an emergency has occurred.

[0648] Step 10:

[0649] If the server is determined to be in an emergency, emergency response measures will be used to automatically notify specialized agencies.

[0650] Input: Emergency detection result

[0651] Output: Automatic notification message

[0652] Specific operation: The server uses the "EmergencyNotifier" class to automatically send a notification to specialized agencies when an emergency response is required.

[0653] This series of processes allows users to receive a prompt and appropriate response simply by consulting using a smartphone or landline, and in the event of an emergency, appropriate emergency response will be provided.

[0654] 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.

[0655] The present invention is embodied by a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine that recognizes the emotions of a user.

[0656] System Overview

[0657] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using a speech recognition system. The text data is analyzed for the user's emotions using an emotion analysis system and an emotion engine, and an appropriate response is generated by a generative AI system. The generated response is converted into speech using a speech synthesis system and played back to the user. Furthermore, if the content of the user's comments or emotional state indicates an emergency, the server notifies a specialist agency. The emotion engine also works in cooperation with the emotion analysis system to more accurately recognize emotions by referring not only to the text data but also to the tone and speed of the voice and past consultation history, thereby optimizing the response.

[0658] Natural language explanation of program processing

[0659] Users Place Calls

[0660] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[0661] Playing a welcome message

[0662] The server automatically plays an IVR message saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[0663] User input of consultation content

[0664] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[0665] Receives the user's voice data

[0666] The server collects the user's speech and obtains it as voice data in real time.

[0667] Speech to text

[0668] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[0669] Emotion analysis using emotion analysis methods and emotion engines

[0670] The server sends both text and voice data to the emotion analysis means and emotion engine to analyze the user's emotions. The emotion engine analyzes not only the text data but also the tone and speed of the voice to improve the accuracy of emotion recognition. It also references past consultation history to optimize responses.

[0671] Appropriate response generation by response generation means

[0672] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[0673] Response synthesis and playback

[0674] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0675] Continue or end the user interaction

[0676] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[0677] Emergency detection and response

[0678] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[0679] To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and deliver it to the user via voice. The emotion engine will also analyze the tone and speed of the user's voice to provide a more accurate response. If it is determined to be an emergency, the system will immediately notify a specialist agency. In this way, the present invention provides 24-hour support for users' mental anxieties.

[0680] The processing flow will be explained below.

[0681] Step 1:

[0682] The user calls the consultation number using a smartphone or landline.

[0683] Step 2:

[0684] The server receives the call and establishes the connection.

[0685] Step 3:

[0686] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[0687] Step 4:

[0688] The user talks about their worries over the phone. For example, they might say, "My work isn't going well, and life is hard."

[0689] Step 5:

[0690] The server collects the user's voice data in real time.

[0691] Step 6:

[0692] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[0693] Step 7:

[0694] The server sends the text data to the emotion analysis means and the emotion engine.

[0695] Step 8:

[0696] An emotion analyzer and emotion engine analyzes the text and audio data (including tone and rate) to infer the user's emotional state (e.g., despair, sadness).

[0697] Step 9:

[0698] The emotion engine refers to past consultation history to recognize emotional states and optimize responses.

[0699] Step 10:

[0700] The server sends the emotion analysis results to the generative AI.

[0701] Step 11:

[0702] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[0703] Step 12:

[0704] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[0705] Step 13:

[0706] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[0707] Step 14:

[0708] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[0709] Step 15:

[0710] The server collects the audio data again and repeats the process from step 5 to step 13.

[0711] Step 16:

[0712] The server detects emergencies based on the user's speech and emotional state.

[0713] Step 17:

[0714] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[0715] Step 18:

[0716] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[0717] Example 2

[0718] 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."

[0719] In modern society, the number of people suffering from mental distress is increasing, and in particularly serious cases, many people consider suicide. It is important to provide a system that can respond quickly and appropriately to such emergencies. However, conventional consultation systems often have difficulty accurately grasping the user's emotional state and are unable to generate an appropriate response. They also lack the functionality to detect emergencies and notify specialized institutions. This presents a challenge in that they are unable to effectively support users with their concerns.

[0720] 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 a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine. This enables highly accurate emotion recognition by collecting the user's voice in real time, converting it into text by voice recognition, and analyzing the text and the tone and speed of the voice. Furthermore, the emotion engine can also refer to past consultation history to improve the appropriateness of the response. Furthermore, by detecting an emergency and automatically notifying a specialized institution, the user's safety can be quickly ensured.

[0721] "Telephone communication means" is a general term for hardware and software for making voice calls, and is compatible with both mobile terminals and landlines.

[0722] "Audio input means" refers to a microphone and associated hardware and software for collecting the user's voice.

[0723] "Speech recognition means" refers to technology or devices for converting collected voice data into text, and includes, for example, a voice recognition engine.

[0724] The "emotion analysis means" refers to a technique or device for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state.

[0725] The "response generation means" refers to a technique or device for generating an appropriate response based on the emotional state of the user estimated by the emotion analysis means, and includes generative artificial intelligence.

[0726] A "voice synthesis means" is a technique or device for converting a generated response into voice form.

[0727] "Audio output means" refers to a speaker and associated hardware and software for playing back to the user the audio response generated by the speech synthesis means.

[0728] "Emergency detection means" refers to technology or devices for determining an emergency from the user's statements and emotional state.

[0729] "Emergency response means" refers to technology or devices that automatically notify specialized agencies when an emergency is determined by the emergency detection means.

[0730] An "emotion engine" is a technology or device that analyzes the tone and speed of speech to improve the accuracy of emotion analysis, and also references past consultation history.

[0731] The present invention is embodied in a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, an emotion engine, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means.

[0732] First, when a user calls a specific consultation number using a smartphone or landline, the server receives the call and establishes a connection. The telephone communication method used here is a general term for the hardware and software used to make voice calls, and is compatible with both mobile devices and landlines.

[0733] Next, the server automatically plays an initial message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us about your concerns." This is done using a voice output means.

[0734] When a user speaks about their concerns, the voice is collected in real time by the server via a voice input means, i.e., a microphone and associated hardware and software.

[0735] The voice data collected by the server is converted into text data using a voice recognition tool (e.g., Google Cloud Speech-to-Text). The voice recognition tool here refers to technology or equipment for converting voice data into text.

[0736] Next, the server sends both the text data and the voice data to an emotion analysis means (e.g., IBM Watson Tone Analyzer) and an emotion engine to analyze the user's emotions. An emotion analysis means refers to technology or equipment that analyzes the consultation content converted into text by a voice recognition means and estimates the user's emotional state. The emotion engine analyzes the tone and speed of the voice and also refers to past consultation history to improve the accuracy of emotion recognition.

[0737] Once the sentiment analysis is complete, the server generates an appropriate response using generative AI (e.g., OpenAI GPT-4). The response generation means is a technology or device that generates an appropriate response based on the sentiment analysis results. The generated response is converted into speech using a speech synthesis means (e.g., Amazon Polly). The speech synthesis means is a technology or device that converts the generated response into speech format.

[0738] The server then plays back the generated voice response. If the user wants to continue speaking, the system collects the voice again and repeats the same process. If an emergency is detected, the server automatically notifies a specialist agency. Emergency detection means are technologies or devices that determine an emergency from the user's speech or emotional state, and emergency response means are technologies or devices that automatically notify a specialist agency when an emergency is determined by the emergency detection means.

[0739] As a concrete example, consider the case where a user calls a dedicated consultation number from their smartphone late at night. The system automatically answers the call, and if the user says, "I've been having a lot of failures at work lately, and life is hard," the server analyzes the statement and generates an appropriate response, "That must be a very painful experience. I understand how you feel," which is conveyed to the user via voice. Furthermore, the emotion engine analyzes the tone and speed of the user's voice to provide a more accurate response. If the call is deemed an emergency, specialized agencies are immediately notified.

[0740] Examples of prompt sentences are as follows:

[0741] User: "I've been failing at work lately and life is hard."

[0742] Server: "That must be a very painful experience. I understand how you feel."

[0743] This allows the system of the present invention to provide support for users' mental anxieties 24 hours a day.

[0744] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0745] Step 1:

[0746] The user calls a specific consultation number using a smartphone or landline. The server receives the call and establishes a connection. The input is the user's call operation, and the output is the establishment of the call. At this time, the server initializes the communication protocol using the telephone communication means.

[0747] Step 2:

[0748] The server plays a welcome message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns." The input is the establishment of a telephone connection, and the output is the audio playback of the welcome message. The server plays a pre-recorded message from the audio output means.

[0749] Step 3:

[0750] The user talks about their worries over the phone, for example, "I've been failing at work a lot lately, and life is hard." The input is the user's voice, and the output is passed to the server as voice data. At this stage, the voice input means captures what the user is saying.

[0751] Step 4:

[0752] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text) to convert the collected voice data into text data. The input is voice data, and the output is text data. The server performs the text conversion of the voice data through an API call.

[0753] Step 5:

[0754] The server sends both text and voice data to an emotion analysis tool (e.g., IBM Watson Tone Analyzer) and an emotion engine to analyze the user's emotions. The input is text and voice data, and the output is data indicating the user's emotional state. The emotion engine improves the accuracy of the analysis by referring to the tone and speed of the voice and past consultation history.

[0755] Step 6:

[0756] The server uses a generative AI (e.g., OpenAI GPT-4) to generate an appropriate response based on the results of emotion analysis. The input is data indicating the emotional state, and the output is a response text. An API is used to send prompts to the generative AI and receive the response text.

[0757] Step 7:

[0758] The server converts the generated response text into speech using a speech synthesis engine (e.g., Amazon Polly) and plays it back to the user. The input is the response text and the output is audio data. The speech synthesis engine converts the text into audio format, which the server plays back.

[0759] Step 8:

[0760] If the user wants to continue speaking, the server collects the audio again and repeats the process to generate a new response: the input is new audio data, the output is a new response, and the system continues the loop.

[0761] Step 9:

[0762] The server determines an emergency based on the user's emotional state and speech content, and automatically notifies specialized organizations. The input is emotion analysis data, and the output is an emergency notification. The server makes a judgment using emergency detection means, and contacts specialized organizations via emergency response means.

[0763] (Application example 2)

[0764] 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."

[0765] Conventional customer support systems often struggle to provide prompt and appropriate responses when users have specific problems or questions. They also struggle to accurately grasp the user's emotional state and provide appropriate responses. Furthermore, it is difficult to quickly detect emergencies and respond appropriately, which prevents users from achieving satisfactory results or a sense of security.

[0766] 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 telephone communication means, voice input means for collecting user voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, response generation means for generating an appropriate response based on the estimated user's emotional state, voice synthesis means for converting the generated response into voice format, voice output means for playing the voice response to the user, emergency detection means for detecting an emergency, emergency response means for automatically notifying a specialist agency when an emergency is determined, means installed on a smart device for providing customer support through voice dialogue in a physical store, response generation means for generating appropriate responses to user questions, voice synthesis means for converting the responses into voice format, and means designed for use in a physical store. This makes it possible to quickly and appropriately respond to user problems and questions, accurately grasp the user's emotional state, provide appropriate responses, and quickly respond to emergencies.

[0767] "Telephone communication means" refers to a means by which a user communicates with a server via voice, and includes mobile terminals and landlines.

[0768] "Voice input means" refers to a device or function for collecting the user's spoken voice.

[0769] "Speech recognition means" refers to a technology or device that analyzes collected speech and converts it into text data.

[0770] "Emotion analysis means" refers to a technique or device for analyzing text data and voice data and estimating the user's emotional state.

[0771] A "response generator" is a system or device that generates an appropriate response based on the estimated emotional state and text data.

[0772] A "speech synthesis means" is a technique or device for converting a generated text response into speech.

[0773] The "audio output means" refers to a technique or device for playing back the voice generated by the voice synthesis means to the user.

[0774] The "emergency detection means" is a technology or device for determining an emergency based on the content of a user's speech or emotional state.

[0775] "Emergency response means" refers to a technology or device that automatically notifies a specialized agency when an emergency is determined by the emergency detection means.

[0776] The "means installed on the smart device" refers to an application or system that provides customer support in a physical store through voice interaction in response to user questions.

[0777] "Means designed for use in a physical store" refers to a system or application with various support functions intended for use in a physical store.

[0778] An embodiment of the present invention will be described.

[0779] System Overview

[0780] The server includes a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means. The system is installed on a smart device and provides customer support through voice dialogue in a physical store. The system is designed for use in a physical store.

[0781] Hardware used

[0782] Smartphones: Works with Android and iOS.

[0783] Server: Operated in a cloud or on-premise environment.

[0784] Software used

[0785] Speech recognition library: Google Cloud Speech-to-Text API

[0786] Sentiment analysis engine: IBM Watson Tone Analyzer

[0787] Response generation method: GPT-3 (OpenAI)

[0788] Speech synthesis engine: Google Cloud Text-to-Speech API

[0789] Data processing and calculation

[0790] The operation of this system is configured as follows.

[0791] 1. User voice input

[0792] The user speaks their question or inquiry into their smartphone, for example, "Please tell me about the features of this product."

[0793] 2. Voice Recognition

[0794] Voice input is captured through the smartphone's microphone and converted to text data using the Google Cloud Speech-to-Text API.

[0795] 3. Emotion analysis

[0796] The resulting text data is sent to IBM Watson Tone Analyzer, which analyzes the user's emotional state and assigns emotion tags such as "excitement" or "interest."

[0797] 4. Response Generation

[0798] Based on the results of sentiment analysis and text data, GPT-3 generates an appropriate response. The following prompt sentence is input into the generative AI model:

[0799] User's query: "What are the features of this product?"

[0800] Detected emotion: "Excited"

[0801] Generate a suitable response for the user considering their emotional state.

[0802] 5. Speech Synthesis

[0803] The generated text response is converted to audio using the Google Cloud Text-to-Speech API.

[0804] 6. Audio Output

[0805] The synthesized response is played back through the smartphone's speaker and conveyed to the user.

[0806] Specific examples

[0807] scenario

[0808] If a user is using their smartphone in a physical store, opens the "Customer Support Concierge" application, and asks, "What are the features of this product?"

[0809] The server uses a speech recognition engine to convert this question into text,

[0810] Using a sentiment analysis engine, it detects when a user is feeling "excited" and

[0811] GPT-3 generates a response like, "This product is the latest model and has the following features..."

[0812] Finally, the speech synthesis engine converts the response into speech and responds to the user on their smartphone.

[0813] In this way, the "Customer Support Concierge" based on the technology of this invention combines voice recognition and emotion analysis to provide high-quality customer support in real time within the store. This allows for quick and appropriate responses to user problems and questions, while also accurately grasping the user's emotional state and providing an appropriate response. It also allows for quick response in emergencies. These functions allow users to use the service with peace of mind.

[0814] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0815] Step 1:

[0816] The user speaks their question or inquiry into the smartphone. For example, the user might say, "Please tell me about the features of this product." This voice input is captured by the smartphone's microphone.

[0817] Step 2:

[0818] The captured voice data is sent to the server, which then uses the Google Cloud Speech-to-Text API to convert the voice data into text. The input is voice data, and the output is text data. The specific operation of this conversion is to analyze the voice waveform and output the corresponding text.

[0819] Step 3:

[0820] The converted text data is sent to IBM Watson Tone Analyzer. The server uses this API to analyze the emotional state from the text data. The input is text data, and the output is an emotion tag. Specifically, it analyzes the content, context, and wording of the text, and assigns emotion tags such as "excitement," "interest," and "anxiety."

[0821] Step 4:

[0822] The server passes the results of the sentiment analysis and text data to GPT-3, which then generates an appropriate response. The input is the sentiment tag and text data, and the output is the response text. At this stage, the following prompt sentence is input to the generative AI model:

[0823] User's query: "What are the features of this product?"

[0824] Detected emotion: "Excited"

[0825] Generate a suitable response for the user considering their emotional state.

[0826] Specifically, GPT-3 generates the optimal response in natural language based on this prompt.

[0827] Step 5:

[0828] The server sends the generated response text to the Google Cloud Text-to-Speech API. The input is the response text, and the output is the audio data. Specifically, the API analyzes the text and synthesizes a natural-sounding voice corresponding to it.

[0829] Step 6:

[0830] The generated voice data is sent to the smartphone, which then plays the voice through its speaker, allowing the user to hear the appropriate response.

[0831] Step 7:

[0832] If the user has any further questions or inquiries, the process is repeated again from step 1.

[0833] This allows the system to respond to user problems and questions in real time, analyze their emotional state to provide appropriate responses, and take appropriate action in emergency situations.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] [Third embodiment]

[0838] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0839] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0840] 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).

[0841] 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.

[0842] 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.

[0843] 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).

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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."

[0850] The present invention is implemented by a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[0851] System Overview

[0852] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using speech recognition means. The text data is analyzed for the user's emotions using emotion analysis means, and an appropriate response is generated by generative AI. The generated response is converted into voice using speech synthesis means and played back to the user. Furthermore, if the user's comments or emotional state indicate an emergency, the server notifies specialized agencies.

[0853] Natural language explanation of program processing

[0854] Users Place Calls

[0855] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[0856] Playing a welcome message

[0857] The server automatically plays an IVR message such as, "Welcome to the suicide prevention consultation center. Please tell us your concerns."

[0858] User input of consultation content

[0859] The user speaks about their worries. For example, the user might say, "My work isn't going well, and life is hard."

[0860] Receives the user's voice data

[0861] The server collects the user's speech and obtains it as voice data in real time.

[0862] Speech to text

[0863] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[0864] Emotion analysis using emotion analysis tools

[0865] The server sends the text data to the emotion analysis engine, which analyzes the user's emotions. The emotion generation engine then reads the emotion "despair" from the text.

[0866] Appropriate response generation by response generation means

[0867] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[0868] Response synthesis and playback

[0869] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0870] Continue or end the user interaction

[0871] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[0872] Emergency detection and response

[0873] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[0874] The above is an embodiment of the present invention. To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer the call. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and convey it to the user via voice. If the situation is deemed an emergency, the system will immediately notify a specialist institution. In this way, the present invention provides 24-hour support for users' mental anxieties.

[0875] The processing flow will be explained below.

[0876] Step 1:

[0877] The user calls the consultation number using a smartphone or landline.

[0878] Step 2:

[0879] The server receives the call and establishes the connection.

[0880] Step 3:

[0881] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[0882] Step 4:

[0883] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[0884] Step 5:

[0885] The server collects the user's voice data in real time.

[0886] Step 6:

[0887] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[0888] Step 7:

[0889] The server transmits the text data to the emotion analysis means.

[0890] Step 8:

[0891] A sentiment analysis means analyzes the text data and estimates the user's emotional state (e.g., despair, sadness).

[0892] Step 9:

[0893] The server sends the emotion analysis results to the generative AI.

[0894] Step 10:

[0895] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[0896] Step 11:

[0897] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[0898] Step 12:

[0899] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[0900] Step 13:

[0901] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[0902] Step 14:

[0903] The server collects the audio data again and repeats the process from step 5 to step 12.

[0904] Step 15:

[0905] The server detects emergencies based on the user's speech and emotional state.

[0906] Step 16:

[0907] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[0908] Step 17:

[0909] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[0910] Example 1

[0911] 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."

[0912] In today's world, many people are experiencing stress and worries in their daily lives and at work, and this is increasingly leading to mental health problems. Conventional systems have limitations in providing prompt and appropriate responses to these problems. In particular, it has been difficult to notify specialized agencies in a timely manner for users at risk of suicide or experiencing serious mental difficulties.

[0913] 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.

[0914] In this invention, the server includes telephone communication means, voice input means for collecting a user's voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state, response generation means for generating an appropriate response based on the user's emotional state estimated by the emotion analysis means, voice synthesis means for converting the generated response into voice format, voice output means for playing back to the user the voice response generated by the voice synthesis means, means for collecting user utterances in real time and acquiring them as voice data, means for analyzing the voice data in real time and generating an appropriate response, means for verifying the generated response before playing it back, emergency detection means for detecting an emergency, and emergency response means for automatically notifying a specialist institution if the emergency detection means determines that an emergency has occurred, thereby enabling a prompt and appropriate response to the user's mental health problem.

[0915] "Telephone communication means" refers to the means by which users connect to the system using smartphones or landlines.

[0916] The "voice input means" refers to a device or program for collecting user utterances as voice data.

[0917] "Speech recognition means" refers to a technology or program for converting collected voice data into text data.

[0918] "Emotion analysis means" refers to analytical techniques or programs for estimating a user's emotional state from text data.

[0919] The "response generation means" refers to a technique or program for generating an appropriate response based on the estimated emotional state of the user.

[0920] A "speech synthesis means" is a technology or program that converts the generated text response into speech form.

[0921] The "audio output means" refers to a device or program for playing back the generated audio response to the user.

[0922] The "means for acquiring as voice data" is a means for collecting user utterances in real time and saving them in voice data format.

[0923] "Means for analyzing in real time" refers to technology or programs for analyzing collected voice data in real time and generating a response.

[0924] The "means for verifying" is a means for checking the content of the generated response to ensure that it is appropriate before playing it back.

[0925] "Emergency detection means" refers to technology or programs for detecting an emergency from the content of a user's statements and emotional state.

[0926] "Emergency response tools" are technologies or programs that automatically notify specialized agencies in response to a detected emergency.

[0927] The present invention is embodied in a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[0928] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays a welcome message to the user using an interactive voice response system (IVR), while identifying the user's phone number and establishing a line to accept the call.

[0929] The initial message played by the server is something like, "Welcome to the Suicide Prevention Counseling Center. Please tell us about your worries." This is achieved by loading and playing an audio file from the automated voice response system. When a user speaks about their worries, the audio is collected using a microphone and temporarily saved as digital audio data. For example, a user might say, "My work isn't going well, and life is hard."

[0930] Next, the server uses a speech recognition engine such as Google Cloud Speech-to-Text API to convert the collected voice data into text data. For example, a statement such as "My job isn't going well, and life is hard" is converted into text. This text data is sent to the Microsoft Azure Text Analytics sentiment analysis engine, which analyzes the user's emotions. The sentiment analysis engine then identifies the emotion "despair" from the text.

[0931] Based on the results of the sentiment analysis, the server generates an appropriate response using a generative AI such as an OpenAI model. For example, it generates a response such as, "That must be a very painful experience. I understand how you feel." This response generation process includes embedding the user's statement and the sentiment analysis results into a prompt sentence and sending it to the AI ​​model. An example of a prompt sentence to input to the generative AI model is, "The user said, 'I've been having a lot of failures at work recently, and life is hard.' Please generate a sympathetic and supportive response for this user."

[0932] The server converts the generated text response into speech using a speech synthesis engine such as Amazon Polly and plays it back to the user, so that if the user wants to continue speaking, the system can collect the audio again and repeat the process of generating a response.

[0933] The system also detects and responds to emergencies. The server determines whether an emergency has occurred based on the user's emotional state and the content of their comments, and automatically contacts specialized agencies if necessary. For example, if a user makes a comment indicating a risk of suicide, the server will automatically notify the emergency response team, enabling a more rapid response.

[0934] With the above configuration, the present invention can provide a prompt and appropriate response to a user's mental health problems.

[0935] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0936] Step 1:

[0937] A user calls a specific consultation number using a smartphone or landline.

[0938] Input: User's phone number, call start signal

[0939] Output: Call connection establishment signal

[0940] What happens: The server identifies the user's phone number and establishes a line to accept the call.

[0941] Step 2:

[0942] The server plays a welcome message using an interactive voice response system (IVR).

[0943] Input: Call connection establishment signal

[0944] Output: Initial message sent

[0945] Specific operation: The server plays an IVR message such as "Welcome to the suicide prevention consultation center. Please tell us your concerns," so it loads an audio file in the IVR system and plays it to the user.

[0946] Step 3:

[0947] The user speaks out their concerns.

[0948] Input: User's voice data

[0949] Output: Collected audio data

[0950] Specific operation: A microphone collects the user's speech and temporarily stores it as digital audio data. For example, the user might say, "My work isn't going well, and life is hard."

[0951] Step 4:

[0952] The server captures the user's voice utterances in real time.

[0953] Input: Collected audio data

[0954] Output: Temporarily saved audio data

[0955] Specific operation: The collected voice data is stored on the server in a specific format (e.g., WAV format) for subsequent voice recognition processing.

[0956] Step 5:

[0957] The server uses a speech recognition engine to convert the collected voice data into text data.

[0958] Input: Temporarily saved audio data

[0959] Output: Text data

[0960] Specific operation: Voice data is sent to a speech recognition engine such as Google Cloud Speech-to-Text API, and the server retrieves the returned text data. For example, it may obtain text data such as "My work is not going well, and life is hard."

[0961] Step 6:

[0962] The server sends the text data to an emotion analysis engine to analyze the user's emotions.

[0963] Input: Text data

[0964] Output: Emotion analysis data (e.g., despair)

[0965] Specific operation: Text data is sent to the Microsoft Azure Text Analytics sentiment analysis engine, and the server retrieves the returned sentiment data (e.g., "despair").

[0966] Step 7:

[0967] The server uses generative AI to generate a response appropriate for the user based on the results of emotion analysis.

[0968] Input: Sentiment analysis data, text data

[0969] Output: Response text data

[0970] Specific operation: The results of the emotion analysis and the user's statement are embedded in a prompt, which is then sent to a generative AI model to generate a response. For example, the model can generate a response such as, "That must be a very painful experience. I understand how you feel."

[0971] Step 8:

[0972] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[0973] Input: Response text data

[0974] Output: Audio data

[0975] Specific operation: Sends the response text data to a speech synthesis engine such as Amazon Polly, and plays the returned audio data to the user.

[0976] Step 9:

[0977] If the user wishes to continue speaking, the system repeats the process from voice collection to response generation.

[0978] Input: The user's new voice data

[0979] Output: New collected voice data, response data

[0980] Specific operation: Repeatedly executes steps from collecting new voice to generating a response, continuing the dialogue with the user.

[0981] Step 10:

[0982] The server detects emergencies and automatically notifies specialist agencies if necessary.

[0983] Input: User sentiment analysis data or statements

[0984] Output: Emergency notification signal

[0985] What it does: If a user makes a statement that indicates a risk of suicide, it automatically notifies emergency response teams.

[0986] (Application example 1)

[0987] 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."

[0988] In modern society, it is important for individuals facing emergencies to receive prompt and appropriate responses. However, conventional systems that rely only on telephones or voice input are unable to adequately analyze emotions or detect emergencies, making it difficult to provide individuals with appropriate responses or emergency measures. For this reason, there is a demand for systems that can respond promptly and appropriately to users' concerns and emergencies.

[0989] 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.

[0990] In this invention, the server includes a telephone communication means, a voice input means for collecting a user's voice, a voice recognition means for converting the collected voice into text, a feeling analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, a response generation means for generating an appropriate response based on the estimated user's emotional state, a voice synthesis means for converting the generated response into a voice format, a voice output means for playing the generated voice response to the user, an emergency detection means for detecting an emergency, an emergency response means for automatically notifying a specialist institution when an emergency is determined, and a means for operating as a device including a program for processing information obtained from the voice input and generating an appropriate response. This makes it possible to analyze the user's emotional state and provide a prompt and appropriate response, as well as to respond quickly to emergencies by automatically notifying a specialist institution.

[0991] "Telephone communication means" is a general term for devices and methods for making voice calls using telephone lines.

[0992] "Voice input means" refers to hardware or software for capturing the user's voice and converting it into digital data.

[0993] "Speech recognition means" is a technology that analyzes collected voice data and converts it into text data.

[0994] "Emotion analysis means" refers to algorithms and technologies for estimating a user's emotional state based on text data.

[0995] A "response generation means" refers to a technology or system for generating an appropriate response based on the results of sentiment analysis.

[0996] A "speech synthesis means" is a technology that converts the generated text response into speech form.

[0997] The "audio output means" refers to a device or technology for playing back the voice generated by the voice synthesis means to the user.

[0998] "Emergency detection means" refers to systems or technologies for detecting emergencies from the content of user comments and emotional state.

[0999] "Emergency response measures" refer to methods or systems that automatically notify specialized agencies when an emergency is deemed to have occurred.

[1000] "Means operating as a device containing a program" means a system or apparatus that allows a device containing a program to process information obtained from voice input and generate an appropriate response to perform the function of the device.

[1001] This invention relates to a system in which, when a user calls a specific consultation number using a smartphone or landline, a server receives the call and analyzes the user's emotions and emergency situations from the voice. This system uses voice recognition and emotion analysis to understand the user's concerns, generate an appropriate response, and, if necessary, take emergency action.

[1002] First, when a user calls the dedicated consultation number using a smartphone or landline, the server receives the call and establishes a connection. The server automatically plays an opening message such as "Welcome to the consultation center. Please tell us about your concerns." When the user speaks about their concerns, the audio is collected by the server.

[1003] The collected voice is converted into text data by a speech recognition means. This speech recognition uses the widely used "speech_recognition" library. The converted text data is sent to an emotion analysis means, where the user's emotions are analyzed. This emotion analysis uses the "EmotionAnalyzer" API.

[1004] Based on the results of the sentiment analysis, the server generates an appropriate response using the response generation means. This response generation uses the "TextGenerator" API. The generated response is converted into audio format by the speech synthesis means and played back to the user. This speech synthesis uses the "pyttsx3" library.

[1005] Furthermore, an emergency detection mechanism is implemented to detect emergencies from the user's speech and emotional state. If an emergency is detected, the server automatically notifies specialized agencies using emergency response mechanisms. This notification is performed using the "EmergencyNotifier" class.

[1006] For example, if a user says, "I've been having a lot of failures at work lately, and life is tough," the system will analyze the statement and generate an appropriate response, such as, "That must be a very tough experience. I understand how you feel." Similarly, if a user says, "I'm scared someone is breaking into my house right now," the system will immediately notify specialized authorities using emergency response methods.

[1007] An example of a prompt sentence is: "User: 'I'm scared that someone is breaking into my house. What should I do?' System response: 'Please move to the nearest safe place immediately and call the police. I will also call emergency services.'"

[1008] The above is an embodiment of the present invention. This system can provide safety and a sense of security by quickly and appropriately responding to user concerns and emergencies.

[1009] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1010] Step 1:

[1011] The user calls the consultation number using a smartphone or landline.

[1012] Input: User's voice data

[1013] Output: Connected call session

[1014] Specific operation: When a user makes a call, the server receives the call and establishes a call connection.

[1015] Step 2:

[1016] The server plays a welcome message.

[1017] Input: Connected call session

[1018] Output: The initial message that is played

[1019] Specific operation: The server plays an automated voice message such as "Welcome to the consultation center. Please tell us your concerns."

[1020] Step 3:

[1021] The user speaks out their concerns.

[1022] Input: Voice input by the user after receiving the initial message from the server

[1023] Output: User's voice data

[1024] Specific operation: The user speaks about their concerns into the microphone, and the voice data is collected by the server.

[1025] Step 4:

[1026] The server collects the voice data and converts it into text using a voice recognition means.

[1027] Input: User's voice data

[1028] Output: Text data

[1029] Specific operation: The server uses the "speech_recognition" library to convert the user's voice data into text format.

[1030] Step 5:

[1031] The server analyzes the text data using emotion analysis means.

[1032] Input: Text data

[1033] Output: Sentiment analysis result data

[1034] Specific operation: The server uses the "EmotionAnalyzer" API to analyze text data and estimate the user's emotional state.

[1035] Step 6:

[1036] The server generates a response based on the sentiment analysis results.

[1037] Input: Sentiment analysis result data

[1038] Output: The generated response text

[1039] Specific operation: The server uses the "TextGenerator" API to generate a response appropriate for the user based on the results of sentiment analysis.

[1040] Step 7:

[1041] The server converts the generated response into speech using a speech synthesis means.

[1042] Input: Generated response text

[1043] Output: Audio data

[1044] What it does: The server uses the "pyttsx3" library to convert the generated text response into audio format.

[1045] Step 8:

[1046] The server plays the audio data to the user.

[1047] Input: Audio data

[1048] Output: The audio message the user hears

[1049] What happens: The server streams the audio data to the user's phone and plays it back.

[1050] Step 9:

[1051] The server detects emergencies based on the user's speech and emotional state.

[1052] Input: Sentiment analysis results and speech content

[1053] Output: Emergency detection result

[1054] Specific operation: The server uses the emergency detection means to analyze the user's comments and emotional state and determine whether or not an emergency has occurred.

[1055] Step 10:

[1056] If the server is determined to be in an emergency, emergency response measures will be used to automatically notify specialized agencies.

[1057] Input: Emergency detection result

[1058] Output: Automatic notification message

[1059] Specific operation: The server uses the "EmergencyNotifier" class to automatically send a notification to specialized agencies when an emergency response is required.

[1060] This series of processes allows users to receive a prompt and appropriate response simply by consulting using a smartphone or landline, and in the event of an emergency, appropriate emergency response will be provided.

[1061] 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.

[1062] The present invention is embodied by a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine that recognizes the emotions of a user.

[1063] System Overview

[1064] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using a speech recognition system. The text data is analyzed for the user's emotions using an emotion analysis system and an emotion engine, and an appropriate response is generated by a generative AI system. The generated response is converted into speech using a speech synthesis system and played back to the user. Furthermore, if the content of the user's comments or emotional state indicates an emergency, the server notifies a specialist agency. The emotion engine also works in cooperation with the emotion analysis system to more accurately recognize emotions by referring not only to the text data but also to the tone and speed of the voice and past consultation history, thereby optimizing the response.

[1065] Natural language explanation of program processing

[1066] Users Place Calls

[1067] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[1068] Playing a welcome message

[1069] The server automatically plays an IVR message saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[1070] User input of consultation content

[1071] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[1072] Receives the user's voice data

[1073] The server collects the user's speech and obtains it as voice data in real time.

[1074] Speech to text

[1075] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[1076] Emotion analysis using emotion analysis methods and emotion engines

[1077] The server sends both text and voice data to the emotion analysis means and emotion engine to analyze the user's emotions. The emotion engine analyzes not only the text data but also the tone and speed of the voice to improve the accuracy of emotion recognition. It also references past consultation history to optimize responses.

[1078] Appropriate response generation by response generation means

[1079] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[1080] Response synthesis and playback

[1081] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[1082] Continue or end the user interaction

[1083] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[1084] Emergency detection and response

[1085] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[1086] To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and deliver it to the user via voice. The emotion engine will also analyze the tone and speed of the user's voice to provide a more accurate response. If it is determined to be an emergency, the system will immediately notify a specialist agency. In this way, the present invention provides 24-hour support for users' mental anxieties.

[1087] The processing flow will be explained below.

[1088] Step 1:

[1089] The user calls the consultation number using a smartphone or landline.

[1090] Step 2:

[1091] The server receives the call and establishes the connection.

[1092] Step 3:

[1093] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[1094] Step 4:

[1095] The user talks about their worries over the phone. For example, they might say, "My work isn't going well, and life is hard."

[1096] Step 5:

[1097] The server collects the user's voice data in real time.

[1098] Step 6:

[1099] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[1100] Step 7:

[1101] The server sends the text data to the emotion analysis means and the emotion engine.

[1102] Step 8:

[1103] An emotion analyzer and emotion engine analyzes the text and audio data (including tone and rate) to infer the user's emotional state (e.g., despair, sadness).

[1104] Step 9:

[1105] The emotion engine refers to past consultation history to recognize emotional states and optimize responses.

[1106] Step 10:

[1107] The server sends the emotion analysis results to the generative AI.

[1108] Step 11:

[1109] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[1110] Step 12:

[1111] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[1112] Step 13:

[1113] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[1114] Step 14:

[1115] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[1116] Step 15:

[1117] The server collects the audio data again and repeats the process from step 5 to step 13.

[1118] Step 16:

[1119] The server detects emergencies based on the user's speech and emotional state.

[1120] Step 17:

[1121] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[1122] Step 18:

[1123] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[1124] Example 2

[1125] 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."

[1126] In modern society, the number of people suffering from mental distress is increasing, and in particularly serious cases, many people consider suicide. It is important to provide a system that can respond quickly and appropriately to such emergencies. However, conventional consultation systems often have difficulty accurately grasping the user's emotional state and are unable to generate an appropriate response. They also lack the functionality to detect emergencies and notify specialized institutions. This presents a challenge in that they are unable to effectively support users with their concerns.

[1127] 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 a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine. This enables highly accurate emotion recognition by collecting the user's voice in real time, converting it into text by voice recognition, and analyzing the text and the tone and speed of the voice. Furthermore, the emotion engine can also refer to past consultation history to improve the appropriateness of the response. Furthermore, by detecting an emergency and automatically notifying a specialized institution, the user's safety can be quickly ensured.

[1128] "Telephone communication means" is a general term for hardware and software for making voice calls, and is compatible with both mobile terminals and landlines.

[1129] "Audio input means" refers to a microphone and associated hardware and software for collecting the user's voice.

[1130] "Speech recognition means" refers to technology or devices for converting collected voice data into text, and includes, for example, a voice recognition engine.

[1131] The "emotion analysis means" refers to a technique or device for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state.

[1132] The "response generation means" refers to a technique or device for generating an appropriate response based on the emotional state of the user estimated by the emotion analysis means, and includes generative artificial intelligence.

[1133] A "voice synthesis means" is a technique or device for converting a generated response into voice form.

[1134] "Audio output means" refers to a speaker and associated hardware and software for playing back to the user the audio response generated by the speech synthesis means.

[1135] "Emergency detection means" refers to technology or devices for determining an emergency from the user's statements and emotional state.

[1136] "Emergency response means" refers to technology or devices that automatically notify specialized agencies when an emergency is determined by the emergency detection means.

[1137] An "emotion engine" is a technology or device that analyzes the tone and speed of speech to improve the accuracy of emotion analysis, and also references past consultation history.

[1138] The present invention is embodied in a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, an emotion engine, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means.

[1139] First, when a user calls a specific consultation number using a smartphone or landline, the server receives the call and establishes a connection. The telephone communication method used here is a general term for the hardware and software used to make voice calls, and is compatible with both mobile devices and landlines.

[1140] Next, the server automatically plays an initial message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us about your concerns." This is done using a voice output means.

[1141] When a user speaks about their concerns, the voice is collected in real time by the server via a voice input means, i.e., a microphone and associated hardware and software.

[1142] The voice data collected by the server is converted into text data using a voice recognition tool (e.g., Google Cloud Speech-to-Text). The voice recognition tool here refers to technology or equipment for converting voice data into text.

[1143] Next, the server sends both the text data and the voice data to an emotion analysis means (e.g., IBM Watson Tone Analyzer) and an emotion engine to analyze the user's emotions. An emotion analysis means refers to technology or equipment that analyzes the consultation content converted into text by a voice recognition means and estimates the user's emotional state. The emotion engine analyzes the tone and speed of the voice and also refers to past consultation history to improve the accuracy of emotion recognition.

[1144] Once the sentiment analysis is complete, the server generates an appropriate response using generative AI (e.g., OpenAI GPT-4). The response generation means is a technology or device that generates an appropriate response based on the sentiment analysis results. The generated response is converted into speech using a speech synthesis means (e.g., Amazon Polly). The speech synthesis means is a technology or device that converts the generated response into speech format.

[1145] The server then plays back the generated voice response. If the user wants to continue speaking, the system collects the voice again and repeats the same process. If an emergency is detected, the server automatically notifies a specialist agency. Emergency detection means are technologies or devices that determine an emergency from the user's speech or emotional state, and emergency response means are technologies or devices that automatically notify a specialist agency when an emergency is determined by the emergency detection means.

[1146] As a concrete example, consider the case where a user calls a dedicated consultation number from their smartphone late at night. The system automatically answers the call, and if the user says, "I've been having a lot of failures at work lately, and life is hard," the server analyzes the statement and generates an appropriate response, "That must be a very painful experience. I understand how you feel," which is conveyed to the user via voice. Furthermore, the emotion engine analyzes the tone and speed of the user's voice to provide a more accurate response. If the call is deemed an emergency, specialized agencies are immediately notified.

[1147] Examples of prompt sentences are as follows:

[1148] User: "I've been failing at work lately and life is hard."

[1149] Server: "That must be a very painful experience. I understand how you feel."

[1150] This allows the system of the present invention to provide support for users' mental anxieties 24 hours a day.

[1151] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1152] Step 1:

[1153] The user calls a specific consultation number using a smartphone or landline. The server receives the call and establishes a connection. The input is the user's call operation, and the output is the establishment of the call. At this time, the server initializes the communication protocol using the telephone communication means.

[1154] Step 2:

[1155] The server plays a welcome message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns." The input is the establishment of a telephone connection, and the output is the audio playback of the welcome message. The server plays a pre-recorded message from the audio output means.

[1156] Step 3:

[1157] The user talks about their worries over the phone, for example, "I've been failing at work a lot lately, and life is hard." The input is the user's voice, and the output is passed to the server as voice data. At this stage, the voice input means captures what the user is saying.

[1158] Step 4:

[1159] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text) to convert the collected voice data into text data. The input is voice data, and the output is text data. The server performs the text conversion of the voice data through an API call.

[1160] Step 5:

[1161] The server sends both text and voice data to an emotion analysis tool (e.g., IBM Watson Tone Analyzer) and an emotion engine to analyze the user's emotions. The input is text and voice data, and the output is data indicating the user's emotional state. The emotion engine improves the accuracy of the analysis by referring to the tone and speed of the voice and past consultation history.

[1162] Step 6:

[1163] The server uses a generative AI (e.g., OpenAI GPT-4) to generate an appropriate response based on the results of emotion analysis. The input is data indicating the emotional state, and the output is a response text. An API is used to send prompts to the generative AI and receive the response text.

[1164] Step 7:

[1165] The server converts the generated response text into speech using a speech synthesis engine (e.g., Amazon Polly) and plays it back to the user. The input is the response text and the output is audio data. The speech synthesis engine converts the text into audio format, which the server plays back.

[1166] Step 8:

[1167] If the user wants to continue speaking, the server collects the audio again and repeats the process to generate a new response: the input is new audio data, the output is a new response, and the system continues the loop.

[1168] Step 9:

[1169] The server determines an emergency based on the user's emotional state and speech content, and automatically notifies specialized organizations. The input is emotion analysis data, and the output is an emergency notification. The server makes a judgment using emergency detection means, and contacts specialized organizations via emergency response means.

[1170] (Application example 2)

[1171] 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."

[1172] Conventional customer support systems often struggle to provide prompt and appropriate responses when users have specific problems or questions. They also struggle to accurately grasp the user's emotional state and provide appropriate responses. Furthermore, it is difficult to quickly detect emergencies and respond appropriately, which prevents users from achieving satisfactory results or a sense of security.

[1173] 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 telephone communication means, voice input means for collecting user voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, response generation means for generating an appropriate response based on the estimated user's emotional state, voice synthesis means for converting the generated response into voice format, voice output means for playing the voice response to the user, emergency detection means for detecting an emergency, emergency response means for automatically notifying a specialist agency when an emergency is determined, means installed on a smart device for providing customer support through voice dialogue in a physical store, response generation means for generating appropriate responses to user questions, voice synthesis means for converting the responses into voice format, and means designed for use in a physical store. This makes it possible to quickly and appropriately respond to user problems and questions, accurately grasp the user's emotional state, provide appropriate responses, and quickly respond to emergencies.

[1174] "Telephone communication means" refers to a means by which a user communicates with a server via voice, and includes mobile terminals and landlines.

[1175] "Voice input means" refers to a device or function for collecting the user's spoken voice.

[1176] "Speech recognition means" refers to a technology or device that analyzes collected speech and converts it into text data.

[1177] "Emotion analysis means" refers to a technique or device for analyzing text data and voice data and estimating the user's emotional state.

[1178] A "response generator" is a system or device that generates an appropriate response based on the estimated emotional state and text data.

[1179] A "speech synthesis means" is a technique or device for converting a generated text response into speech.

[1180] The "audio output means" refers to a technique or device for playing back the voice generated by the voice synthesis means to the user.

[1181] The "emergency detection means" is a technology or device for determining an emergency based on the content of a user's speech or emotional state.

[1182] "Emergency response means" refers to a technology or device that automatically notifies a specialized agency when an emergency is determined by the emergency detection means.

[1183] The "means installed on the smart device" refers to an application or system that provides customer support in a physical store through voice interaction in response to user questions.

[1184] "Means designed for use in a physical store" refers to a system or application with various support functions intended for use in a physical store.

[1185] An embodiment of the present invention will be described.

[1186] System Overview

[1187] The server includes a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means. The system is installed on a smart device and provides customer support through voice dialogue in a physical store. The system is designed for use in a physical store.

[1188] Hardware used

[1189] Smartphones: Works with Android and iOS.

[1190] Server: Operated in a cloud or on-premise environment.

[1191] Software used

[1192] Speech recognition library: Google Cloud Speech-to-Text API

[1193] Sentiment analysis engine: IBM Watson Tone Analyzer

[1194] Response generation method: GPT-3 (OpenAI)

[1195] Speech synthesis engine: Google Cloud Text-to-Speech API

[1196] Data processing and calculation

[1197] The operation of this system is configured as follows.

[1198] 1. User voice input

[1199] The user speaks their question or inquiry into their smartphone, for example, "Please tell me about the features of this product."

[1200] 2. Voice Recognition

[1201] Voice input is captured through the smartphone's microphone and converted to text data using the Google Cloud Speech-to-Text API.

[1202] 3. Emotion analysis

[1203] The resulting text data is sent to IBM Watson Tone Analyzer, which analyzes the user's emotional state and assigns emotion tags such as "excitement" or "interest."

[1204] 4. Response Generation

[1205] Based on the results of sentiment analysis and text data, GPT-3 generates an appropriate response. The following prompt sentence is input into the generative AI model:

[1206] User's query: "What are the features of this product?"

[1207] Detected emotion: "Excited"

[1208] Generate a suitable response for the user considering their emotional state.

[1209] 5. Speech Synthesis

[1210] The generated text response is converted to audio using the Google Cloud Text-to-Speech API.

[1211] 6. Audio Output

[1212] The synthesized response is played back through the smartphone's speaker and conveyed to the user.

[1213] Specific examples

[1214] scenario

[1215] If a user is using their smartphone in a physical store, opens the "Customer Support Concierge" application, and asks, "What are the features of this product?"

[1216] The server uses a speech recognition engine to convert this question into text,

[1217] Using a sentiment analysis engine, it detects when a user is feeling "excited" and

[1218] GPT-3 generates a response like, "This product is the latest model and has the following features..."

[1219] Finally, the speech synthesis engine converts the response into speech and responds to the user on their smartphone.

[1220] In this way, the "Customer Support Concierge" based on the technology of this invention combines voice recognition and emotion analysis to provide high-quality customer support in real time within the store. This allows for quick and appropriate responses to user problems and questions, while also accurately grasping the user's emotional state and providing an appropriate response. It also allows for quick response in emergencies. These functions allow users to use the service with peace of mind.

[1221] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1222] Step 1:

[1223] The user speaks their question or inquiry into the smartphone. For example, the user might say, "Please tell me about the features of this product." This voice input is captured by the smartphone's microphone.

[1224] Step 2:

[1225] The captured voice data is sent to the server, which then uses the Google Cloud Speech-to-Text API to convert the voice data into text. The input is voice data, and the output is text data. The specific operation of this conversion is to analyze the voice waveform and output the corresponding text.

[1226] Step 3:

[1227] The converted text data is sent to IBM Watson Tone Analyzer. The server uses this API to analyze the emotional state from the text data. The input is text data, and the output is an emotion tag. Specifically, it analyzes the content, context, and wording of the text, and assigns emotion tags such as "excitement," "interest," and "anxiety."

[1228] Step 4:

[1229] The server passes the results of the sentiment analysis and text data to GPT-3, which then generates an appropriate response. The input is the sentiment tag and text data, and the output is the response text. At this stage, the following prompt sentence is input to the generative AI model:

[1230] User's query: "What are the features of this product?"

[1231] Detected emotion: "Excited"

[1232] Generate a suitable response for the user considering their emotional state.

[1233] Specifically, GPT-3 generates the optimal response in natural language based on this prompt.

[1234] Step 5:

[1235] The server sends the generated response text to the Google Cloud Text-to-Speech API. The input is the response text, and the output is the audio data. Specifically, the API analyzes the text and synthesizes a natural-sounding voice corresponding to it.

[1236] Step 6:

[1237] The generated voice data is sent to the smartphone, which then plays the voice through its speaker, allowing the user to hear the appropriate response.

[1238] Step 7:

[1239] If the user has any further questions or inquiries, the process is repeated again from step 1.

[1240] This allows the system to respond to user problems and questions in real time, analyze their emotional state to provide appropriate responses, and take appropriate action in emergency situations.

[1241] 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.

[1242] 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.

[1243] 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.

[1244] [Fourth embodiment]

[1245] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1246] 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.

[1247] 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).

[1248] 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.

[1249] 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.

[1250] 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).

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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."

[1258] The present invention is implemented by a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[1259] System Overview

[1260] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using speech recognition means. The text data is analyzed for the user's emotions using emotion analysis means, and an appropriate response is generated by generative AI. The generated response is converted into voice using speech synthesis means and played back to the user. Furthermore, if the user's comments or emotional state indicate an emergency, the server notifies specialized agencies.

[1261] Natural language explanation of program processing

[1262] Users Place Calls

[1263] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[1264] Playing a welcome message

[1265] The server automatically plays an IVR message such as, "Welcome to the suicide prevention consultation center. Please tell us your concerns."

[1266] User input of consultation content

[1267] The user speaks about their worries. For example, the user might say, "My work isn't going well, and life is hard."

[1268] Receives the user's voice data

[1269] The server collects the user's speech and obtains it as voice data in real time.

[1270] Speech to text

[1271] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[1272] Emotion analysis using emotion analysis tools

[1273] The server sends the text data to the emotion analysis engine, which analyzes the user's emotions. The emotion generation engine then reads the emotion "despair" from the text.

[1274] Appropriate response generation by response generation means

[1275] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[1276] Response synthesis and playback

[1277] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[1278] Continue or end the user interaction

[1279] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[1280] Emergency detection and response

[1281] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[1282] The above is an embodiment of the present invention. To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer the call. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and convey it to the user via voice. If the situation is deemed an emergency, the system will immediately notify a specialist institution. In this way, the present invention provides 24-hour support for users' mental anxieties.

[1283] The processing flow will be explained below.

[1284] Step 1:

[1285] The user calls the consultation number using a smartphone or landline.

[1286] Step 2:

[1287] The server receives the call and establishes the connection.

[1288] Step 3:

[1289] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[1290] Step 4:

[1291] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[1292] Step 5:

[1293] The server collects the user's voice data in real time.

[1294] Step 6:

[1295] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[1296] Step 7:

[1297] The server transmits the text data to the emotion analysis means.

[1298] Step 8:

[1299] A sentiment analysis means analyzes the text data and estimates the user's emotional state (e.g., despair, sadness).

[1300] Step 9:

[1301] The server sends the emotion analysis results to the generative AI.

[1302] Step 10:

[1303] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[1304] Step 11:

[1305] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[1306] Step 12:

[1307] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[1308] Step 13:

[1309] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[1310] Step 14:

[1311] The server collects the audio data again and repeats the process from step 5 to step 12.

[1312] Step 15:

[1313] The server detects emergencies based on the user's speech and emotional state.

[1314] Step 16:

[1315] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[1316] Step 17:

[1317] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[1318] Example 1

[1319] 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."

[1320] In today's world, many people are experiencing stress and worries in their daily lives and at work, and this is increasingly leading to mental health problems. Conventional systems have limitations in providing prompt and appropriate responses to these problems. In particular, it has been difficult to notify specialized agencies in a timely manner for users at risk of suicide or experiencing serious mental difficulties.

[1321] 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.

[1322] In this invention, the server includes telephone communication means, voice input means for collecting a user's voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state, response generation means for generating an appropriate response based on the user's emotional state estimated by the emotion analysis means, voice synthesis means for converting the generated response into voice format, voice output means for playing back to the user the voice response generated by the voice synthesis means, means for collecting user utterances in real time and acquiring them as voice data, means for analyzing the voice data in real time and generating an appropriate response, means for verifying the generated response before playing it back, emergency detection means for detecting an emergency, and emergency response means for automatically notifying a specialist institution if the emergency detection means determines that an emergency has occurred, thereby enabling a prompt and appropriate response to the user's mental health problem.

[1323] "Telephone communication means" refers to the means by which users connect to the system using smartphones or landlines.

[1324] The "voice input means" refers to a device or program for collecting user utterances as voice data.

[1325] "Speech recognition means" refers to a technology or program for converting collected voice data into text data.

[1326] "Emotion analysis means" refers to analytical techniques or programs for estimating a user's emotional state from text data.

[1327] The "response generation means" refers to a technique or program for generating an appropriate response based on the estimated emotional state of the user.

[1328] A "speech synthesis means" is a technology or program that converts the generated text response into speech form.

[1329] The "audio output means" refers to a device or program for playing back the generated audio response to the user.

[1330] The "means for acquiring as voice data" is a means for collecting user utterances in real time and saving them in voice data format.

[1331] "Means for analyzing in real time" refers to technology or programs for analyzing collected voice data in real time and generating a response.

[1332] The "means for verifying" is a means for checking the content of the generated response to ensure that it is appropriate before playing it back.

[1333] "Emergency detection means" refers to technology or programs for detecting an emergency from the content of a user's statements and emotional state.

[1334] "Emergency response tools" are technologies or programs that automatically notify specialized agencies in response to a detected emergency.

[1335] The present invention is embodied in a system including means for telephone communication, means for voice input, means for voice recognition, means for emotion analysis, means for response generation, means for voice synthesis, means for voice output, means for emergency detection, and means for emergency response.

[1336] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays a welcome message to the user using an interactive voice response system (IVR), while identifying the user's phone number and establishing a line to accept the call.

[1337] The initial message played by the server is something like, "Welcome to the Suicide Prevention Counseling Center. Please tell us about your worries." This is achieved by loading and playing an audio file from the automated voice response system. When a user speaks about their worries, the audio is collected using a microphone and temporarily saved as digital audio data. For example, a user might say, "My work isn't going well, and life is hard."

[1338] Next, the server uses a speech recognition engine such as Google Cloud Speech-to-Text API to convert the collected voice data into text data. For example, a statement such as "My job isn't going well, and life is hard" is converted into text. This text data is sent to the Microsoft Azure Text Analytics sentiment analysis engine, which analyzes the user's emotions. The sentiment analysis engine then identifies the emotion "despair" from the text.

[1339] Based on the results of the sentiment analysis, the server generates an appropriate response using a generative AI such as an OpenAI model. For example, it generates a response such as, "That must be a very painful experience. I understand how you feel." This response generation process includes embedding the user's statement and the sentiment analysis results into a prompt sentence and sending it to the AI ​​model. An example of a prompt sentence to input to the generative AI model is, "The user said, 'I've been having a lot of failures at work recently, and life is hard.' Please generate a sympathetic and supportive response for this user."

[1340] The server converts the generated text response into speech using a speech synthesis engine such as Amazon Polly and plays it back to the user, so that if the user wants to continue speaking, the system can collect the audio again and repeat the process of generating a response.

[1341] The system also detects and responds to emergencies. The server determines whether an emergency has occurred based on the user's emotional state and the content of their comments, and automatically contacts specialized agencies if necessary. For example, if a user makes a comment indicating a risk of suicide, the server will automatically notify the emergency response team, enabling a more rapid response.

[1342] With the above configuration, the present invention can provide a prompt and appropriate response to a user's mental health problems.

[1343] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1344] Step 1:

[1345] A user calls a specific consultation number using a smartphone or landline.

[1346] Input: User's phone number, call start signal

[1347] Output: Call connection establishment signal

[1348] What happens: The server identifies the user's phone number and establishes a line to accept the call.

[1349] Step 2:

[1350] The server plays a welcome message using an interactive voice response system (IVR).

[1351] Input: Call connection establishment signal

[1352] Output: Initial message sent

[1353] Specific operation: The server plays an IVR message such as "Welcome to the suicide prevention consultation center. Please tell us your concerns," so it loads an audio file in the IVR system and plays it to the user.

[1354] Step 3:

[1355] The user speaks out their concerns.

[1356] Input: User's voice data

[1357] Output: Collected audio data

[1358] Specific operation: A microphone collects the user's speech and temporarily stores it as digital audio data. For example, the user might say, "My work isn't going well, and life is hard."

[1359] Step 4:

[1360] The server captures the user's voice utterances in real time.

[1361] Input: Collected audio data

[1362] Output: Temporarily saved audio data

[1363] Specific operation: The collected voice data is stored on the server in a specific format (e.g., WAV format) for subsequent voice recognition processing.

[1364] Step 5:

[1365] The server uses a speech recognition engine to convert the collected voice data into text data.

[1366] Input: Temporarily saved audio data

[1367] Output: Text data

[1368] Specific operation: Voice data is sent to a speech recognition engine such as Google Cloud Speech-to-Text API, and the server retrieves the returned text data. For example, it may obtain text data such as "My work is not going well, and life is hard."

[1369] Step 6:

[1370] The server sends the text data to an emotion analysis engine to analyze the user's emotions.

[1371] Input: Text data

[1372] Output: Emotion analysis data (e.g., despair)

[1373] Specific operation: Text data is sent to the Microsoft Azure Text Analytics sentiment analysis engine, and the server retrieves the returned sentiment data (e.g., "despair").

[1374] Step 7:

[1375] The server uses generative AI to generate a response appropriate for the user based on the results of emotion analysis.

[1376] Input: Sentiment analysis data, text data

[1377] Output: Response text data

[1378] Specific operation: The results of the emotion analysis and the user's statement are embedded in a prompt, which is then sent to a generative AI model to generate a response. For example, the model can generate a response such as, "That must be a very painful experience. I understand how you feel."

[1379] Step 8:

[1380] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[1381] Input: Response text data

[1382] Output: Audio data

[1383] Specific operation: Sends the response text data to a speech synthesis engine such as Amazon Polly, and plays the returned audio data to the user.

[1384] Step 9:

[1385] If the user wishes to continue speaking, the system repeats the process from voice collection to response generation.

[1386] Input: The user's new voice data

[1387] Output: New collected voice data, response data

[1388] Specific operation: Repeatedly executes steps from collecting new voice to generating a response, continuing the dialogue with the user.

[1389] Step 10:

[1390] The server detects emergencies and automatically notifies specialist agencies if necessary.

[1391] Input: User sentiment analysis data or statements

[1392] Output: Emergency notification signal

[1393] What it does: If a user makes a statement that indicates a risk of suicide, it automatically notifies emergency response teams.

[1394] (Application example 1)

[1395] 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."

[1396] In modern society, it is important for individuals facing emergencies to receive prompt and appropriate responses. However, conventional systems that rely only on telephones or voice input are unable to adequately analyze emotions or detect emergencies, making it difficult to provide individuals with appropriate responses or emergency measures. For this reason, there is a demand for systems that can respond promptly and appropriately to users' concerns and emergencies.

[1397] 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.

[1398] In this invention, the server includes a telephone communication means, a voice input means for collecting a user's voice, a voice recognition means for converting the collected voice into text, a feeling analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, a response generation means for generating an appropriate response based on the estimated user's emotional state, a voice synthesis means for converting the generated response into a voice format, a voice output means for playing the generated voice response to the user, an emergency detection means for detecting an emergency, an emergency response means for automatically notifying a specialist institution when an emergency is determined, and a means for operating as a device including a program for processing information obtained from the voice input and generating an appropriate response. This makes it possible to analyze the user's emotional state and provide a prompt and appropriate response, as well as to respond quickly to emergencies by automatically notifying a specialist institution.

[1399] "Telephone communication means" is a general term for devices and methods for making voice calls using telephone lines.

[1400] "Voice input means" refers to hardware or software for capturing the user's voice and converting it into digital data.

[1401] "Speech recognition means" is a technology that analyzes collected voice data and converts it into text data.

[1402] "Emotion analysis means" refers to algorithms and technologies for estimating a user's emotional state based on text data.

[1403] A "response generation means" refers to a technology or system for generating an appropriate response based on the results of sentiment analysis.

[1404] A "speech synthesis means" is a technology that converts the generated text response into speech form.

[1405] The "audio output means" refers to a device or technology for playing back the voice generated by the voice synthesis means to the user.

[1406] "Emergency detection means" refers to systems or technologies for detecting emergencies from the content of user comments and emotional state.

[1407] "Emergency response measures" refer to methods or systems that automatically notify specialized agencies when an emergency is deemed to have occurred.

[1408] "Means operating as a device containing a program" means a system or apparatus that allows a device containing a program to process information obtained from voice input and generate an appropriate response to perform the function of the device.

[1409] This invention relates to a system in which, when a user calls a specific consultation number using a smartphone or landline, a server receives the call and analyzes the user's emotions and emergency situations from the voice. This system uses voice recognition and emotion analysis to understand the user's concerns, generate an appropriate response, and, if necessary, take emergency action.

[1410] First, when a user calls the dedicated consultation number using a smartphone or landline, the server receives the call and establishes a connection. The server automatically plays an opening message such as "Welcome to the consultation center. Please tell us about your concerns." When the user speaks about their concerns, the audio is collected by the server.

[1411] The collected voice is converted into text data by a speech recognition means. This speech recognition uses the widely used "speech_recognition" library. The converted text data is sent to an emotion analysis means, where the user's emotions are analyzed. This emotion analysis uses the "EmotionAnalyzer" API.

[1412] Based on the results of the sentiment analysis, the server generates an appropriate response using the response generation means. This response generation uses the "TextGenerator" API. The generated response is converted into audio format by the speech synthesis means and played back to the user. This speech synthesis uses the "pyttsx3" library.

[1413] Furthermore, an emergency detection mechanism is implemented to detect emergencies from the user's speech and emotional state. If an emergency is detected, the server automatically notifies specialized agencies using emergency response mechanisms. This notification is performed using the "EmergencyNotifier" class.

[1414] For example, if a user says, "I've been having a lot of failures at work lately, and life is tough," the system will analyze the statement and generate an appropriate response, such as, "That must be a very tough experience. I understand how you feel." Similarly, if a user says, "I'm scared someone is breaking into my house right now," the system will immediately notify specialized authorities using emergency response methods.

[1415] An example of a prompt sentence is: "User: 'I'm scared that someone is breaking into my house. What should I do?' System response: 'Please move to the nearest safe place immediately and call the police. I will also call emergency services.'"

[1416] The above is an embodiment of the present invention. This system can provide safety and a sense of security by quickly and appropriately responding to user concerns and emergencies.

[1417] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1418] Step 1:

[1419] The user calls the consultation number using a smartphone or landline.

[1420] Input: User's voice data

[1421] Output: Connected call session

[1422] Specific operation: When a user makes a call, the server receives the call and establishes a call connection.

[1423] Step 2:

[1424] The server plays a welcome message.

[1425] Input: Connected call session

[1426] Output: The initial message that is played

[1427] Specific operation: The server plays an automated voice message such as "Welcome to the consultation center. Please tell us your concerns."

[1428] Step 3:

[1429] The user speaks out their concerns.

[1430] Input: Voice input by the user after receiving the initial message from the server

[1431] Output: User's voice data

[1432] Specific operation: The user speaks about their concerns into the microphone, and the voice data is collected by the server.

[1433] Step 4:

[1434] The server collects the voice data and converts it into text using a voice recognition means.

[1435] Input: User's voice data

[1436] Output: Text data

[1437] Specific operation: The server uses the "speech_recognition" library to convert the user's voice data into text format.

[1438] Step 5:

[1439] The server analyzes the text data using emotion analysis means.

[1440] Input: Text data

[1441] Output: Sentiment analysis result data

[1442] Specific operation: The server uses the "EmotionAnalyzer" API to analyze text data and estimate the user's emotional state.

[1443] Step 6:

[1444] The server generates a response based on the sentiment analysis results.

[1445] Input: Sentiment analysis result data

[1446] Output: The generated response text

[1447] Specific operation: The server uses the "TextGenerator" API to generate a response appropriate for the user based on the results of sentiment analysis.

[1448] Step 7:

[1449] The server converts the generated response into speech using a speech synthesis means.

[1450] Input: Generated response text

[1451] Output: Audio data

[1452] What it does: The server uses the "pyttsx3" library to convert the generated text response into audio format.

[1453] Step 8:

[1454] The server plays the audio data to the user.

[1455] Input: Audio data

[1456] Output: The audio message the user hears

[1457] What happens: The server streams the audio data to the user's phone and plays it back.

[1458] Step 9:

[1459] The server detects emergencies based on the user's speech and emotional state.

[1460] Input: Sentiment analysis results and speech content

[1461] Output: Emergency detection result

[1462] Specific operation: The server uses the emergency detection means to analyze the user's comments and emotional state and determine whether or not an emergency has occurred.

[1463] Step 10:

[1464] If the server is determined to be in an emergency, emergency response measures will be used to automatically notify specialized agencies.

[1465] Input: Emergency detection result

[1466] Output: Automatic notification message

[1467] Specific operation: The server uses the "EmergencyNotifier" class to automatically send a notification to specialized agencies when an emergency response is required.

[1468] This series of processes allows users to receive a prompt and appropriate response simply by consulting using a smartphone or landline, and in the event of an emergency, appropriate emergency response will be provided.

[1469] 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.

[1470] The present invention is embodied by a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine that recognizes the emotions of a user.

[1471] System Overview

[1472] When a user calls a specific consultation number using a smartphone or landline, the server receives the call and plays an initial message to the user using an interactive voice response system (IVR). When the user talks about their concerns, the voice is collected by the server and converted into text using a speech recognition system. The text data is analyzed for the user's emotions using an emotion analysis system and an emotion engine, and an appropriate response is generated by a generative AI system. The generated response is converted into speech using a speech synthesis system and played back to the user. Furthermore, if the content of the user's comments or emotional state indicates an emergency, the server notifies a specialist agency. The emotion engine also works in cooperation with the emotion analysis system to more accurately recognize emotions by referring not only to the text data but also to the tone and speed of the voice and past consultation history, thereby optimizing the response.

[1473] Natural language explanation of program processing

[1474] Users Place Calls

[1475] The user calls a specific consultation number using a smartphone or landline, and the server receives the call and establishes a connection.

[1476] Playing a welcome message

[1477] The server automatically plays an IVR message saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[1478] User input of consultation content

[1479] The user talks about their worries over the phone, for example, "My work isn't going well and life is hard."

[1480] Receives the user's voice data

[1481] The server collects the user's speech and obtains it as voice data in real time.

[1482] Speech to text

[1483] The server uses a speech recognition engine to convert the voice data into text data. For example, a statement such as "My work is not going well, and life is hard" is converted into text.

[1484] Emotion analysis using emotion analysis methods and emotion engines

[1485] The server sends both text and voice data to the emotion analysis means and emotion engine to analyze the user's emotions. The emotion engine analyzes not only the text data but also the tone and speed of the voice to improve the accuracy of emotion recognition. It also references past consultation history to optimize responses.

[1486] Appropriate response generation by response generation means

[1487] The server uses generative AI to generate a response appropriate to the user based on the results of emotion analysis, such as "That must be a very painful experience. I understand how you feel."

[1488] Response synthesis and playback

[1489] The server converts the generated text response into speech using a speech synthesis engine and plays it back to the user.

[1490] Continue or end the user interaction

[1491] If the user wishes to continue speaking, the system will collect audio again at their request and repeat the process of generating a response.

[1492] Emergency detection and response

[1493] The server determines whether an emergency situation exists based on the user's emotional state and the content of their comments, and automatically contacts appropriate authorities if necessary. For example, if a user makes a comment indicating a risk of suicide, the server automatically notifies the emergency response team.

[1494] To give a specific example, if a user calls a dedicated consultation number from their smartphone late at night, the system will automatically answer. If the user says, "I've been failing at work lately, and life is hard," the server will analyze the statement, generate an appropriate response, and deliver it to the user via voice. The emotion engine will also analyze the tone and speed of the user's voice to provide a more accurate response. If it is determined to be an emergency, the system will immediately notify a specialist agency. In this way, the present invention provides 24-hour support for users' mental anxieties.

[1495] The processing flow will be explained below.

[1496] Step 1:

[1497] The user calls the consultation number using a smartphone or landline.

[1498] Step 2:

[1499] The server receives the call and establishes the connection.

[1500] Step 3:

[1501] The server initializes the interactive voice response system (IVR) and plays the welcome message, "Welcome to the suicide prevention counseling center. Please tell us your concerns."

[1502] Step 4:

[1503] The user talks about their worries over the phone. For example, they might say, "My work isn't going well, and life is hard."

[1504] Step 5:

[1505] The server collects the user's voice data in real time.

[1506] Step 6:

[1507] The server uses a speech recognition engine to convert the collected voice data into text data. For example, the speech "My work is not going well, and life is hard" is converted into text "My work is not going well, and life is hard."

[1508] Step 7:

[1509] The server sends the text data to the emotion analysis means and the emotion engine.

[1510] Step 8:

[1511] An emotion analyzer and emotion engine analyzes the text and audio data (including tone and rate) to infer the user's emotional state (e.g., despair, sadness).

[1512] Step 9:

[1513] The emotion engine refers to past consultation history to recognize emotional states and optimize responses.

[1514] Step 10:

[1515] The server sends the emotion analysis results to the generative AI.

[1516] Step 11:

[1517] Based on the user's emotional state, the generative AI generates an appropriate response in text form, such as "That must be a very painful experience. I understand how you feel."

[1518] Step 12:

[1519] The server passes the generated text response to a speech synthesis engine to generate an audio file.

[1520] Step 13:

[1521] The server plays an audio file and tells the user, "That must be a very painful experience. We understand how you feel."

[1522] Step 14:

[1523] If the user speaks again, for example, he continues to ask, "I really don't know what to do."

[1524] Step 15:

[1525] The server collects the audio data again and repeats the process from step 5 to step 13.

[1526] Step 16:

[1527] The server detects emergencies based on the user's speech and emotional state.

[1528] Step 17:

[1529] If the emergency detection means determines that an emergency has occurred, for example, if a person says, "I want to end everything now," the server will notify a specialist agency.

[1530] Step 18:

[1531] The server executes emergency response procedures and automatically notifies emergency response teams or specialized consultants.

[1532] Example 2

[1533] 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."

[1534] In modern society, the number of people suffering from mental distress is increasing, and in particularly serious cases, many people consider suicide. It is important to provide a system that can respond quickly and appropriately to such emergencies. However, conventional consultation systems often have difficulty accurately grasping the user's emotional state and are unable to generate an appropriate response. They also lack the functionality to detect emergencies and notify specialized institutions. This presents a challenge in that they are unable to effectively support users with their concerns.

[1535] 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 a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, an emergency response means, and an emotion engine. This enables highly accurate emotion recognition by collecting the user's voice in real time, converting it into text by voice recognition, and analyzing the text and the tone and speed of the voice. Furthermore, the emotion engine can also refer to past consultation history to improve the appropriateness of the response. Furthermore, by detecting an emergency and automatically notifying a specialized institution, the user's safety can be quickly ensured.

[1536] "Telephone communication means" is a general term for hardware and software for making voice calls, and is compatible with both mobile terminals and landlines.

[1537] "Audio input means" refers to a microphone and associated hardware and software for collecting the user's voice.

[1538] "Speech recognition means" refers to technology or devices for converting collected voice data into text, and includes, for example, a voice recognition engine.

[1539] The "emotion analysis means" refers to a technique or device for analyzing the consultation content converted into text by the voice recognition means and estimating the user's emotional state.

[1540] The "response generation means" refers to a technique or device for generating an appropriate response based on the emotional state of the user estimated by the emotion analysis means, and includes generative artificial intelligence.

[1541] A "voice synthesis means" is a technique or device for converting a generated response into voice form.

[1542] "Audio output means" refers to a speaker and associated hardware and software for playing back to the user the audio response generated by the speech synthesis means.

[1543] "Emergency detection means" refers to technology or devices for determining an emergency from the user's statements and emotional state.

[1544] "Emergency response means" refers to technology or devices that automatically notify specialized agencies when an emergency is determined by the emergency detection means.

[1545] An "emotion engine" is a technology or device that analyzes the tone and speed of speech to improve the accuracy of emotion analysis, and also references past consultation history.

[1546] The present invention is embodied in a system including a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, an emotion engine, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means.

[1547] First, when a user calls a specific consultation number using a smartphone or landline, the server receives the call and establishes a connection. The telephone communication method used here is a general term for the hardware and software used to make voice calls, and is compatible with both mobile devices and landlines.

[1548] Next, the server automatically plays an initial message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us about your concerns." This is done using a voice output means.

[1549] When a user speaks about their concerns, the voice is collected in real time by the server via a voice input means, i.e., a microphone and associated hardware and software.

[1550] The voice data collected by the server is converted into text data using a voice recognition tool (e.g., Google Cloud Speech-to-Text). The voice recognition tool here refers to technology or equipment for converting voice data into text.

[1551] Next, the server sends both the text data and the voice data to an emotion analysis means (e.g., IBM Watson Tone Analyzer) and an emotion engine to analyze the user's emotions. An emotion analysis means refers to technology or equipment that analyzes the consultation content converted into text by a voice recognition means and estimates the user's emotional state. The emotion engine analyzes the tone and speed of the voice and also refers to past consultation history to improve the accuracy of emotion recognition.

[1552] Once the sentiment analysis is complete, the server generates an appropriate response using generative AI (e.g., OpenAI GPT-4). The response generation means is a technology or device that generates an appropriate response based on the sentiment analysis results. The generated response is converted into speech using a speech synthesis means (e.g., Amazon Polly). The speech synthesis means is a technology or device that converts the generated response into speech format.

[1553] The server then plays back the generated voice response. If the user wants to continue speaking, the system collects the voice again and repeats the same process. If an emergency is detected, the server automatically notifies a specialist agency. Emergency detection means are technologies or devices that determine an emergency from the user's speech or emotional state, and emergency response means are technologies or devices that automatically notify a specialist agency when an emergency is determined by the emergency detection means.

[1554] As a concrete example, consider the case where a user calls a dedicated consultation number from their smartphone late at night. The system automatically answers the call, and if the user says, "I've been having a lot of failures at work lately, and life is hard," the server analyzes the statement and generates an appropriate response, "That must be a very painful experience. I understand how you feel," which is conveyed to the user via voice. Furthermore, the emotion engine analyzes the tone and speed of the user's voice to provide a more accurate response. If the call is deemed an emergency, specialized agencies are immediately notified.

[1555] Examples of prompt sentences are as follows:

[1556] User: "I've been failing at work lately and life is hard."

[1557] Server: "That must be a very painful experience. I understand how you feel."

[1558] This allows the system of the present invention to provide support for users' mental anxieties 24 hours a day.

[1559] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1560] Step 1:

[1561] The user calls a specific consultation number using a smartphone or landline. The server receives the call and establishes a connection. The input is the user's call operation, and the output is the establishment of the call. At this time, the server initializes the communication protocol using the telephone communication means.

[1562] Step 2:

[1563] The server plays a welcome message via an interactive voice response system (IVR) saying, "Welcome to the suicide prevention counseling center. Please tell us your concerns." The input is the establishment of a telephone connection, and the output is the audio playback of the welcome message. The server plays a pre-recorded message from the audio output means.

[1564] Step 3:

[1565] The user talks about their worries over the phone, for example, "I've been failing at work a lot lately, and life is hard." The input is the user's voice, and the output is passed to the server as voice data. At this stage, the voice input means captures what the user is saying.

[1566] Step 4:

[1567] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text) to convert the collected voice data into text data. The input is voice data, and the output is text data. The server performs the text conversion of the voice data through an API call.

[1568] Step 5:

[1569] The server sends both text and voice data to an emotion analysis tool (e.g., IBM Watson Tone Analyzer) and an emotion engine to analyze the user's emotions. The input is text and voice data, and the output is data indicating the user's emotional state. The emotion engine improves the accuracy of the analysis by referring to the tone and speed of the voice and past consultation history.

[1570] Step 6:

[1571] The server uses a generative AI (e.g., OpenAI GPT-4) to generate an appropriate response based on the results of emotion analysis. The input is data indicating the emotional state, and the output is a response text. An API is used to send prompts to the generative AI and receive the response text.

[1572] Step 7:

[1573] The server converts the generated response text into speech using a speech synthesis engine (e.g., Amazon Polly) and plays it back to the user. The input is the response text and the output is audio data. The speech synthesis engine converts the text into audio format, which the server plays back.

[1574] Step 8:

[1575] If the user wants to continue speaking, the server collects the audio again and repeats the process to generate a new response: the input is new audio data, the output is a new response, and the system continues the loop.

[1576] Step 9:

[1577] The server determines an emergency based on the user's emotional state and speech content, and automatically notifies specialized organizations. The input is emotion analysis data, and the output is an emergency notification. The server makes a judgment using emergency detection means, and contacts specialized organizations via emergency response means.

[1578] (Application example 2)

[1579] 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."

[1580] Conventional customer support systems often struggle to provide prompt and appropriate responses when users have specific problems or questions. They also struggle to accurately grasp the user's emotional state and provide appropriate responses. Furthermore, it is difficult to quickly detect emergencies and respond appropriately, which prevents users from achieving satisfactory results or a sense of security.

[1581] 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 telephone communication means, voice input means for collecting user voice, voice recognition means for converting the collected voice into text, emotion analysis means for analyzing the text-converted consultation content and estimating the user's emotional state, response generation means for generating an appropriate response based on the estimated user's emotional state, voice synthesis means for converting the generated response into voice format, voice output means for playing the voice response to the user, emergency detection means for detecting an emergency, emergency response means for automatically notifying a specialist agency when an emergency is determined, means installed on a smart device for providing customer support through voice dialogue in a physical store, response generation means for generating appropriate responses to user questions, voice synthesis means for converting the responses into voice format, and means designed for use in a physical store. This makes it possible to quickly and appropriately respond to user problems and questions, accurately grasp the user's emotional state, provide appropriate responses, and quickly respond to emergencies.

[1582] "Telephone communication means" refers to a means by which a user communicates with a server via voice, and includes mobile terminals and landlines.

[1583] "Voice input means" refers to a device or function for collecting the user's spoken voice.

[1584] "Speech recognition means" refers to a technology or device that analyzes collected speech and converts it into text data.

[1585] "Emotion analysis means" refers to a technique or device for analyzing text data and voice data and estimating the user's emotional state.

[1586] A "response generator" is a system or device that generates an appropriate response based on the estimated emotional state and text data.

[1587] A "speech synthesis means" is a technique or device for converting a generated text response into speech.

[1588] The "audio output means" refers to a technique or device for playing back the voice generated by the voice synthesis means to the user.

[1589] The "emergency detection means" is a technology or device for determining an emergency based on the content of a user's speech or emotional state.

[1590] "Emergency response means" refers to a technology or device that automatically notifies a specialized agency when an emergency is determined by the emergency detection means.

[1591] The "means installed on the smart device" refers to an application or system that provides customer support in a physical store through voice interaction in response to user questions.

[1592] "Means designed for use in a physical store" refers to a system or application with various support functions intended for use in a physical store.

[1593] An embodiment of the present invention will be described.

[1594] System Overview

[1595] The server includes a telephone communication means, a voice input means, a voice recognition means, an emotion analysis means, a response generation means, a voice synthesis means, a voice output means, an emergency detection means, and an emergency response means. The system is installed on a smart device and provides customer support through voice dialogue in a physical store. The system is designed for use in a physical store.

[1596] Hardware used

[1597] Smartphones: Works with Android and iOS.

[1598] Server: Operated in a cloud or on-premise environment.

[1599] Software used

[1600] Speech recognition library: Google Cloud Speech-to-Text API

[1601] Sentiment analysis engine: IBM Watson Tone Analyzer

[1602] Response generation method: GPT-3 (OpenAI)

[1603] Speech synthesis engine: Google Cloud Text-to-Speech API

[1604] Data processing and calculation

[1605] The operation of this system is configured as follows.

[1606] 1. User voice input

[1607] The user speaks their question or inquiry into their smartphone, for example, "Please tell me about the features of this product."

[1608] 2. Voice Recognition

[1609] Voice input is captured through the smartphone's microphone and converted to text data using the Google Cloud Speech-to-Text API.

[1610] 3. Emotion analysis

[1611] The resulting text data is sent to IBM Watson Tone Analyzer, which analyzes the user's emotional state and assigns emotion tags such as "excitement" or "interest."

[1612] 4. Response Generation

[1613] Based on the results of sentiment analysis and text data, GPT-3 generates an appropriate response. The following prompt sentence is input into the generative AI model:

[1614] User's query: "What are the features of this product?"

[1615] Detected emotion: "Excited"

[1616] Generate a suitable response for the user considering their emotional state.

[1617] 5. Speech Synthesis

[1618] The generated text response is converted to audio using the Google Cloud Text-to-Speech API.

[1619] 6. Audio Output

[1620] The synthesized response is played back through the smartphone's speaker and conveyed to the user.

[1621] Specific examples

[1622] scenario

[1623] If a user is using their smartphone in a physical store, opens the "Customer Support Concierge" application, and asks, "What are the features of this product?"

[1624] The server uses a speech recognition engine to convert this question into text,

[1625] Using a sentiment analysis engine, it detects when a user is feeling "excited" and

[1626] GPT-3 generates a response like, "This product is the latest model and has the following features..."

[1627] Finally, the speech synthesis engine converts the response into speech and responds to the user on their smartphone.

[1628] In this way, the "Customer Support Concierge" based on the technology of this invention combines voice recognition and emotion analysis to provide high-quality customer support in real time within the store. This allows for quick and appropriate responses to user problems and questions, while also accurately grasping the user's emotional state and providing an appropriate response. It also allows for quick response in emergencies. These functions allow users to use the service with peace of mind.

[1629] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1630] Step 1:

[1631] The user speaks their question or inquiry into the smartphone. For example, the user might say, "Please tell me about the features of this product." This voice input is captured by the smartphone's microphone.

[1632] Step 2:

[1633] The captured voice data is sent to the server, which then uses the Google Cloud Speech-to-Text API to convert the voice data into text. The input is voice data, and the output is text data. The specific operation of this conversion is to analyze the voice waveform and output the corresponding text.

[1634] Step 3:

[1635] The converted text data is sent to IBM Watson Tone Analyzer. The server uses this API to analyze the emotional state from the text data. The input is text data, and the output is an emotion tag. Specifically, it analyzes the content, context, and wording of the text, and assigns emotion tags such as "excitement," "interest," and "anxiety."

[1636] Step 4:

[1637] The server passes the results of the sentiment analysis and text data to GPT-3, which then generates an appropriate response. The input is the sentiment tag and text data, and the output is the response text. At this stage, the following prompt sentence is input to the generative AI model:

[1638] User's query: "What are the features of this product?"

[1639] Detected emotion: "Excited"

[1640] Generate a suitable response for the user considering their emotional state.

[1641] Specifically, GPT-3 generates the optimal response in natural language based on this prompt.

[1642] Step 5:

[1643] The server sends the generated response text to the Google Cloud Text-to-Speech API. The input is the response text, and the output is the audio data. Specifically, the API analyzes the text and synthesizes a natural-sounding voice corresponding to it.

[1644] Step 6:

[1645] The generated voice data is sent to the smartphone, which then plays the voice through its speaker, allowing the user to hear the appropriate response.

[1646] Step 7:

[1647] If the user has any further questions or inquiries, the process is repeated again from step 1.

[1648] This allows the system to respond to user problems and questions in real time, analyze their emotional state to provide appropriate responses, and take appropriate action in emergency situations.

[1649] 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.

[1650] 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.

[1651] 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 robot 414.

[1652] 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.

[1653] FIG. 9 illustrates 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 behaviors 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.

[1654] 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.

[1655] 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).

[1656] 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.

[1657] 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."

[1658] 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.

[1659] 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).

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] The following is further disclosed regarding the above embodiment.

[1671] (Claim 1)

[1672] Telephone communication means;

[1673] a voice input means for collecting a user's voice;

[1674] a speech recognition means for converting collected speech into text;

[1675] emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the emotional state of the user;

[1676] a response generating means for generating an appropriate response based on the emotional state of the user estimated by the emotion analyzing means;

[1677] a speech synthesis means for converting the generated response into speech form;

[1678] a voice output means for reproducing the voice response generated by the voice synthesis means to the user;

[1679] emergency detection means for detecting an emergency;

[1680] an emergency response means for automatically notifying a specialized agency when the emergency detection means determines that an emergency has occurred;

[1681] A system including:

[1682] (Claim 2)

[1683] 10. The system of claim 1, wherein the telephony means supports both mobile terminals and landlines.

[1684] (Claim 3)

[1685] 10. The system of claim 1, wherein the sentiment analysis means utilizes generative artificial intelligence.

[1686] "Example 1"

[1687] (Claim 1)

[1688] Telephone communication means;

[1689] a voice input means for collecting a user's voice;

[1690] a speech recognition means for converting collected speech into text;

[1691] emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the emotional state of the user;

[1692] a response generating means for generating an appropriate response based on the emotional state of the user estimated by the emotion analyzing means;

[1693] a speech synthesis means for converting the generated response into speech form;

[1694] a voice output means for reproducing the voice response generated by the voice synthesis means to the user;

[1695] A means for collecting user speech content in real time and acquiring it as voice data;

[1696] means for analyzing the voice data in real time and generating an appropriate response;

[1697] a means for validating the generated response before playing it;

[1698] emergency detection means for detecting an emergency;

[1699] an emergency response means for automatically notifying a specialized agency when the emergency detection means determines that an emergency has occurred;

[1700] A system including:

[1701] (Claim 2)

[1702] 10. The system of claim 1, wherein the telephony means supports both mobile terminals and landlines.

[1703] (Claim 3)

[1704] 10. The system of claim 1, wherein the sentiment analysis means utilizes generative artificial intelligence.

[1705] "Application Example 1"

[1706] (Claim 1)

[1707] Telephone communication means;

[1708] a voice input means for collecting a user's voice;

[1709] a speech recognition means for converting collected speech into text;

[1710] emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the emotional state of the user;

[1711] a response generating means for generating an appropriate response based on the emotional state of the user estimated by the emotion analyzing means;

[1712] a speech synthesis means for converting the generated response into speech form;

[1713] a voice output means for reproducing the voice response generated by the voice synthesis means to the user;

[1714] emergency detection means for detecting an emergency;

[1715] an emergency response means for automatically notifying a specialized agency when the emergency detection means determines that an emergency has occurred;

[1716] means for operating as a device containing a program for processing information obtained from the voice input and generating an appropriate response;

[1717] A system including:

[1718] (Claim 2)

[1719] 10. The system of claim 1, wherein the telephony means supports both mobile terminals and landlines.

[1720] (Claim 3)

[1721] 10. The system of claim 1, wherein the sentiment analysis means and response generation means utilize generative artificial intelligence to generate prompt sentences that provide appropriate responses to the user.

[1722] "Example 2: Combining Emotion Engines"

[1723] (Claim 1)

[1724] Telephone communication means;

[1725] a voice input means for collecting a user's voice;

[1726] a speech recognition means for converting collected speech into text;

[1727] emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the emotional state of the user;

[1728] a response generating means for generating an appropriate response based on the emotional state of the user estimated by the emotion analyzing means;

[1729] a speech synthesis means for converting the generated response into speech form;

[1730] a voice output means for reproducing the voice response generated by the voice synthesis means to the user;

[1731] emergency detection means for detecting an emergency;

[1732] an emergency response means for automatically notifying a specialized agency when the emergency detection means determines that an emergency has occurred;

[1733] An emotion engine that analyzes the tone and speed of the voice and also references past consultation history to improve the accuracy of emotion analysis;

[1734] A system including:

[1735] (Claim 2)

[1736] 10. The system of claim 1, wherein the telephony means supports both mobile terminals and landlines.

[1737] (Claim 3)

[1738] 10. The system of claim 1, wherein the sentiment analysis means utilizes generative artificial intelligence.

[1739] "Application example 2 when combining emotion engines"

[1740] (Claim 1)

[1741] Telephone communication means;

[1742] a voice input means for collecting a user's voice;

[1743] a speech recognition means for converting collected speech into text;

[1744] emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the emotional state of the user;

[1745] a response generating means for generating an appropriate response based on the emotional state of the user estimated by the emotion analyzing means;

[1746] a speech synthesis means for converting the generated response into speech form;

[1747] a voice output means for reproducing the voice response generated by the voice synthesis means to the user;

[1748] emergency detection means for detecting an emergency;

[1749] an emergency response means for automatically notifying a specialized agency when the emergency detection means determines that an emergency has occurred;

[1750] A means to provide customer support through voice interaction within a physical store, installed on a smart device;

[1751] a response generating means for generating an appropriate response to a user's question by the customer support means;

[1752] a speech synthesis means for converting the response into speech form;

[1753] A means designed for use in a physical store;

[1754] A system including:

[1755] (Claim 2)

[1756] 10. The system of claim 1, wherein the telephony means supports both mobile terminals and landlines.

[1757] (Claim 3)

[1758] 10. The system of claim 1, wherein the sentiment analysis means utilizes generative artificial intelligence. [Explanation of symbols]

[1759] 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. Telephone communication means; a voice input means for collecting a user's voice; a speech recognition means for converting collected speech into text; emotion analysis means for analyzing the consultation content converted into text by the voice recognition means and estimating the emotional state of the user; a response generating means for generating an appropriate response based on the emotional state of the user estimated by the emotion analyzing means; a speech synthesis means for converting the generated response into speech form; a voice output means for reproducing the voice response generated by the voice synthesis means to the user; emergency detection means for detecting an emergency; an emergency response means for automatically notifying a specialized agency when the emergency detection means determines that an emergency has occurred; A system including:

2. 2. The system of claim 1, wherein the telephone communication means supports both mobile terminals and landlines.

3. 10. The system of claim 1, wherein the sentiment analysis means utilizes generative artificial intelligence.

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