System

Abstract

A system is provided.SOLUTION: This system is provided with a means for receiving voice data, a means for converting the received voice data into text data, a means for generating interaction contents based on the text data, a means for outputting the generated interaction contents as voice, and a means for changing the kind of the voice based on the setting of a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention aims to solve health problems that are becoming more apparent with the increasing number of elderly people, particularly the risk of age-related hearing loss and dementia. Conventional television viewing methods may increase the risk of dementia, and the lack of communication with elderly people with hearing loss is also a problem. [Means for solving the problem]

[0005] The present invention provides a system including means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, and means for changing the type of voice based on user settings. This system allows elderly people to actively engage in dialogue while watching television, thereby simultaneously achieving dementia prevention and hearing assistance.

[0006] "Audio data" refers to digitized audio data, typically obtained from a microphone or recording device.

[0007] "Means for receiving" refers to the equipment and technology used to acquire audio data and process it within the system.

[0008] "Means for converting into text data" refers to voice recognition technology or software for converting voice data into text information.

[0009] The "means for generating dialogue content" refers to an algorithm or system for creating appropriate responses from text data to engage in dialogue with a user.

[0010] "Means for outputting as voice" refers to speech synthesis technology or devices for playing back text data or generated dialogue content as a voice signal from a speaker or the like.

[0011] "Means for changing the type of voice based on user settings" refers to a system or software that has the function of changing the characteristics of the voice (for example, pitch or speaking style) according to the user's preferences.

[0012] "Television audio data" refers to audio data acquired from television programs or broadcasts.

[0013] "Content aimed at preventing dementia" refers to information and dialogue designed to help users reduce their risk of developing dementia. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0022] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] The present invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life. Specific embodiments of the present invention will be described below.

[0036] First, the system has a means for receiving voice data. The server receives the TV's voice data and stores it. Next, the terminal has a means for converting the received voice data into text data. The terminal uses voice recognition technology to analyze the voice data and generate text data.

[0037] The generated text data is processed by a dialogue content generation means. The terminal generates an appropriate response for dialogue with the user based on the text data. In this case, the response generation algorithm has multiple response patterns and randomly selects one to achieve a natural dialogue.

[0038] The system also includes a means for outputting the generated dialogue content as voice. The terminal converts the text data into voice using speech synthesis technology and outputs it to the user. The user can listen to this voice through a speaker.

[0039] The terminal also includes a means for changing the type of voice based on user settings. The user can select the type of voice (e.g., a child's voice or a grandchild's voice) on the settings screen, and the terminal changes the voice synthesis parameters according to the settings.

[0040] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." The device then generates a response such as "That's interesting!" and outputs the response using a speech synthesis engine. This speech is output in a child's voice, allowing the user to enjoy active interaction while watching television.

[0041] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content, allowing the user to constantly enjoy new dialogue and potentially helping to prevent dementia and assist hearing loss.

[0042] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0043] The processing flow will be explained below.

[0044] Step 1:

[0045] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. This is the audio data recorded while the user was watching TV.

[0046] Step 2:

[0047] The device retrieves the stored voice data and initializes the speech recognition engine, for example, using the speech_recognition library.

[0048] Step 3:

[0049] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. The voice recognition engine analyzes the voice data and generates text data, e.g., "Today's weather is sunny."

[0050] Step 4:

[0051] The device generates dialogue content based on the generated text data. The dialogue generation algorithm selects an appropriate response from multiple response patterns, e.g., "That's interesting!"

[0052] Step 5:

[0053] The device initializes the speech synthesis engine and converts the generated dialogue into speech. For example, it uses the pyttsx3 library to synthesize speech in the specified voice type (e.g., a child's voice).

[0054] Step 6:

[0055] The device outputs the synthesized voice to the user through a speaker, allowing the user to enjoy a conversation with the AI ​​while watching TV.

[0056] Step 7:

[0057] The user selects the type of voice on the settings screen. Based on this, the device changes the parameters of the speech synthesis engine and outputs the next conversation voice in the type of voice the user desires (e.g., the voice of a grandchild).

[0058] Example 1

[0059] 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."

[0060] There is a lack of systems that can assist elderly people in hearing and stimulate communication when watching television. As a result, elderly people often feel lonely, and problems such as the progression of dementia and a decline in quality of life (QOL) occur. Conventional systems lack the ability to interact while watching television, and do not adequately assist hearing and mental health.

[0061] 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.

[0062] In this invention, the server includes means for receiving voice data, means for saving the received voice data, means for converting the voice data into text data using voice recognition technology, means for generating dialogue content based on the text data using a generative AI model, means for outputting the generated dialogue content as voice, and means for changing the type of voice based on user settings. This makes it possible to prevent dementia and assist hearing through dialogue with elderly people watching television.

[0063] "Audio data" refers to the digital or analog format of audio signals output from televisions and other audio equipment.

[0064] "Means for receiving" refers to a device or method for acquiring audio data, examples of which include a microphone or data streaming technology.

[0065] "Storage means" refers to a device or method for storing received audio data, examples of which include a database or cloud storage.

[0066] "Speech recognition technology" refers to technology that analyzes voice data and converts it into text data, and examples include speech recognition APIs.

[0067] "Text data" refers to character string information obtained by analyzing voice data.

[0068] A "generative AI model" refers to a machine learning algorithm that generates natural-sounding dialogue based on input text data.

[0069] "Means for generating dialogue content" refers to a method or device that uses a generative AI model to create appropriate dialogue based on text data.

[0070] "Means for outputting as audio" refers to a method or device for converting the generated dialogue content into an audio format and delivering it to the user via a speaker or the like.

[0071] "Means for changing the voice type based on user settings" refers to a method or device that adjusts the voice output according to the voice parameters (e.g., voice type) desired by the user.

[0072] This invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life (QOL). Specific embodiments of the invention are described below.

[0073] First, the server has a means to receive audio data. The server uses a microphone or data streaming technology to acquire audio data output from a television or other audio equipment. For example, it can receive audio data in the format "tv_audio_sample.wav" from a microphone connected to a television.

[0074] Next, the server stores the received audio data. For storage, it uses a database or cloud storage (e.g., AWS (registered trademark) S3) to temporarily store the received audio data. The server stores the audio file in AWS S3 and records the file name and metadata in the database.

[0075] The device then retrieves the voice data file stored on the server and converts the voice data into text data using voice recognition technology, such as the Google® Cloud Speech-to-Text API. The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate the text data "Today's weather is sunny."

[0076] Next, the device uses a generative AI model to generate dialogue content based on the text data. In this process, appropriate dialogue content can be obtained by inputting a prompt sentence into the generative AI model (e.g., OpenAI (registered trademark) GPT-4 (registered trademark)). The device sends a prompt to the generative AI model: "Text data: 'The weather is sunny today.' Please generate a response to this." The model generates a response: "That's interesting!"

[0077] The device outputs the generated dialogue as voice. Using speech synthesis technology (e.g., Amazon Polly), the text data is converted into voice and delivered to the user through the speaker. The device uses Amazon Polly to convert the text "That's interesting!" into voice data, which is then output through the speaker in a child's voice.

[0078] Furthermore, the user can select the type of voice (e.g., child's voice, grandchild's voice) on the settings screen. The device changes the voice synthesis parameters according to the setting. If the user selects "grandchild's voice" on the settings screen, the device sets the parameters of the voice synthesis engine to "grandchild's voice" and generates voices using those parameters from the next conversation.

[0079] For example, the server receives and saves an audio file called "tv_audio_sample.wav." The device analyzes this audio file using the Google Cloud Speech-to-Text API and generates text data such as "The weather is sunny today." The device then inputs the prompt text "Text data: 'The weather is sunny today.' Please generate a response to this." into the generative AI model, which generates the response "That's interesting!" This response is then converted into speech using Amazon Polly and output from the speaker in a child's voice.

[0080] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, generates dialogue content, and outputs it as voice. This allows the user to constantly enjoy new dialogue, and can help prevent dementia and assist hearing loss.

[0081] This system allows elderly people to not only watch television, but also have an active, interactive experience, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0082] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0083] Program processing flow

[0084] Step 1:

[0085] The server receives the audio data.

[0086] Input: Audio signal from a TV or audio device

[0087] Processing: Convert the audio signal into digital format and send it to the server

[0088] Output: Digital audio data file (e.g. "tv_audio_sample.wav")

[0089] How it works: The server collects audio data using a microphone connected to the TV, converts it into digital format, and stores it.

[0090] Step 2:

[0091] The server stores the received audio data.

[0092] Input: Digital audio data file (e.g. "tv_audio_sample.wav")

[0093] Processing: Store the audio data in cloud storage (e.g. AWS S3) and record the metadata in a database.

[0094] Output: Audio data file saved on cloud storage

[0095] Specific operation: The server uploads "tv_audio_sample.wav" to AWS S3 and records the file metadata (e.g. timestamp, file size) in the database.

[0096] Step 3:

[0097] The terminal acquires the voice data file stored on the server and converts the voice data into text data using voice recognition technology.

[0098] Input: Audio data files stored in cloud storage

[0099] Processing: Analyzes the audio data using speech recognition technology (e.g., Google Cloud Speech-to-Text API) and generates text data.

[0100] Output: Converted text data (e.g. "Today's weather is sunny")

[0101] Specific operation: The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate text data saying "Today's weather is sunny."

[0102] Step 4:

[0103] The device uses a generative AI model to generate dialogue content based on the text data.

[0104] Input: Text data (e.g., "Today's weather is sunny")

[0105] Processing: Input a prompt into a generative AI model (e.g., OpenAI GPT-4) to generate appropriate dialogue.

[0106] Output: The generated dialogue (e.g., "That's interesting!")

[0107] Specific operation: The device sends the prompt "Text data: 'The weather is sunny today.' Please generate a response to this." to the generative AI model, which then generates the response "That's interesting!"

[0108] Step 5:

[0109] The terminal outputs the generated dialogue content as voice.

[0110] Input: The generated dialogue (e.g., "That's interesting!")

[0111] Processing: Convert text data into speech using speech synthesis technology (e.g., Amazon Polly)

[0112] Output: Audio data (e.g., audio generated from a child's voice)

[0113] What it does: The device converts the text "That's interesting!" into speech data using Amazon Polly and outputs it through the speaker in a child's voice.

[0114] Step 6:

[0115] The user selects the type of voice (e.g., child's voice, grandchild's voice) on the settings screen.

[0116] Input: The voice type selected by the user in the settings screen

[0117] Processing: Change the settings of the speech synthesis engine based on the selected voice parameters

[0118] Output: Updated audio parameters

[0119] Specific operation: When the user selects "Grandchild's voice" on the settings screen, the device changes the Amazon Polly parameters to "Grandchild's voice" and reflects this setting from the next conversation.

[0120] Step 7:

[0121] The server and the terminal work together to continuously receive, analyze, and generate dialogue content while the user is watching television.

[0122] Input: New audio data received periodically

[0123] Processing: Repeat steps 1 to 6

[0124] Output: Continuously generated dialogue and its audio output

[0125] Specific operation: The server periodically receives new voice data, and the terminal analyzes the data in real time, generates dialogue content, and continuously outputs it to the user as voice.

[0126] This series of processes allows users to enjoy continuous interaction while watching television, making it possible to prevent dementia and assist hearing.

[0127] (Application example 1)

[0128] 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."

[0129] Preventing dementia and improving the quality of life of elderly people is important when they spend time alone. However, for elderly people with impaired vision or hearing, communication through television and other forms of entertainment can be difficult. Safety issues also exist, such as the inability to respond appropriately in the event of a sudden illness or accident. Therefore, technological solutions are needed to enable elderly people to live independently and with peace of mind.

[0130] 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.

[0131] In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on user settings, and means for monitoring the voice and activity of the elderly person and notifying them when an abnormality is detected. This enables elderly people to live a safe and fulfilling life through dialogue to prevent dementia, even when they are spending time alone.

[0132] "Audio data" is a general term for digital or analog data that records audio.

[0133] "Text data" refers to data that is generated by analyzing voice data and expressing it as a string of characters.

[0134] "Dialogue content" refers to the content of the conversation between a human and a system, generated based on text data.

[0135] "Means for receiving" refers to a device or software that acquires audio data and passes it to an application.

[0136] "Conversion means" refers to a function or device that converts voice data into text data.

[0137] "Generation means" refers to a function or device for generating dialogue content based on text data.

[0138] "Output means" refers to a function or device for reproducing the generated dialogue content as voice.

[0139] "Means for changing the type" refers to a function or device for changing the characteristics of the voice (for example, voice quality or speaking style) according to the user's settings.

[0140] "Monitoring means" refers to functions or devices that constantly observe the sounds and activities around the elderly person and collect data.

[0141] "Means for detecting abnormalities" refers to functions or devices that recognize unusual sounds or behavioral patterns and determine whether an abnormality has occurred.

[0142] "Means of notification" refers to functions or devices that promptly alert family members or emergency services when an abnormality is detected.

[0143] This invention provides an interactive assistant system for preventing dementia and ensuring the safety of elderly people while they are alone. This system has the functions of receiving voice data, converting it to text data, generating dialogue content, synthesizing voice, changing the type of voice based on user settings, and detecting and notifying abnormalities in the elderly.

[0144] The server is equipped with a microphone to receive audio data such as environmental sounds and the voices of elderly people. This audio data is then transmitted to the server via devices such as smart glasses or head-mounted displays.

[0145] The received voice data is then converted into text data using voice recognition technology, such as Google Speech Recognition API or IBM Watson (registered trademark).

[0146] A dialogue with the user is then generated based on the text data. A generative AI model is used to generate the dialogue, resulting in natural responses. The generated responses are randomly selected from multiple patterns, providing a more human-like dialogue.

[0147] The generated dialogue is converted into speech using speech synthesis technology. Speech synthesis engines such as gTTS (Google Text-to-Speech) and Amazon Polly are used for speech synthesis. This speech is output to the elderly through the device's speaker.

[0148] Additionally, it includes the ability to change the voice type based on user preferences: users can select the voice type (e.g., a child's voice or a grandchild's voice) in the settings screen, and the voice synthesis parameters will automatically adjust accordingly.

[0149] The system also constantly monitors the elderly person's voice and behavior to detect any abnormalities. For example, if the system detects an abnormal voice (such as a cry for help) or activity (such as the sound of someone falling), it will immediately notify family members or emergency services. Notification methods include email, SMS, and phone call.

[0150] In a specific scenario, if an elderly person says, "I'm a little tired today," the system converts this into text data and returns a generated response such as, "That's tough. It would be good for you to get some rest." An example of the prompt sentence in this case is as follows:

[0151] "When an elderly person says, 'I feel a little tired today,' the assistant responds, 'That's tough. It would be good for you to get some rest.'"

[0152] This system can help elderly people live safe and secure lives even when they are alone, and at the same time contribute to the prevention of dementia.

[0153] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0154] Step 1:

[0155] The server receives the voice data. When the user speaks, the voice data is picked up through the microphone of the device. The picked up voice data is sent to the server in real time.

[0156] Input: User's voice data

[0157] Data processing: Collection of voice data

[0158] Output: Collected audio data

[0159] Specific operation: When a user says, "I'm a little tired today," the voice is picked up by the device's microphone.

[0160] Step 2:

[0161] The server converts the received voice data into text data using voice recognition technology. The server uses voice recognition software such as Google Speech Recognition API or IBM Watson to analyze the voice data and generate text data.

[0162] Input: Received audio data

[0163] Data processing: Converting voice data into text

[0164] Output: Generated text data

[0165] Specific operation: The server receives the voice data "I'm a little tired today" and converts it into text data "I'm a little tired today."

[0166] Step 3:

[0167] The server generates dialogue content based on the text data, using a generative AI model to achieve natural dialogue. The server inputs the text data as a prompt, and generates the dialogue content based on the response obtained from the generative AI model.

[0168] Input: Converted text data

[0169] Data processing: Dialogue content generation using generative AI models

[0170] Output: Generated dialogue

[0171] Specific operation: The server inputs the text data "I'm a little tired today" as a prompt and generates the dialogue content "That's tough. It would be good for you to take a short rest."

[0172] Step 4:

[0173] The device outputs the generated dialogue content as voice. The server sends the generated dialogue content as text data to the device, which then converts it into voice data using a speech synthesis engine such as gTTS or Amazon Polly and outputs it.

[0174] Input: Text data of the generated dialogue

[0175] Data processing: Converting text data into speech

[0176] Output: Audio data

[0177] Specific operation: The device converts the text data "That's tough, you should take a short rest" into speech and outputs it through the speaker.

[0178] Step 5:

[0179] The device adjusts voice parameters based on the voice type selected by the user. The device checks the user's settings and adjusts the gTTS or Amazon Polly voice synthesis parameters to output the voice type desired by the user (for example, a child's voice or a grandchild's voice).

[0180] Input: User's voice settings

[0181] Data processing: Adjustment of speech synthesis parameters

[0182] Output: Modified audio data

[0183] Specific operation: When the user selects "Grandchild's voice" on the device's settings screen, the device adjusts the parameters of the voice synthesis engine to match the grandchild's voice and outputs the corresponding voice.

[0184] Step 6:

[0185] The server monitors the elderly person's voice and activity and notifies them if it detects any abnormalities. The server constantly analyzes voice data and notifies family members and emergency services if it detects any abnormal voice or activity.

[0186] Input: Elderly person's voice and activity data

[0187] Data processing: Analysis using anomaly detection algorithms

[0188] Output: Notification data (email, SMS, phone)

[0189] Specific operation: If the server detects an abnormal voice saying "help," it will immediately send an email notification to family members.

[0190] 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.

[0191] This invention aims to prevent dementia and improve quality of life by providing hearing assistance and activating communication while the elderly are watching television. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more appropriate and personalized dialogue.

[0192] The overall system configuration is as follows:

[0193] First, the server has a means for receiving audio data. It receives audio data (e.g., tv_audio_sample.wav) recorded while a user is watching television via the Internet and saves it in a specified directory.

[0194] Next, the terminal has a means for converting the received voice data into text data. The terminal analyzes the voice data using a voice recognition engine and generates text data. The generated text data is processed by a means for generating dialogue content. The terminal selects an appropriate response from a plurality of response patterns and realizes a dialogue with the user.

[0195] Furthermore, the system is equipped with an emotion engine that can recognize the user's emotions by analyzing the user's tone of voice, facial expressions, or biometric signals to determine whether the user is enjoying or dissatisfied.

[0196] The system also includes a means to tailor dialogue based on the perceived emotion: for example, if the user is determined to be sad, the dialogue may be altered to provide an encouraging response.

[0197] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[0198] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content. This allows the user to constantly enjoy new dialogue, making it possible to prevent dementia and assist hearing. The emotion engine provides dialogue that takes the user's emotions into account, increasing user satisfaction.

[0199] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0200] The processing flow will be explained below.

[0201] Step 1:

[0202] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. The audio data was recorded while the user was watching TV.

[0203] Step 2:

[0204] The device retrieves the stored voice data and initializes the speech recognition engine (e.g., the speech_recognition library), which analyzes the voice data and prepares it for conversion to text data.

[0205] Step 3:

[0206] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. For example, it generates text data such as "Today's weather is sunny."

[0207] Step 4:

[0208] The device inputs the generated text data into a dialogue content generation engine to generate appropriate dialogue content. The dialogue content generation engine selects an appropriate response pattern from multiple response patterns and generates the response text "That's interesting!" as an example.

[0209] Step 5:

[0210] The device initializes the emotion engine and analyzes the user's emotions. The emotion engine obtains the user's tone of voice and facial expressions through the camera and microphone and determines the user's emotions.

[0211] Step 6:

[0212] The emotion engine adjusts the dialogue content based on the user's emotions. For example, if it determines that the user is having fun, the dialogue content will be positive, such as "That's great!"

[0213] Step 7:

[0214] The device converts the adjusted dialogue into speech using a speech synthesis engine (e.g., the pyttsx3 library), which uses a voice type (e.g., a child's voice) based on the user's settings.

[0215] Step 8:

[0216] The terminal outputs the generated voice to the user through a speaker, and the user can enjoy active dialogue while watching television by listening to the adjusted dialogue voice.

[0217] Step 9:

[0218] The user can select the type of voice on the settings screen. The user settings are sent to the server and are reflected in the next and subsequent conversations.

[0219] Step 10:

[0220] The above process is carried out continuously, allowing users to enjoy constantly new conversations while receiving dementia prevention and hearing support. The emotion engine also takes the user's emotions into consideration, increasing user satisfaction.

[0221] Example 2

[0222] 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."

[0223] There is a lack of technology that can assist elderly people in hearing loss and stimulate communication while watching television, aiming to prevent dementia and improve their quality of life. Conventional systems have difficulty providing dialogue that takes into account the user's emotions, and are unable to generate dialogue that is appropriate for each individual user.

[0224] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for recognizing the user's emotion, and means for adjusting the dialogue content based on the recognized emotion. This allows elderly people to enjoy dialogue that suits their individual emotions while watching television, thereby preventing dementia and improving their quality of life.

[0225] The "means for receiving voice data" refers to technology including a communication device and software for receiving voice data sent from a user.

[0226] The "means for converting voice data into text data" includes voice recognition technology and software that analyzes received voice data and converts it into corresponding text data.

[0227] "Means for generating dialogue content" refers to a generative AI model or dialogue generation engine for creating dialogue content based on text data generated from voice data.

[0228] The "means for outputting the generated dialogue content as voice" refers to a voice synthesis technology for converting text data into voice data and providing it to the user through a voice output device.

[0229] The "means for changing the type of voice" is a software function for changing the characteristics of the voice to be output (e.g., type of voice, pitch) according to the user's settings.

[0230] "Means for recognizing the user's emotions" refers to emotion recognition technology or software that analyzes the user's tone of voice, facial expressions, biometric signals, etc. to determine the user's emotional state.

[0231] The "means for adjusting the dialogue content based on the recognized emotion" is a dialogue adjustment technique for appropriately correcting or adjusting the dialogue content generated in accordance with the emotional state of the user.

[0232] "Audio data from a video device" refers to an audio signal output from a television or other audiovisual device.

[0233] "Content aimed at preventing dementia" refers to dialogue and information provision designed to maintain or improve the user's cognitive function.

[0234] The present invention relates to a system that provides hearing assistance and stimulates communication while elderly people watch television, aiming to prevent dementia and improve their quality of life. This system is characterized by its ability to consistently perform processes from receiving and analyzing voice data, generating dialogue content, and outputting the voice, to adjusting the dialogue based on the user's emotions. A specific embodiment of this system is described below.

[0235] First, the server receives audio data. The audio data is recorded while the user is watching TV and is sent to the server via the Internet. As an example, consider the audio file "tv_audio_sample.wav". This audio file is saved in a specified directory on the server.

[0236] Next, the device receives the voice data received from the server. The device analyzes the voice data using a voice recognition engine (e.g., Google Speech-to-Text API). Once the voice data is analyzed, text data is generated as a result. For example, the audio file "tv_audio_sample.wav" is converted into text data such as "The weather is sunny today."

[0237] After the text data is generated, the device uses a dialogue generation engine (e.g., GPT-3 (registered trademark)) to generate appropriate dialogue content. The generated dialogue content is saved as text, for example, in the form of "That's great!"

[0238] The device also incorporates an emotion recognition engine (e.g., IBM Watson Tone Analyzer) that recognizes the user's emotions. This engine analyzes the user's tone of voice, facial expressions, and biometric signals to determine their emotional state, such as whether they are enjoying themselves or feeling dissatisfied. For example, if it determines that the user is happy, it adjusts the dialogue content generated based on that result. Specifically, if the user is happy, positive dialogue content is selected.

[0239] Finally, the generated dialogue is converted into speech by a speech synthesis engine. The device outputs this speech through a speaker. The type of voice can be changed based on the user's settings. For example, the user can select a child's voice to output the response.

[0240] This system allows elderly people to enjoy constantly new dialogues, helping to prevent dementia and assisting hearing. In addition, the emotion engine provides dialogue that takes the user's emotions into account, improving user satisfaction.

[0241] Specific examples

[0242] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[0243] Prompt Sentence Examples

[0244] The voice data "tv_audio_sample.wav" recorded while the user was watching TV was received. The text obtained using the speech recognition engine is "It's a nice day today." In this case, if the user's emotion is recognized as good mood, please show the appropriate response to generate.

[0245] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0246] Step 1: Receiving and storing audio data

[0247] Input: Audio data recorded while the user is watching TV (e.g., tv_audio_sample.wav)

[0248] Processing: The server receives the audio data via the Internet.

[0249] Specific behavior:

[0250] The user records audio through a dedicated microphone.

[0251] The recorded audio data is sent from the terminal to the server.

[0252] The server detects the data reception trigger, captures the connected data stream, and saves it as a file.

[0253] Output: Audio data file saved in the specified directory on the server (e.g. / path / to / tv_audio_sample.wav)

[0254] Step 2: Analyzing the audio data and generating text data

[0255] Input: Audio data file saved on the server (e.g., / path / to / tv_audio_sample.wav)

[0256] Processing: The device uses a speech recognition engine to analyze the voice data and generate text data.

[0257] Specific behavior:

[0258] The terminal downloads the audio data from the specified directory on the server.

[0259] Initialize the speech recognition engine (e.g., Google Speech-to-Text API) and begin analyzing the audio data.

[0260] Once the analysis is complete, text data is generated and saved in temporary memory or as a file.

[0261] Output: Generated text data (e.g., "Today's weather is sunny")

[0262] Step 3: Dialogue generation and response selection

[0263] Input: Generated text data (e.g., "Today's weather is sunny")

[0264] Processing: The device uses a dialogue generation engine to generate appropriate dialogue content and selects the most appropriate response from multiple response candidates.

[0265] Specific behavior:

[0266] The terminal creates a prompt based on the generated text data.

[0267] The prompt is fed into a generative AI model (e.g., GPT-3) to generate an appropriate response.

[0268] Select the best response from multiple possible responses and save it as text.

[0269] Output: Selected dialogue text (e.g., "That's great!")

[0270] Step 4: Recognize emotions

[0271] Input: Data such as the user's tone of voice, facial expressions, and biometric signals

[0272] Processing: The device uses an emotion recognition engine to analyze the user's emotions.

[0273] Specific behavior:

[0274] The device collects emotion-related data from the microphone, camera, and biometric sensors.

[0275] An emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes this data to determine the user's emotional state.

[0276] The analysis results are saved in a database.

[0277] Output: Perceived user emotional state (e.g., good mood)

[0278] Step 5: Adjust the conversation

[0279] Input: Recognized user emotional state (e.g., in a good mood), selected dialogue text (e.g., "That's great!")

[0280] Processing: The device adjusts the generated dialogue content based on the recognized emotion.

[0281] Specific behavior:

[0282] The device receives the emotion recognition results and reevaluates the existing response text.

[0283] If necessary, modify your response to make it more appropriate.

[0284] The corrected response is saved as text and used as input data for the speech synthesis engine.

[0285] Output: Adjusted dialogue text (e.g., "That's really great!")

[0286] Step 6: Speech synthesis and response output

[0287] Input: Adjusted dialogue text (e.g., "That's really great!")

[0288] Processing: The device uses a speech synthesis engine to convert the generated text response into speech and output it to the user.

[0289] Specific behavior:

[0290] The device inputs the modified response text into a speech synthesis engine.

[0291] A speech synthesis engine converts the text data into speech data.

[0292] The generated audio is output to a speaker so that the user can hear it.

[0293] Output: A voice response played in a voice you set (e.g., "That's really great!" in a child's voice)

[0294] (Application example 2)

[0295] 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."

[0296] There is a need for methods to prevent elderly people from feeling isolated in their daily lives and to prevent cognitive decline. However, existing systems are unable to communicate with them based on their emotions, making it difficult to respond immediately in emergencies. Furthermore, there is a lack of systems equipped with monitoring functions that can respond immediately to abnormalities or emergencies within the home. As a result, it is difficult to create a safe and secure environment while ensuring the quality of life of the elderly.

[0297] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for analyzing the user's emotions, means for adjusting the dialogue content based on the analyzed emotions, and means for issuing an alarm in an emergency. This enables dialogue that is appropriate for the emotions and situation of the elderly, which not only contributes to maintaining and improving cognitive function but also enables a quick response in an emergency.

[0298] The "means for receiving audio data" refers to a device or program that has the function of taking audio information transmitted from the outside into the device.

[0299] The "means for converting received voice data into text data" refers to a device or program for converting voice information into text information using voice recognition technology.

[0300] The "means for generating dialogue content based on text data" refers to a device or program that automatically generates dialogue content with a user based on the converted character information.

[0301] The "means for outputting the generated dialogue content as voice" refers to a device or program for reproducing the text-based dialogue content using voice synthesis technology.

[0302] The "means for changing the type of voice based on the user's settings" refers to a device or program that adjusts the characteristics of the voice (tone of voice, speed, etc.) that is output in accordance with the settings that the user has specified in advance.

[0303] The "means for analyzing user emotions" refers to a device or program that identifies the user's emotional state from the user's voice or speech content and obtains the results.

[0304] The "means for adjusting the content of the dialogue based on the analyzed emotions" refers to a device or program that changes the content of the dialogue in a timely manner in accordance with the results of analyzing the user's emotions.

[0305] "Means for issuing an alarm in an emergency" refers to a device or program that detects a physical or psychological emergency in a user and issues a prompt warning.

[0306] "Audio data from video equipment" refers to audio information emitted from visual equipment such as televisions and displays.

[0307] "Content aimed at maintaining or improving cognitive function" is interactive content designed to maintain or improve the user's cognitive abilities.

[0308] This invention is a system that aims to maintain and improve cognitive function and ensure safety for elderly people in their daily lives without them feeling isolated. This system provides personalized dialogue to users through a series of processes including receiving and analyzing voice data, generating dialogue, analyzing emotions, and responding to emergencies.

[0309] The server has a means to receive audio data from an external source. For example, it retrieves audio files from the Internet and saves them in a specified directory. This means can be realized by a home device such as a smartphone or smart speaker. A standard HTTP request is used to receive the audio data.

[0310] The voice data stored on the server is converted into text data by a voice recognition engine. Existing voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text can be used for this purpose. The converted text data is used to generate dialogue with the user.

[0311] The device runs an algorithm that generates dialogue content based on the generated text data. The dialogue content is adjusted to match the user's emotions by selecting appropriate response patterns from a variety of options. This process utilizes emotion analysis engines such as EmoPy and Microsoft® Azure® Emotion API.

[0312] The generated dialogue is output as voice using a speech synthesis engine, and the type of voice (tone of voice, speed, etc.) preset by the user is also supported. Google Cloud Text-to-Speech and Amazon Polly can be used for speech synthesis.

[0313] The system also has a means of issuing an alarm in an emergency. If an abnormality is detected in the user's voice or behavior, a notification is sent promptly to family members and medical personnel via the middleware, allowing for a smooth response in an emergency.

[0314] For example, if a user says, "I wonder what the weather will be like today?" the system goes through the following process:

[0315] 1. Receive audio data and save it as "tv_audio_sample.wav".

[0316] 2. The speech recognition engine converts the text into "I wonder what the weather will be like today?"

[0317] 3. The emotion analysis engine determines the user's emotion as "happy."

[0318] 4. Generate the corresponding response and output it as voice using the speech synthesis engine: "It's sunny today, what beautiful weather!"

[0319] Example prompt sentence:

[0320] Determine the emotion of the user's tone of voice and generate an appropriate response.

[0321] Input: "I wonder what the weather will be like today?"

[0322] Emotion: "happy"

[0323] Response: "It's a beautiful sunny day today!"

[0324] This system allows elderly people to enjoy appropriate dialogue that reflects their emotions, reducing feelings of isolation, maintaining and improving cognitive function, and enabling rapid response in emergencies.

[0325] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0326] Step 1:

[0327] The server receives audio data sent from an external source. This process downloads the audio data using an HTTP request and saves it locally as a file. The input is the URL of the audio data, and the output is an audio file (e.g., "tv_audio_sample.wav") saved to the local disk.

[0328] Step 2:

[0329] The server sends the saved audio file to a speech recognition engine, which converts the audio data into text data. This process uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to analyze the audio file and return text. The input is the audio file, and the output is text data (e.g., "I wonder what the weather will be like today?").

[0330] Step 3:

[0331] The device analyzes the text data obtained from the speech recognition engine to determine the user's intentions and emotions. This process uses an emotion analysis engine (e.g., EmoPy) to determine the emotion of the text data. The input is the text data, and the output is the determined emotion information (e.g., "happy").

[0332] Step 4:

[0333] The device generates dialogue content based on the analyzed emotions. This process uses a generative AI model to generate an appropriate response. The input is text data and emotion information, and the output is the generated dialogue content (e.g., "It's sunny today, the weather is very nice!").

[0334] Step 5:

[0335] The device sends the generated dialogue to a speech synthesis engine, which outputs it as speech. This process uses a speech synthesis service (e.g., Google Cloud Text-to-Speech) to convert the text into speech and play it back to the user. The input is the text of the generated dialogue, and the output is the synthesized speech.

[0336] Step 6:

[0337] The device changes the voice type based on the user's settings. This process adjusts the settings of the speech synthesis engine and outputs speech at the tone and speed specified by the user. The input is the user's settings information and the generated dialogue text, and the output is a customized voice.

[0338] Step 7:

[0339] The server performs a process to issue an alarm in the event of an emergency. This process detects abnormalities from the user's voice and behavioral analysis results, issues an alarm, and sends notifications to family members and medical personnel. The input is the user's behavior and voice analysis results, and the output is the issued alarm message.

[0340] Through this series of processing steps, the system provides interactive dialogue based on the user's emotions and situation, enabling them to maintain cognitive function and respond quickly in emergencies.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] [Second embodiment]

[0345] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0346] 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.

[0347] 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).

[0348] 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.

[0349] 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.

[0350] 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).

[0351] 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.

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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."

[0357] The present invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life. Specific embodiments of the present invention will be described below.

[0358] First, the system has a means for receiving voice data. The server receives the TV's voice data and stores it. Next, the terminal has a means for converting the received voice data into text data. The terminal uses voice recognition technology to analyze the voice data and generate text data.

[0359] The generated text data is processed by a dialogue content generation means. The terminal generates an appropriate response for dialogue with the user based on the text data. In this case, the response generation algorithm has multiple response patterns and randomly selects one to achieve a natural dialogue.

[0360] The system also includes a means for outputting the generated dialogue content as voice. The terminal converts the text data into voice using speech synthesis technology and outputs it to the user. The user can listen to this voice through a speaker.

[0361] The terminal also includes a means for changing the type of voice based on user settings. The user can select the type of voice (e.g., a child's voice or a grandchild's voice) on the settings screen, and the terminal changes the voice synthesis parameters according to the settings.

[0362] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." The device then generates a response such as "That's interesting!" and outputs the response using a speech synthesis engine. This speech is output in a child's voice, allowing the user to enjoy active interaction while watching television.

[0363] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content, allowing the user to constantly enjoy new dialogue and potentially helping to prevent dementia and assist hearing loss.

[0364] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0365] The processing flow will be explained below.

[0366] Step 1:

[0367] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. This is the audio data recorded while the user was watching TV.

[0368] Step 2:

[0369] The device retrieves the stored voice data and initializes the speech recognition engine, for example, using the speech_recognition library.

[0370] Step 3:

[0371] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. The voice recognition engine analyzes the voice data and generates text data, e.g., "Today's weather is sunny."

[0372] Step 4:

[0373] The device generates dialogue content based on the generated text data. The dialogue generation algorithm selects an appropriate response from multiple response patterns, e.g., "That's interesting!"

[0374] Step 5:

[0375] The device initializes the speech synthesis engine and converts the generated dialogue into speech. For example, it uses the pyttsx3 library to synthesize speech in the specified voice type (e.g., a child's voice).

[0376] Step 6:

[0377] The device outputs the synthesized voice to the user through a speaker, allowing the user to enjoy a conversation with the AI ​​while watching TV.

[0378] Step 7:

[0379] The user selects the type of voice on the settings screen. Based on this, the device changes the parameters of the speech synthesis engine and outputs the next conversation voice in the type of voice the user desires (e.g., the voice of a grandchild).

[0380] Example 1

[0381] 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."

[0382] There is a lack of systems that can assist elderly people in hearing and stimulate communication when watching television. As a result, elderly people often feel lonely, and problems such as the progression of dementia and a decline in quality of life (QOL) occur. Conventional systems lack the ability to interact while watching television, and do not adequately assist hearing and mental health.

[0383] 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.

[0384] In this invention, the server includes means for receiving voice data, means for saving the received voice data, means for converting the voice data into text data using voice recognition technology, means for generating dialogue content based on the text data using a generative AI model, means for outputting the generated dialogue content as voice, and means for changing the type of voice based on user settings. This makes it possible to prevent dementia and assist hearing through dialogue with elderly people watching television.

[0385] "Audio data" refers to the digital or analog format of audio signals output from televisions and other audio equipment.

[0386] "Means for receiving" refers to a device or method for acquiring audio data, examples of which include a microphone or data streaming technology.

[0387] "Storage means" refers to a device or method for storing received audio data, examples of which include a database or cloud storage.

[0388] "Speech recognition technology" refers to technology that analyzes voice data and converts it into text data, and examples include speech recognition APIs.

[0389] "Text data" refers to character string information obtained by analyzing voice data.

[0390] A "generative AI model" refers to a machine learning algorithm that generates natural-sounding dialogue based on input text data.

[0391] "Means for generating dialogue content" refers to a method or device that uses a generative AI model to create appropriate dialogue based on text data.

[0392] "Means for outputting as audio" refers to a method or device for converting the generated dialogue content into an audio format and delivering it to the user via a speaker or the like.

[0393] "Means for changing the voice type based on user settings" refers to a method or device that adjusts the voice output according to the voice parameters (e.g., voice type) desired by the user.

[0394] This invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life (QOL). Specific embodiments of the invention are described below.

[0395] First, the server has a means to receive audio data. The server uses a microphone or data streaming technology to acquire audio data output from a television or other audio equipment. For example, it can receive audio data in the format "tv_audio_sample.wav" from a microphone connected to a television.

[0396] Next, the server stores the received audio data. To store it, it uses a database or cloud storage (e.g., AWS S3) to temporarily store the received audio data. The server saves the audio file in AWS S3 and records the file name and metadata in the database.

[0397] The device then retrieves the voice data file stored on the server and converts it into text using voice recognition technology, such as the Google Cloud Speech-to-Text API. The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate the text "Today's weather is sunny."

[0398] Next, the device uses a generative AI model to generate dialogue content based on the text data. In this process, appropriate dialogue content can be obtained by inputting a prompt sentence into the generative AI model (e.g., OpenAI GPT-4). The device sends a prompt to the generative AI model: "Text data: 'The weather is sunny today.' Please generate a response to this." The model generates a response: "That's interesting!"

[0399] The device outputs the generated dialogue as voice. Using speech synthesis technology (e.g., Amazon Polly), the text data is converted into voice and delivered to the user through the speaker. The device uses Amazon Polly to convert the text "That's interesting!" into voice data, which is then output through the speaker in a child's voice.

[0400] Furthermore, the user can select the type of voice (e.g., child's voice, grandchild's voice) on the settings screen. The device changes the voice synthesis parameters according to the setting. If the user selects "grandchild's voice" on the settings screen, the device sets the parameters of the voice synthesis engine to "grandchild's voice" and generates voices using those parameters from the next conversation.

[0401] For example, the server receives and saves an audio file called "tv_audio_sample.wav." The device analyzes this audio file using the Google Cloud Speech-to-Text API and generates text data such as "The weather is sunny today." The device then inputs the prompt text "Text data: 'The weather is sunny today.' Please generate a response to this." into the generative AI model, which generates the response "That's interesting!" This response is then converted into speech using Amazon Polly and output from the speaker in a child's voice.

[0402] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, generates dialogue content, and outputs it as voice. This allows the user to constantly enjoy new dialogue, and can help prevent dementia and assist hearing loss.

[0403] This system allows elderly people to not only watch television, but also have an active, interactive experience, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0404] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0405] Program processing flow

[0406] Step 1:

[0407] The server receives the audio data.

[0408] Input: Audio signal from a TV or audio device

[0409] Processing: Convert the audio signal into digital format and send it to the server

[0410] Output: Digital audio data file (e.g. "tv_audio_sample.wav")

[0411] How it works: The server collects audio data using a microphone connected to the TV, converts it into digital format, and stores it.

[0412] Step 2:

[0413] The server stores the received audio data.

[0414] Input: Digital audio data file (e.g. "tv_audio_sample.wav")

[0415] Processing: Store the audio data in cloud storage (e.g. AWS S3) and record the metadata in a database.

[0416] Output: Audio data file saved on cloud storage

[0417] Specific operation: The server uploads "tv_audio_sample.wav" to AWS S3 and records the file metadata (e.g. timestamp, file size) in the database.

[0418] Step 3:

[0419] The terminal acquires the voice data file stored on the server and converts the voice data into text data using voice recognition technology.

[0420] Input: Audio data files stored in cloud storage

[0421] Processing: Analyzes the audio data using speech recognition technology (e.g., Google Cloud Speech-to-Text API) and generates text data.

[0422] Output: Converted text data (e.g. "Today's weather is sunny")

[0423] Specific operation: The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate text data saying "Today's weather is sunny."

[0424] Step 4:

[0425] The device uses a generative AI model to generate dialogue content based on the text data.

[0426] Input: Text data (e.g., "Today's weather is sunny")

[0427] Processing: Input a prompt into a generative AI model (e.g., OpenAI GPT-4) to generate appropriate dialogue.

[0428] Output: The generated dialogue (e.g., "That's interesting!")

[0429] Specific operation: The device sends the prompt "Text data: 'The weather is sunny today.' Please generate a response to this." to the generative AI model, which then generates the response "That's interesting!"

[0430] Step 5:

[0431] The terminal outputs the generated dialogue content as voice.

[0432] Input: The generated dialogue (e.g., "That's interesting!")

[0433] Processing: Convert text data into speech using speech synthesis technology (e.g., Amazon Polly)

[0434] Output: Audio data (e.g., audio generated from a child's voice)

[0435] What it does: The device converts the text "That's interesting!" into speech data using Amazon Polly and outputs it through the speaker in a child's voice.

[0436] Step 6:

[0437] The user selects the type of voice (e.g., child's voice, grandchild's voice) on the settings screen.

[0438] Input: The voice type selected by the user in the settings screen

[0439] Processing: Change the settings of the speech synthesis engine based on the selected voice parameters

[0440] Output: Updated audio parameters

[0441] Specific operation: When the user selects "Grandchild's voice" on the settings screen, the device changes the Amazon Polly parameters to "Grandchild's voice" and reflects this setting from the next conversation.

[0442] Step 7:

[0443] The server and the terminal work together to continuously receive, analyze, and generate dialogue content while the user is watching television.

[0444] Input: New audio data received periodically

[0445] Processing: Repeat steps 1 to 6

[0446] Output: Continuously generated dialogue and its audio output

[0447] Specific operation: The server periodically receives new voice data, and the terminal analyzes the data in real time, generates dialogue content, and continuously outputs it to the user as voice.

[0448] This series of processes allows users to enjoy continuous interaction while watching television, making it possible to prevent dementia and assist hearing.

[0449] (Application example 1)

[0450] 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."

[0451] Preventing dementia and improving the quality of life of elderly people is important when they spend time alone. However, for elderly people with impaired vision or hearing, communication through television and other forms of entertainment can be difficult. Safety issues also exist, such as the inability to respond appropriately in the event of a sudden illness or accident. Therefore, technological solutions are needed to enable elderly people to live independently and with peace of mind.

[0452] 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.

[0453] In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on user settings, and means for monitoring the voice and activity of the elderly person and notifying them when an abnormality is detected. This enables elderly people to live a safe and fulfilling life through dialogue to prevent dementia, even when they are spending time alone.

[0454] "Audio data" is a general term for digital or analog data that records audio.

[0455] "Text data" refers to data that is generated by analyzing voice data and expressing it as a string of characters.

[0456] "Dialogue content" refers to the content of the conversation between a human and a system, generated based on text data.

[0457] "Means for receiving" refers to a device or software that acquires audio data and passes it to an application.

[0458] "Conversion means" refers to a function or device that converts voice data into text data.

[0459] "Generation means" refers to a function or device for generating dialogue content based on text data.

[0460] "Output means" refers to a function or device for reproducing the generated dialogue content as voice.

[0461] "Means for changing the type" refers to a function or device for changing the characteristics of the voice (for example, voice quality or speaking style) according to the user's settings.

[0462] "Monitoring means" refers to functions or devices that constantly observe the sounds and activities around the elderly person and collect data.

[0463] "Means for detecting abnormalities" refers to functions or devices that recognize unusual sounds or behavioral patterns and determine whether an abnormality has occurred.

[0464] "Means of notification" refers to functions or devices that promptly alert family members or emergency services when an abnormality is detected.

[0465] This invention provides an interactive assistant system for preventing dementia and ensuring the safety of elderly people while they are alone. This system has the functions of receiving voice data, converting it to text data, generating dialogue content, synthesizing voice, changing the type of voice based on user settings, and detecting and notifying abnormalities in the elderly.

[0466] The server is equipped with a microphone to receive audio data such as environmental sounds and the voices of elderly people. This audio data is then transmitted to the server via devices such as smart glasses or head-mounted displays.

[0467] The received voice data is then converted into text data using voice recognition technology, such as Google Speech Recognition API or IBM Watson.

[0468] A dialogue with the user is then generated based on the text data. A generative AI model is used to generate the dialogue, resulting in natural responses. The generated responses are randomly selected from multiple patterns, providing a more human-like dialogue.

[0469] The generated dialogue is converted into speech using speech synthesis technology. Speech synthesis engines such as gTTS (Google Text-to-Speech) and Amazon Polly are used for speech synthesis. This speech is output to the elderly through the device's speaker.

[0470] Additionally, it includes the ability to change the voice type based on user preferences: users can select the voice type (e.g., a child's voice or a grandchild's voice) in the settings screen, and the voice synthesis parameters will automatically adjust accordingly.

[0471] The system also constantly monitors the elderly person's voice and behavior to detect any abnormalities. For example, if the system detects an abnormal voice (such as a cry for help) or activity (such as the sound of someone falling), it will immediately notify family members or emergency services. Notification methods include email, SMS, and phone call.

[0472] In a specific scenario, if an elderly person says, "I'm a little tired today," the system converts this into text data and returns a generated response such as, "That's tough. It would be good for you to get some rest." An example of the prompt sentence in this case is as follows:

[0473] "When an elderly person says, 'I feel a little tired today,' the assistant responds, 'That's tough. It would be good for you to get some rest.'"

[0474] This system can help elderly people live safe and secure lives even when they are alone, and at the same time contribute to the prevention of dementia.

[0475] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0476] Step 1:

[0477] The server receives the voice data. When the user speaks, the voice data is picked up through the microphone of the device. The picked up voice data is sent to the server in real time.

[0478] Input: User's voice data

[0479] Data processing: Collection of voice data

[0480] Output: Collected audio data

[0481] Specific operation: When a user says, "I'm a little tired today," the voice is picked up by the device's microphone.

[0482] Step 2:

[0483] The server converts the received voice data into text data using voice recognition technology. The server uses voice recognition software such as Google Speech Recognition API or IBM Watson to analyze the voice data and generate text data.

[0484] Input: Received audio data

[0485] Data processing: Converting voice data into text

[0486] Output: Generated text data

[0487] Specific operation: The server receives the voice data "I'm a little tired today" and converts it into text data "I'm a little tired today."

[0488] Step 3:

[0489] The server generates dialogue content based on the text data, using a generative AI model to achieve natural dialogue. The server inputs the text data as a prompt, and generates the dialogue content based on the response obtained from the generative AI model.

[0490] Input: Converted text data

[0491] Data processing: Dialogue content generation using generative AI models

[0492] Output: Generated dialogue

[0493] Specific operation: The server inputs the text data "I'm a little tired today" as a prompt and generates the dialogue content "That's tough. It would be good for you to take a short rest."

[0494] Step 4:

[0495] The device outputs the generated dialogue content as voice. The server sends the generated dialogue content as text data to the device, which then converts it into voice data using a speech synthesis engine such as gTTS or Amazon Polly and outputs it.

[0496] Input: Text data of the generated dialogue

[0497] Data processing: Converting text data into speech

[0498] Output: Audio data

[0499] Specific operation: The device converts the text data "That's tough, you should take a short rest" into speech and outputs it through the speaker.

[0500] Step 5:

[0501] The device adjusts voice parameters based on the voice type selected by the user. The device checks the user's settings and adjusts the gTTS or Amazon Polly voice synthesis parameters to output the voice type desired by the user (for example, a child's voice or a grandchild's voice).

[0502] Input: User's voice settings

[0503] Data processing: Adjustment of speech synthesis parameters

[0504] Output: Modified audio data

[0505] Specific operation: When the user selects "Grandchild's voice" on the device's settings screen, the device adjusts the parameters of the voice synthesis engine to match the grandchild's voice and outputs the corresponding voice.

[0506] Step 6:

[0507] The server monitors the elderly person's voice and activity and notifies them if it detects any abnormalities. The server constantly analyzes voice data and notifies family members and emergency services if it detects any abnormal voice or activity.

[0508] Input: Elderly person's voice and activity data

[0509] Data processing: Analysis using anomaly detection algorithms

[0510] Output: Notification data (email, SMS, phone)

[0511] Specific operation: If the server detects an abnormal voice saying "help," it will immediately send an email notification to family members.

[0512] 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.

[0513] This invention aims to prevent dementia and improve quality of life by providing hearing assistance and activating communication while the elderly are watching television. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more appropriate and personalized dialogue.

[0514] The overall system configuration is as follows:

[0515] First, the server has a means for receiving audio data. It receives audio data (e.g., tv_audio_sample.wav) recorded while a user is watching television via the Internet and saves it in a specified directory.

[0516] Next, the terminal has a means for converting the received voice data into text data. The terminal analyzes the voice data using a voice recognition engine and generates text data. The generated text data is processed by a means for generating dialogue content. The terminal selects an appropriate response from a plurality of response patterns and realizes a dialogue with the user.

[0517] Furthermore, the system is equipped with an emotion engine that can recognize the user's emotions by analyzing the user's tone of voice, facial expressions, or biometric signals to determine whether the user is enjoying or dissatisfied.

[0518] The system also includes a means to tailor dialogue based on the perceived emotion: for example, if the user is determined to be sad, the dialogue may be altered to provide an encouraging response.

[0519] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[0520] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content. This allows the user to constantly enjoy new dialogue, making it possible to prevent dementia and assist hearing. The emotion engine provides dialogue that takes the user's emotions into account, increasing user satisfaction.

[0521] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. The audio data was recorded while the user was watching TV.

[0525] Step 2:

[0526] The device retrieves the stored voice data and initializes the speech recognition engine (e.g., the speech_recognition library), which analyzes the voice data and prepares it for conversion to text data.

[0527] Step 3:

[0528] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. For example, it generates text data such as "Today's weather is sunny."

[0529] Step 4:

[0530] The device inputs the generated text data into a dialogue content generation engine to generate appropriate dialogue content. The dialogue content generation engine selects an appropriate response pattern from multiple response patterns and generates the response text "That's interesting!" as an example.

[0531] Step 5:

[0532] The device initializes the emotion engine and analyzes the user's emotions. The emotion engine obtains the user's tone of voice and facial expressions through the camera and microphone and determines the user's emotions.

[0533] Step 6:

[0534] The emotion engine adjusts the dialogue content based on the user's emotions. For example, if it determines that the user is having fun, the dialogue content will be positive, such as "That's great!"

[0535] Step 7:

[0536] The device converts the adjusted dialogue into speech using a speech synthesis engine (e.g., the pyttsx3 library), which uses a voice type (e.g., a child's voice) based on the user's settings.

[0537] Step 8:

[0538] The terminal outputs the generated voice to the user through a speaker, and the user can enjoy active dialogue while watching television by listening to the adjusted dialogue voice.

[0539] Step 9:

[0540] The user can select the type of voice on the settings screen. The user settings are sent to the server and are reflected in the next and subsequent conversations.

[0541] Step 10:

[0542] The above process is carried out continuously, allowing users to enjoy constantly new conversations while receiving dementia prevention and hearing support. The emotion engine also takes the user's emotions into consideration, increasing user satisfaction.

[0543] Example 2

[0544] 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."

[0545] There is a lack of technology that can assist elderly people in hearing loss and stimulate communication while watching television, aiming to prevent dementia and improve their quality of life. Conventional systems have difficulty providing dialogue that takes into account the user's emotions, and are unable to generate dialogue that is appropriate for each individual user.

[0546] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for recognizing the user's emotion, and means for adjusting the dialogue content based on the recognized emotion. This allows elderly people to enjoy dialogue that suits their individual emotions while watching television, thereby preventing dementia and improving their quality of life.

[0547] The "means for receiving voice data" refers to technology including a communication device and software for receiving voice data sent from a user.

[0548] The "means for converting voice data into text data" includes voice recognition technology and software that analyzes received voice data and converts it into corresponding text data.

[0549] "Means for generating dialogue content" refers to a generative AI model or dialogue generation engine for creating dialogue content based on text data generated from voice data.

[0550] The "means for outputting the generated dialogue content as voice" refers to a voice synthesis technology for converting text data into voice data and providing it to the user through a voice output device.

[0551] The "means for changing the type of voice" is a software function for changing the characteristics of the voice to be output (e.g., type of voice, pitch) according to the user's settings.

[0552] "Means for recognizing the user's emotions" refers to emotion recognition technology or software that analyzes the user's tone of voice, facial expressions, biometric signals, etc. to determine the user's emotional state.

[0553] The "means for adjusting the dialogue content based on the recognized emotion" is a dialogue adjustment technique for appropriately correcting or adjusting the dialogue content generated in accordance with the emotional state of the user.

[0554] "Audio data from a video device" refers to an audio signal output from a television or other audiovisual device.

[0555] "Content aimed at preventing dementia" refers to dialogue and information provision designed to maintain or improve the user's cognitive function.

[0556] The present invention relates to a system that provides hearing assistance and stimulates communication while elderly people watch television, aiming to prevent dementia and improve their quality of life. This system is characterized by its ability to consistently perform processes from receiving and analyzing voice data, generating dialogue content, and outputting the voice, to adjusting the dialogue based on the user's emotions. A specific embodiment of this system is described below.

[0557] First, the server receives audio data. The audio data is recorded while the user is watching TV and is sent to the server via the Internet. As an example, consider the audio file "tv_audio_sample.wav". This audio file is saved in a specified directory on the server.

[0558] Next, the device receives the voice data received from the server. The device analyzes the voice data using a voice recognition engine (e.g., Google Speech-to-Text API). Once the voice data is analyzed, text data is generated as a result. For example, the audio file "tv_audio_sample.wav" is converted into text data such as "The weather is sunny today."

[0559] After the text data is generated, the device uses a dialogue generation engine (e.g., GPT-3) to generate appropriate dialogue content, which is then saved as text, such as "That's great!"

[0560] The device also incorporates an emotion recognition engine (e.g., IBM Watson Tone Analyzer) that recognizes the user's emotions. This engine analyzes the user's tone of voice, facial expressions, and biometric signals to determine their emotional state, such as whether they are enjoying themselves or feeling dissatisfied. For example, if it determines that the user is happy, it adjusts the dialogue content generated based on that result. Specifically, if the user is happy, positive dialogue content is selected.

[0561] Finally, the generated dialogue is converted into speech by a speech synthesis engine. The device outputs this speech through a speaker. The type of voice can be changed based on the user's settings. For example, the user can select a child's voice to output the response.

[0562] This system allows elderly people to enjoy constantly new dialogues, helping to prevent dementia and assisting hearing. In addition, the emotion engine provides dialogue that takes the user's emotions into account, improving user satisfaction.

[0563] Specific examples

[0564] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[0565] Prompt Sentence Examples

[0566] The voice data "tv_audio_sample.wav" recorded while the user was watching TV was received. The text obtained using the speech recognition engine is "It's a nice day today." In this case, if the user's emotion is recognized as good mood, please show the appropriate response to generate.

[0567] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0568] Step 1: Receiving and storing audio data

[0569] Input: Audio data recorded while the user is watching TV (e.g., tv_audio_sample.wav)

[0570] Processing: The server receives the audio data via the Internet.

[0571] Specific behavior:

[0572] The user records audio through a dedicated microphone.

[0573] The recorded audio data is sent from the terminal to the server.

[0574] The server detects the data reception trigger, captures the connected data stream, and saves it as a file.

[0575] Output: Audio data file saved in the specified directory on the server (e.g. / path / to / tv_audio_sample.wav)

[0576] Step 2: Analyzing the audio data and generating text data

[0577] Input: Audio data file saved on the server (e.g., / path / to / tv_audio_sample.wav)

[0578] Processing: The device uses a speech recognition engine to analyze the voice data and generate text data.

[0579] Specific behavior:

[0580] The terminal downloads the audio data from the specified directory on the server.

[0581] Initialize the speech recognition engine (e.g., Google Speech-to-Text API) and begin analyzing the audio data.

[0582] Once the analysis is complete, text data is generated and saved in temporary memory or as a file.

[0583] Output: Generated text data (e.g., "Today's weather is sunny")

[0584] Step 3: Dialogue generation and response selection

[0585] Input: Generated text data (e.g., "Today's weather is sunny")

[0586] Processing: The device uses a dialogue generation engine to generate appropriate dialogue content and selects the most appropriate response from multiple response candidates.

[0587] Specific behavior:

[0588] The terminal creates a prompt based on the generated text data.

[0589] The prompt is fed into a generative AI model (e.g., GPT-3) to generate an appropriate response.

[0590] Select the best response from multiple possible responses and save it as text.

[0591] Output: Selected dialogue text (e.g., "That's great!")

[0592] Step 4: Recognize emotions

[0593] Input: Data such as the user's tone of voice, facial expressions, and biometric signals

[0594] Processing: The device uses an emotion recognition engine to analyze the user's emotions.

[0595] Specific behavior:

[0596] The device collects emotion-related data from the microphone, camera, and biometric sensors.

[0597] An emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes this data to determine the user's emotional state.

[0598] The analysis results are saved in a database.

[0599] Output: Perceived user emotional state (e.g., good mood)

[0600] Step 5: Adjust the conversation

[0601] Input: Recognized user emotional state (e.g., in a good mood), selected dialogue text (e.g., "That's great!")

[0602] Processing: The device adjusts the generated dialogue content based on the recognized emotion.

[0603] Specific behavior:

[0604] The device receives the emotion recognition results and reevaluates the existing response text.

[0605] If necessary, modify your response to make it more appropriate.

[0606] The corrected response is saved as text and used as input data for the speech synthesis engine.

[0607] Output: Adjusted dialogue text (e.g., "That's really great!")

[0608] Step 6: Speech synthesis and response output

[0609] Input: Adjusted dialogue text (e.g., "That's really great!")

[0610] Processing: The device uses a speech synthesis engine to convert the generated text response into speech and output it to the user.

[0611] Specific behavior:

[0612] The device inputs the modified response text into a speech synthesis engine.

[0613] A speech synthesis engine converts the text data into speech data.

[0614] The generated audio is output to a speaker so that the user can hear it.

[0615] Output: A voice response played in a voice you set (e.g., "That's really great!" in a child's voice)

[0616] (Application example 2)

[0617] 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."

[0618] There is a need for methods to prevent elderly people from feeling isolated in their daily lives and to prevent cognitive decline. However, existing systems are unable to communicate with them based on their emotions, making it difficult to respond immediately in emergencies. Furthermore, there is a lack of systems equipped with monitoring functions that can respond immediately to abnormalities or emergencies within the home. As a result, it is difficult to create a safe and secure environment while ensuring the quality of life of the elderly.

[0619] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for analyzing the user's emotions, means for adjusting the dialogue content based on the analyzed emotions, and means for issuing an alarm in an emergency. This enables dialogue that is appropriate for the emotions and situation of the elderly, which not only contributes to maintaining and improving cognitive function but also enables a quick response in an emergency.

[0620] The "means for receiving audio data" refers to a device or program that has the function of taking audio information transmitted from the outside into the device.

[0621] The "means for converting received voice data into text data" refers to a device or program for converting voice information into text information using voice recognition technology.

[0622] The "means for generating dialogue content based on text data" refers to a device or program that automatically generates dialogue content with a user based on the converted character information.

[0623] The "means for outputting the generated dialogue content as voice" refers to a device or program for reproducing the text-based dialogue content using voice synthesis technology.

[0624] The "means for changing the type of voice based on the user's settings" refers to a device or program that adjusts the characteristics of the voice (tone of voice, speed, etc.) that is output in accordance with the settings that the user has specified in advance.

[0625] The "means for analyzing user emotions" refers to a device or program that identifies the user's emotional state from the user's voice or speech content and obtains the results.

[0626] The "means for adjusting the content of the dialogue based on the analyzed emotions" refers to a device or program that changes the content of the dialogue in a timely manner in accordance with the results of analyzing the user's emotions.

[0627] "Means for issuing an alarm in an emergency" refers to a device or program that detects a physical or psychological emergency in a user and issues a prompt warning.

[0628] "Audio data from video equipment" refers to audio information emitted from visual equipment such as televisions and displays.

[0629] "Content aimed at maintaining or improving cognitive function" is interactive content designed to maintain or improve the user's cognitive abilities.

[0630] This invention is a system that aims to maintain and improve cognitive function and ensure safety for elderly people in their daily lives without them feeling isolated. This system provides personalized dialogue to users through a series of processes including receiving and analyzing voice data, generating dialogue, analyzing emotions, and responding to emergencies.

[0631] The server has a means to receive audio data from an external source. For example, it retrieves audio files from the Internet and saves them in a specified directory. This means can be realized by a home device such as a smartphone or smart speaker. A standard HTTP request is used to receive the audio data.

[0632] The voice data stored on the server is converted into text data by a voice recognition engine. Existing voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text can be used for this purpose. The converted text data is used to generate dialogue with the user.

[0633] The device then runs an algorithm that generates dialogue based on the generated text data. The dialogue is then tailored to the user's emotions by selecting appropriate responses from a variety of patterns. The device uses emotion analysis engines such as EmoPy and the Microsoft Azure Emotion API.

[0634] The generated dialogue is output as voice using a speech synthesis engine, and the type of voice (tone of voice, speed, etc.) preset by the user is also supported. Google Cloud Text-to-Speech and Amazon Polly can be used for speech synthesis.

[0635] The system also has a means of issuing an alarm in an emergency. If an abnormality is detected in the user's voice or behavior, a notification is sent promptly to family members and medical personnel via the middleware, allowing for a smooth response in an emergency.

[0636] For example, if a user says, "I wonder what the weather will be like today?" the system goes through the following process:

[0637] 1. Receive audio data and save it as "tv_audio_sample.wav".

[0638] 2. The speech recognition engine converts the text into "I wonder what the weather will be like today?"

[0639] 3. The emotion analysis engine determines the user's emotion as "happy."

[0640] 4. Generate the corresponding response and output it as voice using the speech synthesis engine: "It's sunny today, what beautiful weather!"

[0641] Example prompt sentence:

[0642] Determine the emotion of the user's tone of voice and generate an appropriate response.

[0643] Input: "I wonder what the weather will be like today?"

[0644] Emotion: "happy"

[0645] Response: "It's a beautiful sunny day today!"

[0646] This system allows elderly people to enjoy appropriate dialogue that reflects their emotions, reducing feelings of isolation, maintaining and improving cognitive function, and enabling rapid response in emergencies.

[0647] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0648] Step 1:

[0649] The server receives audio data sent from an external source. This process downloads the audio data using an HTTP request and saves it locally as a file. The input is the URL of the audio data, and the output is an audio file (e.g., "tv_audio_sample.wav") saved to the local disk.

[0650] Step 2:

[0651] The server sends the saved audio file to a speech recognition engine, which converts the audio data into text data. This process uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to analyze the audio file and return text. The input is the audio file, and the output is text data (e.g., "I wonder what the weather will be like today?").

[0652] Step 3:

[0653] The device analyzes the text data obtained from the speech recognition engine to determine the user's intentions and emotions. This process uses an emotion analysis engine (e.g., EmoPy) to determine the emotion of the text data. The input is the text data, and the output is the determined emotion information (e.g., "happy").

[0654] Step 4:

[0655] The device generates dialogue content based on the analyzed emotions. This process uses a generative AI model to generate an appropriate response. The input is text data and emotion information, and the output is the generated dialogue content (e.g., "It's sunny today, the weather is very nice!").

[0656] Step 5:

[0657] The device sends the generated dialogue to a speech synthesis engine, which outputs it as speech. This process uses a speech synthesis service (e.g., Google Cloud Text-to-Speech) to convert the text into speech and play it back to the user. The input is the text of the generated dialogue, and the output is the synthesized speech.

[0658] Step 6:

[0659] The device changes the voice type based on the user's settings. This process adjusts the settings of the speech synthesis engine and outputs speech at the tone and speed specified by the user. The input is the user's settings information and the generated dialogue text, and the output is a customized voice.

[0660] Step 7:

[0661] The server performs a process to issue an alarm in the event of an emergency. This process detects abnormalities from the user's voice and behavioral analysis results, issues an alarm, and sends notifications to family members and medical personnel. The input is the user's behavior and voice analysis results, and the output is the issued alarm message.

[0662] Through this series of processing steps, the system provides interactive dialogue based on the user's emotions and situation, enabling them to maintain cognitive function and respond quickly in emergencies.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] [Third embodiment]

[0667] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0668] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0669] 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).

[0670] 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.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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."

[0679] The present invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life. Specific embodiments of the present invention will be described below.

[0680] First, the system has a means for receiving voice data. The server receives the TV's voice data and stores it. Next, the terminal has a means for converting the received voice data into text data. The terminal uses voice recognition technology to analyze the voice data and generate text data.

[0681] The generated text data is processed by a dialogue content generation means. The terminal generates an appropriate response for dialogue with the user based on the text data. In this case, the response generation algorithm has multiple response patterns and randomly selects one to achieve a natural dialogue.

[0682] The system also includes a means for outputting the generated dialogue content as voice. The terminal converts the text data into voice using speech synthesis technology and outputs it to the user. The user can listen to this voice through a speaker.

[0683] The terminal also includes a means for changing the type of voice based on user settings. The user can select the type of voice (e.g., a child's voice or a grandchild's voice) on the settings screen, and the terminal changes the voice synthesis parameters according to the settings.

[0684] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." The device then generates a response such as "That's interesting!" and outputs the response using a speech synthesis engine. This speech is output in a child's voice, allowing the user to enjoy active interaction while watching television.

[0685] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content, allowing the user to constantly enjoy new dialogue and potentially helping to prevent dementia and assist hearing loss.

[0686] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0687] The processing flow will be explained below.

[0688] Step 1:

[0689] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. This is the audio data recorded while the user was watching TV.

[0690] Step 2:

[0691] The device retrieves the stored voice data and initializes the speech recognition engine, for example, using the speech_recognition library.

[0692] Step 3:

[0693] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. The voice recognition engine analyzes the voice data and generates text data, e.g., "Today's weather is sunny."

[0694] Step 4:

[0695] The device generates dialogue content based on the generated text data. The dialogue generation algorithm selects an appropriate response from multiple response patterns, e.g., "That's interesting!"

[0696] Step 5:

[0697] The device initializes the speech synthesis engine and converts the generated dialogue into speech. For example, it uses the pyttsx3 library to synthesize speech in the specified voice type (e.g., a child's voice).

[0698] Step 6:

[0699] The device outputs the synthesized voice to the user through a speaker, allowing the user to enjoy a conversation with the AI ​​while watching TV.

[0700] Step 7:

[0701] The user selects the type of voice on the settings screen. Based on this, the device changes the parameters of the speech synthesis engine and outputs the next conversation voice in the type of voice the user desires (e.g., the voice of a grandchild).

[0702] Example 1

[0703] 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."

[0704] There is a lack of systems that can assist elderly people in hearing and stimulate communication when watching television. As a result, elderly people often feel lonely, and problems such as the progression of dementia and a decline in quality of life (QOL) occur. Conventional systems lack the ability to interact while watching television, and do not adequately assist hearing and mental health.

[0705] 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.

[0706] In this invention, the server includes means for receiving voice data, means for saving the received voice data, means for converting the voice data into text data using voice recognition technology, means for generating dialogue content based on the text data using a generative AI model, means for outputting the generated dialogue content as voice, and means for changing the type of voice based on user settings. This makes it possible to prevent dementia and assist hearing through dialogue with elderly people watching television.

[0707] "Audio data" refers to the digital or analog format of audio signals output from televisions and other audio equipment.

[0708] "Means for receiving" refers to a device or method for acquiring audio data, examples of which include a microphone or data streaming technology.

[0709] "Storage means" refers to a device or method for storing received audio data, examples of which include a database or cloud storage.

[0710] "Speech recognition technology" refers to technology that analyzes voice data and converts it into text data, and examples include speech recognition APIs.

[0711] "Text data" refers to character string information obtained by analyzing voice data.

[0712] A "generative AI model" refers to a machine learning algorithm that generates natural-sounding dialogue based on input text data.

[0713] "Means for generating dialogue content" refers to a method or device that uses a generative AI model to create appropriate dialogue based on text data.

[0714] "Means for outputting as audio" refers to a method or device for converting the generated dialogue content into an audio format and delivering it to the user via a speaker or the like.

[0715] "Means for changing the voice type based on user settings" refers to a method or device that adjusts the voice output according to the voice parameters (e.g., voice type) desired by the user.

[0716] This invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life (QOL). Specific embodiments of the invention are described below.

[0717] First, the server has a means to receive audio data. The server uses a microphone or data streaming technology to acquire audio data output from a television or other audio equipment. For example, it can receive audio data in the format "tv_audio_sample.wav" from a microphone connected to a television.

[0718] Next, the server stores the received audio data. To store it, it uses a database or cloud storage (e.g., AWS S3) to temporarily store the received audio data. The server saves the audio file in AWS S3 and records the file name and metadata in the database.

[0719] The device then retrieves the voice data file stored on the server and converts it into text using voice recognition technology, such as the Google Cloud Speech-to-Text API. The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate the text "Today's weather is sunny."

[0720] Next, the device uses a generative AI model to generate dialogue content based on the text data. In this process, appropriate dialogue content can be obtained by inputting a prompt sentence into the generative AI model (e.g., OpenAI GPT-4). The device sends a prompt to the generative AI model: "Text data: 'The weather is sunny today.' Please generate a response to this." The model generates a response: "That's interesting!"

[0721] The device outputs the generated dialogue as voice. Using speech synthesis technology (e.g., Amazon Polly), the text data is converted into voice and delivered to the user through the speaker. The device uses Amazon Polly to convert the text "That's interesting!" into voice data, which is then output through the speaker in a child's voice.

[0722] Furthermore, the user can select the type of voice (e.g., child's voice, grandchild's voice) on the settings screen. The device changes the voice synthesis parameters according to the setting. If the user selects "grandchild's voice" on the settings screen, the device sets the parameters of the voice synthesis engine to "grandchild's voice" and generates voices using those parameters from the next conversation.

[0723] For example, the server receives and saves an audio file called "tv_audio_sample.wav." The device analyzes this audio file using the Google Cloud Speech-to-Text API and generates text data such as "The weather is sunny today." The device then inputs the prompt text "Text data: 'The weather is sunny today.' Please generate a response to this." into the generative AI model, which generates the response "That's interesting!" This response is then converted into speech using Amazon Polly and output from the speaker in a child's voice.

[0724] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, generates dialogue content, and outputs it as voice. This allows the user to constantly enjoy new dialogue, and can help prevent dementia and assist hearing loss.

[0725] This system allows elderly people to not only watch television, but also have an active, interactive experience, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0726] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0727] Program processing flow

[0728] Step 1:

[0729] The server receives the audio data.

[0730] Input: Audio signal from a TV or audio device

[0731] Processing: Convert the audio signal into digital format and send it to the server

[0732] Output: Digital audio data file (e.g. "tv_audio_sample.wav")

[0733] How it works: The server collects audio data using a microphone connected to the TV, converts it into digital format, and stores it.

[0734] Step 2:

[0735] The server stores the received audio data.

[0736] Input: Digital audio data file (e.g. "tv_audio_sample.wav")

[0737] Processing: Store the audio data in cloud storage (e.g. AWS S3) and record the metadata in a database.

[0738] Output: Audio data file saved on cloud storage

[0739] Specific operation: The server uploads "tv_audio_sample.wav" to AWS S3 and records the file metadata (e.g. timestamp, file size) in the database.

[0740] Step 3:

[0741] The terminal acquires the voice data file stored on the server and converts the voice data into text data using voice recognition technology.

[0742] Input: Audio data files stored in cloud storage

[0743] Processing: Analyzes the audio data using speech recognition technology (e.g., Google Cloud Speech-to-Text API) and generates text data.

[0744] Output: Converted text data (e.g. "Today's weather is sunny")

[0745] Specific operation: The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate text data saying "Today's weather is sunny."

[0746] Step 4:

[0747] The device uses a generative AI model to generate dialogue content based on the text data.

[0748] Input: Text data (e.g., "Today's weather is sunny")

[0749] Processing: Input a prompt into a generative AI model (e.g., OpenAI GPT-4) to generate appropriate dialogue.

[0750] Output: The generated dialogue (e.g., "That's interesting!")

[0751] Specific operation: The device sends the prompt "Text data: 'The weather is sunny today.' Please generate a response to this." to the generative AI model, which then generates the response "That's interesting!"

[0752] Step 5:

[0753] The terminal outputs the generated dialogue content as voice.

[0754] Input: The generated dialogue (e.g., "That's interesting!")

[0755] Processing: Convert text data into speech using speech synthesis technology (e.g., Amazon Polly)

[0756] Output: Audio data (e.g., audio generated from a child's voice)

[0757] What it does: The device converts the text "That's interesting!" into speech data using Amazon Polly and outputs it through the speaker in a child's voice.

[0758] Step 6:

[0759] The user selects the type of voice (e.g., child's voice, grandchild's voice) on the settings screen.

[0760] Input: The voice type selected by the user in the settings screen

[0761] Processing: Change the settings of the speech synthesis engine based on the selected voice parameters

[0762] Output: Updated audio parameters

[0763] Specific operation: When the user selects "Grandchild's voice" on the settings screen, the device changes the Amazon Polly parameters to "Grandchild's voice" and reflects this setting from the next conversation.

[0764] Step 7:

[0765] The server and the terminal work together to continuously receive, analyze, and generate dialogue content while the user is watching television.

[0766] Input: New audio data received periodically

[0767] Processing: Repeat steps 1 to 6

[0768] Output: Continuously generated dialogue and its audio output

[0769] Specific operation: The server periodically receives new voice data, and the terminal analyzes the data in real time, generates dialogue content, and continuously outputs it to the user as voice.

[0770] This series of processes allows users to enjoy continuous interaction while watching television, making it possible to prevent dementia and assist hearing.

[0771] (Application example 1)

[0772] 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."

[0773] Preventing dementia and improving the quality of life of elderly people is important when they spend time alone. However, for elderly people with impaired vision or hearing, communication through television and other forms of entertainment can be difficult. Safety issues also exist, such as the inability to respond appropriately in the event of a sudden illness or accident. Therefore, technological solutions are needed to enable elderly people to live independently and with peace of mind.

[0774] 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.

[0775] In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on user settings, and means for monitoring the voice and activity of the elderly person and notifying them when an abnormality is detected. This enables elderly people to live a safe and fulfilling life through dialogue to prevent dementia, even when they are spending time alone.

[0776] "Audio data" is a general term for digital or analog data that records audio.

[0777] "Text data" refers to data that is generated by analyzing voice data and expressing it as a string of characters.

[0778] "Dialogue content" refers to the content of the conversation between a human and a system, generated based on text data.

[0779] "Means for receiving" refers to a device or software that acquires audio data and passes it to an application.

[0780] "Conversion means" refers to a function or device that converts voice data into text data.

[0781] "Generation means" refers to a function or device for generating dialogue content based on text data.

[0782] "Output means" refers to a function or device for reproducing the generated dialogue content as voice.

[0783] "Means for changing the type" refers to a function or device for changing the characteristics of the voice (for example, voice quality or speaking style) according to the user's settings.

[0784] "Monitoring means" refers to functions or devices that constantly observe the sounds and activities around the elderly person and collect data.

[0785] "Means for detecting abnormalities" refers to functions or devices that recognize unusual sounds or behavioral patterns and determine whether an abnormality has occurred.

[0786] "Means of notification" refers to functions or devices that promptly alert family members or emergency services when an abnormality is detected.

[0787] This invention provides an interactive assistant system for preventing dementia and ensuring the safety of elderly people while they are alone. This system has the functions of receiving voice data, converting it to text data, generating dialogue content, synthesizing voice, changing the type of voice based on user settings, and detecting and notifying abnormalities in the elderly.

[0788] The server is equipped with a microphone to receive audio data such as environmental sounds and the voices of elderly people. This audio data is then transmitted to the server via devices such as smart glasses or head-mounted displays.

[0789] The received voice data is then converted into text data using voice recognition technology, such as Google Speech Recognition API or IBM Watson.

[0790] A dialogue with the user is then generated based on the text data. A generative AI model is used to generate the dialogue, resulting in natural responses. The generated responses are randomly selected from multiple patterns, providing a more human-like dialogue.

[0791] The generated dialogue is converted into speech using speech synthesis technology. Speech synthesis engines such as gTTS (Google Text-to-Speech) and Amazon Polly are used for speech synthesis. This speech is output to the elderly through the device's speaker.

[0792] Additionally, it includes the ability to change the voice type based on user preferences: users can select the voice type (e.g., a child's voice or a grandchild's voice) in the settings screen, and the voice synthesis parameters will automatically adjust accordingly.

[0793] The system also constantly monitors the elderly person's voice and behavior to detect any abnormalities. For example, if the system detects an abnormal voice (such as a cry for help) or activity (such as the sound of someone falling), it will immediately notify family members or emergency services. Notification methods include email, SMS, and phone call.

[0794] In a specific scenario, if an elderly person says, "I'm a little tired today," the system converts this into text data and returns a generated response such as, "That's tough. It would be good for you to get some rest." An example of the prompt sentence in this case is as follows:

[0795] "When an elderly person says, 'I feel a little tired today,' the assistant responds, 'That's tough. It would be good for you to get some rest.'"

[0796] This system can help elderly people live safe and secure lives even when they are alone, and at the same time contribute to the prevention of dementia.

[0797] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0798] Step 1:

[0799] The server receives the voice data. When the user speaks, the voice data is picked up through the microphone of the device. The picked up voice data is sent to the server in real time.

[0800] Input: User's voice data

[0801] Data processing: Collection of voice data

[0802] Output: Collected audio data

[0803] Specific operation: When a user says, "I'm a little tired today," the voice is picked up by the device's microphone.

[0804] Step 2:

[0805] The server converts the received voice data into text data using voice recognition technology. The server uses voice recognition software such as Google Speech Recognition API or IBM Watson to analyze the voice data and generate text data.

[0806] Input: Received audio data

[0807] Data processing: Converting voice data into text

[0808] Output: Generated text data

[0809] Specific operation: The server receives the voice data "I'm a little tired today" and converts it into text data "I'm a little tired today."

[0810] Step 3:

[0811] The server generates dialogue content based on the text data, using a generative AI model to achieve natural dialogue. The server inputs the text data as a prompt, and generates the dialogue content based on the response obtained from the generative AI model.

[0812] Input: Converted text data

[0813] Data processing: Dialogue content generation using generative AI models

[0814] Output: Generated dialogue

[0815] Specific operation: The server inputs the text data "I'm a little tired today" as a prompt and generates the dialogue content "That's tough. It would be good for you to take a short rest."

[0816] Step 4:

[0817] The device outputs the generated dialogue content as voice. The server sends the generated dialogue content as text data to the device, which then converts it into voice data using a speech synthesis engine such as gTTS or Amazon Polly and outputs it.

[0818] Input: Text data of the generated dialogue

[0819] Data processing: Converting text data into speech

[0820] Output: Audio data

[0821] Specific operation: The device converts the text data "That's tough, you should take a short rest" into speech and outputs it through the speaker.

[0822] Step 5:

[0823] The device adjusts voice parameters based on the voice type selected by the user. The device checks the user's settings and adjusts the gTTS or Amazon Polly voice synthesis parameters to output the voice type desired by the user (for example, a child's voice or a grandchild's voice).

[0824] Input: User's voice settings

[0825] Data processing: Adjustment of speech synthesis parameters

[0826] Output: Modified audio data

[0827] Specific operation: When the user selects "Grandchild's voice" on the device's settings screen, the device adjusts the parameters of the voice synthesis engine to match the grandchild's voice and outputs the corresponding voice.

[0828] Step 6:

[0829] The server monitors the elderly person's voice and activity and notifies them if it detects any abnormalities. The server constantly analyzes voice data and notifies family members and emergency services if it detects any abnormal voice or activity.

[0830] Input: Elderly person's voice and activity data

[0831] Data processing: Analysis using anomaly detection algorithms

[0832] Output: Notification data (email, SMS, phone)

[0833] Specific operation: If the server detects an abnormal voice saying "help," it will immediately send an email notification to family members.

[0834] 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.

[0835] This invention aims to prevent dementia and improve quality of life by providing hearing assistance and activating communication while the elderly are watching television. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more appropriate and personalized dialogue.

[0836] The overall system configuration is as follows:

[0837] First, the server has a means for receiving audio data. It receives audio data (e.g., tv_audio_sample.wav) recorded while a user is watching television via the Internet and saves it in a specified directory.

[0838] Next, the terminal has a means for converting the received voice data into text data. The terminal analyzes the voice data using a voice recognition engine and generates text data. The generated text data is processed by a means for generating dialogue content. The terminal selects an appropriate response from a plurality of response patterns and realizes a dialogue with the user.

[0839] Furthermore, the system is equipped with an emotion engine that can recognize the user's emotions by analyzing the user's tone of voice, facial expressions, or biometric signals to determine whether the user is enjoying or dissatisfied.

[0840] The system also includes a means to tailor dialogue based on the perceived emotion: for example, if the user is determined to be sad, the dialogue may be altered to provide an encouraging response.

[0841] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[0842] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content. This allows the user to constantly enjoy new dialogue, making it possible to prevent dementia and assist hearing. The emotion engine provides dialogue that takes the user's emotions into account, increasing user satisfaction.

[0843] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[0844] The processing flow will be explained below.

[0845] Step 1:

[0846] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. The audio data was recorded while the user was watching TV.

[0847] Step 2:

[0848] The device retrieves the stored voice data and initializes the speech recognition engine (e.g., the speech_recognition library), which analyzes the voice data and prepares it for conversion to text data.

[0849] Step 3:

[0850] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. For example, it generates text data such as "Today's weather is sunny."

[0851] Step 4:

[0852] The device inputs the generated text data into a dialogue content generation engine to generate appropriate dialogue content. The dialogue content generation engine selects an appropriate response pattern from multiple response patterns and generates the response text "That's interesting!" as an example.

[0853] Step 5:

[0854] The device initializes the emotion engine and analyzes the user's emotions. The emotion engine obtains the user's tone of voice and facial expressions through the camera and microphone and determines the user's emotions.

[0855] Step 6:

[0856] The emotion engine adjusts the dialogue content based on the user's emotions. For example, if it determines that the user is having fun, the dialogue content will be positive, such as "That's great!"

[0857] Step 7:

[0858] The device converts the adjusted dialogue into speech using a speech synthesis engine (e.g., the pyttsx3 library), which uses a voice type (e.g., a child's voice) based on the user's settings.

[0859] Step 8:

[0860] The terminal outputs the generated voice to the user through a speaker, and the user can enjoy active dialogue while watching television by listening to the adjusted dialogue voice.

[0861] Step 9:

[0862] The user can select the type of voice on the settings screen. The user settings are sent to the server and are reflected in the next and subsequent conversations.

[0863] Step 10:

[0864] The above process is carried out continuously, allowing users to enjoy constantly new conversations while receiving dementia prevention and hearing support. The emotion engine also takes the user's emotions into consideration, increasing user satisfaction.

[0865] Example 2

[0866] 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."

[0867] There is a lack of technology that can assist elderly people in hearing loss and stimulate communication while watching television, aiming to prevent dementia and improve their quality of life. Conventional systems have difficulty providing dialogue that takes into account the user's emotions, and are unable to generate dialogue that is appropriate for each individual user.

[0868] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for recognizing the user's emotion, and means for adjusting the dialogue content based on the recognized emotion. This allows elderly people to enjoy dialogue that suits their individual emotions while watching television, thereby preventing dementia and improving their quality of life.

[0869] The "means for receiving voice data" refers to technology including a communication device and software for receiving voice data sent from a user.

[0870] The "means for converting voice data into text data" includes voice recognition technology and software that analyzes received voice data and converts it into corresponding text data.

[0871] "Means for generating dialogue content" refers to a generative AI model or dialogue generation engine for creating dialogue content based on text data generated from voice data.

[0872] The "means for outputting the generated dialogue content as voice" refers to a voice synthesis technology for converting text data into voice data and providing it to the user through a voice output device.

[0873] The "means for changing the type of voice" is a software function for changing the characteristics of the voice to be output (e.g., type of voice, pitch) according to the user's settings.

[0874] "Means for recognizing the user's emotions" refers to emotion recognition technology or software that analyzes the user's tone of voice, facial expressions, biometric signals, etc. to determine the user's emotional state.

[0875] The "means for adjusting the dialogue content based on the recognized emotion" is a dialogue adjustment technique for appropriately correcting or adjusting the dialogue content generated in accordance with the emotional state of the user.

[0876] "Audio data from a video device" refers to an audio signal output from a television or other audiovisual device.

[0877] "Content aimed at preventing dementia" refers to dialogue and information provision designed to maintain or improve the user's cognitive function.

[0878] The present invention relates to a system that provides hearing assistance and stimulates communication while elderly people watch television, aiming to prevent dementia and improve their quality of life. This system is characterized by its ability to consistently perform processes from receiving and analyzing voice data, generating dialogue content, and outputting the voice, to adjusting the dialogue based on the user's emotions. A specific embodiment of this system is described below.

[0879] First, the server receives audio data. The audio data is recorded while the user is watching TV and is sent to the server via the Internet. As an example, consider the audio file "tv_audio_sample.wav". This audio file is saved in a specified directory on the server.

[0880] Next, the device receives the voice data received from the server. The device analyzes the voice data using a voice recognition engine (e.g., Google Speech-to-Text API). Once the voice data is analyzed, text data is generated as a result. For example, the audio file "tv_audio_sample.wav" is converted into text data such as "The weather is sunny today."

[0881] After the text data is generated, the device uses a dialogue generation engine (e.g., GPT-3) to generate appropriate dialogue content, which is then saved as text, such as "That's great!"

[0882] The device also incorporates an emotion recognition engine (e.g., IBM Watson Tone Analyzer) that recognizes the user's emotions. This engine analyzes the user's tone of voice, facial expressions, and biometric signals to determine their emotional state, such as whether they are enjoying themselves or feeling dissatisfied. For example, if it determines that the user is happy, it adjusts the dialogue content generated based on that result. Specifically, if the user is happy, positive dialogue content is selected.

[0883] Finally, the generated dialogue is converted into speech by a speech synthesis engine. The device outputs this speech through a speaker. The type of voice can be changed based on the user's settings. For example, the user can select a child's voice to output the response.

[0884] This system allows elderly people to enjoy constantly new dialogues, helping to prevent dementia and assisting hearing. In addition, the emotion engine provides dialogue that takes the user's emotions into account, improving user satisfaction.

[0885] Specific examples

[0886] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[0887] Prompt Sentence Examples

[0888] The voice data "tv_audio_sample.wav" recorded while the user was watching TV was received. The text obtained using the speech recognition engine is "It's a nice day today." In this case, if the user's emotion is recognized as good mood, please show the appropriate response to generate.

[0889] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0890] Step 1: Receiving and storing audio data

[0891] Input: Audio data recorded while the user is watching TV (e.g., tv_audio_sample.wav)

[0892] Processing: The server receives the audio data via the Internet.

[0893] Specific behavior:

[0894] The user records audio through a dedicated microphone.

[0895] The recorded audio data is sent from the terminal to the server.

[0896] The server detects the data reception trigger, captures the connected data stream, and saves it as a file.

[0897] Output: Audio data file saved in the specified directory on the server (e.g. / path / to / tv_audio_sample.wav)

[0898] Step 2: Analyzing the audio data and generating text data

[0899] Input: Audio data file saved on the server (e.g., / path / to / tv_audio_sample.wav)

[0900] Processing: The device uses a speech recognition engine to analyze the voice data and generate text data.

[0901] Specific behavior:

[0902] The terminal downloads the audio data from the specified directory on the server.

[0903] Initialize the speech recognition engine (e.g., Google Speech-to-Text API) and begin analyzing the audio data.

[0904] Once the analysis is complete, text data is generated and saved in temporary memory or as a file.

[0905] Output: Generated text data (e.g., "Today's weather is sunny")

[0906] Step 3: Dialogue generation and response selection

[0907] Input: Generated text data (e.g., "Today's weather is sunny")

[0908] Processing: The device uses a dialogue generation engine to generate appropriate dialogue content and selects the most appropriate response from multiple response candidates.

[0909] Specific behavior:

[0910] The terminal creates a prompt based on the generated text data.

[0911] The prompt is fed into a generative AI model (e.g., GPT-3) to generate an appropriate response.

[0912] Select the best response from multiple possible responses and save it as text.

[0913] Output: Selected dialogue text (e.g., "That's great!")

[0914] Step 4: Recognize emotions

[0915] Input: Data such as the user's tone of voice, facial expressions, and biometric signals

[0916] Processing: The device uses an emotion recognition engine to analyze the user's emotions.

[0917] Specific behavior:

[0918] The device collects emotion-related data from the microphone, camera, and biometric sensors.

[0919] An emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes this data to determine the user's emotional state.

[0920] The analysis results are saved in a database.

[0921] Output: Perceived user emotional state (e.g., good mood)

[0922] Step 5: Adjust the conversation

[0923] Input: Recognized user emotional state (e.g., in a good mood), selected dialogue text (e.g., "That's great!")

[0924] Processing: The device adjusts the generated dialogue content based on the recognized emotion.

[0925] Specific behavior:

[0926] The device receives the emotion recognition results and reevaluates the existing response text.

[0927] If necessary, modify your response to make it more appropriate.

[0928] The corrected response is saved as text and used as input data for the speech synthesis engine.

[0929] Output: Adjusted dialogue text (e.g., "That's really great!")

[0930] Step 6: Speech synthesis and response output

[0931] Input: Adjusted dialogue text (e.g., "That's really great!")

[0932] Processing: The device uses a speech synthesis engine to convert the generated text response into speech and output it to the user.

[0933] Specific behavior:

[0934] The device inputs the modified response text into a speech synthesis engine.

[0935] A speech synthesis engine converts the text data into speech data.

[0936] The generated audio is output to a speaker so that the user can hear it.

[0937] Output: A voice response played in a voice you set (e.g., "That's really great!" in a child's voice)

[0938] (Application example 2)

[0939] 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."

[0940] There is a need for methods to prevent elderly people from feeling isolated in their daily lives and to prevent cognitive decline. However, existing systems are unable to communicate with them based on their emotions, making it difficult to respond immediately in emergencies. Furthermore, there is a lack of systems equipped with monitoring functions that can respond immediately to abnormalities or emergencies within the home. As a result, it is difficult to create a safe and secure environment while ensuring the quality of life of the elderly.

[0941] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for analyzing the user's emotions, means for adjusting the dialogue content based on the analyzed emotions, and means for issuing an alarm in an emergency. This enables dialogue that is appropriate for the emotions and situation of the elderly, which not only contributes to maintaining and improving cognitive function but also enables a quick response in an emergency.

[0942] The "means for receiving audio data" refers to a device or program that has the function of taking audio information transmitted from the outside into the device.

[0943] The "means for converting received voice data into text data" refers to a device or program for converting voice information into text information using voice recognition technology.

[0944] The "means for generating dialogue content based on text data" refers to a device or program that automatically generates dialogue content with a user based on the converted character information.

[0945] The "means for outputting the generated dialogue content as voice" refers to a device or program for reproducing the text-based dialogue content using voice synthesis technology.

[0946] The "means for changing the type of voice based on the user's settings" refers to a device or program that adjusts the characteristics of the voice (tone of voice, speed, etc.) that is output in accordance with the settings that the user has specified in advance.

[0947] The "means for analyzing user emotions" refers to a device or program that identifies the user's emotional state from the user's voice or speech content and obtains the results.

[0948] The "means for adjusting the content of the dialogue based on the analyzed emotions" refers to a device or program that changes the content of the dialogue in a timely manner in accordance with the results of analyzing the user's emotions.

[0949] "Means for issuing an alarm in an emergency" refers to a device or program that detects a physical or psychological emergency in a user and issues a prompt warning.

[0950] "Audio data from video equipment" refers to audio information emitted from visual equipment such as televisions and displays.

[0951] "Content aimed at maintaining or improving cognitive function" is interactive content designed to maintain or improve the user's cognitive abilities.

[0952] This invention is a system that aims to maintain and improve cognitive function and ensure safety for elderly people in their daily lives without them feeling isolated. This system provides personalized dialogue to users through a series of processes including receiving and analyzing voice data, generating dialogue, analyzing emotions, and responding to emergencies.

[0953] The server has a means to receive audio data from an external source. For example, it retrieves audio files from the Internet and saves them in a specified directory. This means can be realized by a home device such as a smartphone or smart speaker. A standard HTTP request is used to receive the audio data.

[0954] The voice data stored on the server is converted into text data by a voice recognition engine. Existing voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text can be used for this purpose. The converted text data is used to generate dialogue with the user.

[0955] The device then runs an algorithm that generates dialogue based on the generated text data. The dialogue is then tailored to the user's emotions by selecting appropriate responses from a variety of patterns. The device uses emotion analysis engines such as EmoPy and the Microsoft Azure Emotion API.

[0956] The generated dialogue is output as voice using a speech synthesis engine, and the type of voice (tone of voice, speed, etc.) preset by the user is also supported. Google Cloud Text-to-Speech and Amazon Polly can be used for speech synthesis.

[0957] The system also has a means of issuing an alarm in an emergency. If an abnormality is detected in the user's voice or behavior, a notification is sent promptly to family members and medical personnel via the middleware, allowing for a smooth response in an emergency.

[0958] For example, if a user says, "I wonder what the weather will be like today?" the system goes through the following process:

[0959] 1. Receive audio data and save it as "tv_audio_sample.wav".

[0960] 2. The speech recognition engine converts the text into "I wonder what the weather will be like today?"

[0961] 3. The emotion analysis engine determines the user's emotion as "happy."

[0962] 4. Generate the corresponding response and output it as voice using the speech synthesis engine: "It's sunny today, what beautiful weather!"

[0963] Example prompt sentence:

[0964] Determine the emotion of the user's tone of voice and generate an appropriate response.

[0965] Input: "I wonder what the weather will be like today?"

[0966] Emotion: "happy"

[0967] Response: "It's a beautiful sunny day today!"

[0968] This system allows elderly people to enjoy appropriate dialogue that reflects their emotions, reducing feelings of isolation, maintaining and improving cognitive function, and enabling rapid response in emergencies.

[0969] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0970] Step 1:

[0971] The server receives audio data sent from an external source. This process downloads the audio data using an HTTP request and saves it locally as a file. The input is the URL of the audio data, and the output is an audio file (e.g., "tv_audio_sample.wav") saved to the local disk.

[0972] Step 2:

[0973] The server sends the saved audio file to a speech recognition engine, which converts the audio data into text data. This process uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to analyze the audio file and return text. The input is the audio file, and the output is text data (e.g., "I wonder what the weather will be like today?").

[0974] Step 3:

[0975] The device analyzes the text data obtained from the speech recognition engine to determine the user's intentions and emotions. This process uses an emotion analysis engine (e.g., EmoPy) to determine the emotion of the text data. The input is the text data, and the output is the determined emotion information (e.g., "happy").

[0976] Step 4:

[0977] The device generates dialogue content based on the analyzed emotions. This process uses a generative AI model to generate an appropriate response. The input is text data and emotion information, and the output is the generated dialogue content (e.g., "It's sunny today, the weather is very nice!").

[0978] Step 5:

[0979] The device sends the generated dialogue to a speech synthesis engine, which outputs it as speech. This process uses a speech synthesis service (e.g., Google Cloud Text-to-Speech) to convert the text into speech and play it back to the user. The input is the text of the generated dialogue, and the output is the synthesized speech.

[0980] Step 6:

[0981] The device changes the voice type based on the user's settings. This process adjusts the settings of the speech synthesis engine and outputs speech at the tone and speed specified by the user. The input is the user's settings information and the generated dialogue text, and the output is a customized voice.

[0982] Step 7:

[0983] The server performs a process to issue an alarm in the event of an emergency. This process detects abnormalities from the user's voice and behavioral analysis results, issues an alarm, and sends notifications to family members and medical personnel. The input is the user's behavior and voice analysis results, and the output is the issued alarm message.

[0984] Through this series of processing steps, the system provides interactive dialogue based on the user's emotions and situation, enabling them to maintain cognitive function and respond quickly in emergencies.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] [Fourth embodiment]

[0989] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0990] 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.

[0991] 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).

[0992] 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.

[0993] 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.

[0994] 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).

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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."

[1002] The present invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life. Specific embodiments of the present invention will be described below.

[1003] First, the system has a means for receiving voice data. The server receives the TV's voice data and stores it. Next, the terminal has a means for converting the received voice data into text data. The terminal uses voice recognition technology to analyze the voice data and generate text data.

[1004] The generated text data is processed by a dialogue content generation means. The terminal generates an appropriate response for dialogue with the user based on the text data. In this case, the response generation algorithm has multiple response patterns and randomly selects one to achieve a natural dialogue.

[1005] The system also includes a means for outputting the generated dialogue content as voice. The terminal converts the text data into voice using speech synthesis technology and outputs it to the user. The user can listen to this voice through a speaker.

[1006] The terminal also includes a means for changing the type of voice based on user settings. The user can select the type of voice (e.g., a child's voice or a grandchild's voice) on the settings screen, and the terminal changes the voice synthesis parameters according to the settings.

[1007] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." The device then generates a response such as "That's interesting!" and outputs the response using a speech synthesis engine. This speech is output in a child's voice, allowing the user to enjoy active interaction while watching television.

[1008] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content, allowing the user to constantly enjoy new dialogue and potentially helping to prevent dementia and assist hearing loss.

[1009] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[1010] The processing flow will be explained below.

[1011] Step 1:

[1012] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. This is the audio data recorded while the user was watching TV.

[1013] Step 2:

[1014] The device retrieves the stored voice data and initializes the speech recognition engine, for example, using the speech_recognition library.

[1015] Step 3:

[1016] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. The voice recognition engine analyzes the voice data and generates text data, e.g., "Today's weather is sunny."

[1017] Step 4:

[1018] The device generates dialogue content based on the generated text data. The dialogue generation algorithm selects an appropriate response from multiple response patterns, e.g., "That's interesting!"

[1019] Step 5:

[1020] The device initializes the speech synthesis engine and converts the generated dialogue into speech. For example, it uses the pyttsx3 library to synthesize speech in the specified voice type (e.g., a child's voice).

[1021] Step 6:

[1022] The device outputs the synthesized voice to the user through a speaker, allowing the user to enjoy a conversation with the AI ​​while watching TV.

[1023] Step 7:

[1024] The user selects the type of voice on the settings screen. Based on this, the device changes the parameters of the speech synthesis engine and outputs the next conversation voice in the type of voice the user desires (e.g., the voice of a grandchild).

[1025] Example 1

[1026] 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."

[1027] There is a lack of systems that can assist elderly people in hearing and stimulate communication when watching television. As a result, elderly people often feel lonely, and problems such as the progression of dementia and a decline in quality of life (QOL) occur. Conventional systems lack the ability to interact while watching television, and do not adequately assist hearing and mental health.

[1028] 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.

[1029] In this invention, the server includes means for receiving voice data, means for saving the received voice data, means for converting the voice data into text data using voice recognition technology, means for generating dialogue content based on the text data using a generative AI model, means for outputting the generated dialogue content as voice, and means for changing the type of voice based on user settings. This makes it possible to prevent dementia and assist hearing through dialogue with elderly people watching television.

[1030] "Audio data" refers to the digital or analog format of audio signals output from televisions and other audio equipment.

[1031] "Means for receiving" refers to a device or method for acquiring audio data, examples of which include a microphone or data streaming technology.

[1032] "Storage means" refers to a device or method for storing received audio data, examples of which include a database or cloud storage.

[1033] "Speech recognition technology" refers to technology that analyzes voice data and converts it into text data, and examples include speech recognition APIs.

[1034] "Text data" refers to character string information obtained by analyzing voice data.

[1035] A "generative AI model" refers to a machine learning algorithm that generates natural-sounding dialogue based on input text data.

[1036] "Means for generating dialogue content" refers to a method or device that uses a generative AI model to create appropriate dialogue based on text data.

[1037] "Means for outputting as audio" refers to a method or device for converting the generated dialogue content into an audio format and delivering it to the user via a speaker or the like.

[1038] "Means for changing the voice type based on user settings" refers to a method or device that adjusts the voice output according to the voice parameters (e.g., voice type) desired by the user.

[1039] This invention aims to provide hearing assistance and stimulate communication when elderly people watch television, thereby preventing dementia and improving quality of life (QOL). Specific embodiments of the invention are described below.

[1040] First, the server has a means to receive audio data. The server uses a microphone or data streaming technology to acquire audio data output from a television or other audio equipment. For example, it can receive audio data in the format "tv_audio_sample.wav" from a microphone connected to a television.

[1041] Next, the server stores the received audio data. To store it, it uses a database or cloud storage (e.g., AWS S3) to temporarily store the received audio data. The server saves the audio file in AWS S3 and records the file name and metadata in the database.

[1042] The device then retrieves the voice data file stored on the server and converts it into text using voice recognition technology, such as the Google Cloud Speech-to-Text API. The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate the text "Today's weather is sunny."

[1043] Next, the device uses a generative AI model to generate dialogue content based on the text data. In this process, appropriate dialogue content can be obtained by inputting a prompt sentence into the generative AI model (e.g., OpenAI GPT-4). The device sends a prompt to the generative AI model: "Text data: 'The weather is sunny today.' Please generate a response to this." The model generates a response: "That's interesting!"

[1044] The device outputs the generated dialogue as voice. Using speech synthesis technology (e.g., Amazon Polly), the text data is converted into voice and delivered to the user through the speaker. The device uses Amazon Polly to convert the text "That's interesting!" into voice data, which is then output through the speaker in a child's voice.

[1045] Furthermore, the user can select the type of voice (e.g., child's voice, grandchild's voice) on the settings screen. The device changes the voice synthesis parameters according to the setting. If the user selects "grandchild's voice" on the settings screen, the device sets the parameters of the voice synthesis engine to "grandchild's voice" and generates voices using those parameters from the next conversation.

[1046] For example, the server receives and saves an audio file called "tv_audio_sample.wav." The device analyzes this audio file using the Google Cloud Speech-to-Text API and generates text data such as "The weather is sunny today." The device then inputs the prompt text "Text data: 'The weather is sunny today.' Please generate a response to this." into the generative AI model, which generates the response "That's interesting!" This response is then converted into speech using Amazon Polly and output from the speaker in a child's voice.

[1047] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, generates dialogue content, and outputs it as voice. This allows the user to constantly enjoy new dialogue, and can help prevent dementia and assist hearing loss.

[1048] This system allows elderly people to not only watch television, but also have an active, interactive experience, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[1049] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1050] Program processing flow

[1051] Step 1:

[1052] The server receives the audio data.

[1053] Input: Audio signal from a TV or audio device

[1054] Processing: Convert the audio signal into digital format and send it to the server

[1055] Output: Digital audio data file (e.g. "tv_audio_sample.wav")

[1056] How it works: The server collects audio data using a microphone connected to the TV, converts it into digital format, and stores it.

[1057] Step 2:

[1058] The server stores the received audio data.

[1059] Input: Digital audio data file (e.g. "tv_audio_sample.wav")

[1060] Processing: Store the audio data in cloud storage (e.g. AWS S3) and record the metadata in a database.

[1061] Output: Audio data file saved on cloud storage

[1062] Specific operation: The server uploads "tv_audio_sample.wav" to AWS S3 and records the file metadata (e.g. timestamp, file size) in the database.

[1063] Step 3:

[1064] The terminal acquires the voice data file stored on the server and converts the voice data into text data using voice recognition technology.

[1065] Input: Audio data files stored in cloud storage

[1066] Processing: Analyzes the audio data using speech recognition technology (e.g., Google Cloud Speech-to-Text API) and generates text data.

[1067] Output: Converted text data (e.g. "Today's weather is sunny")

[1068] Specific operation: The device downloads "tv_audio_sample.wav" from the server and uses the Google Cloud Speech-to-Text API to generate text data saying "Today's weather is sunny."

[1069] Step 4:

[1070] The device uses a generative AI model to generate dialogue content based on the text data.

[1071] Input: Text data (e.g., "Today's weather is sunny")

[1072] Processing: Input a prompt into a generative AI model (e.g., OpenAI GPT-4) to generate appropriate dialogue.

[1073] Output: The generated dialogue (e.g., "That's interesting!")

[1074] Specific operation: The device sends the prompt "Text data: 'The weather is sunny today.' Please generate a response to this." to the generative AI model, which then generates the response "That's interesting!"

[1075] Step 5:

[1076] The terminal outputs the generated dialogue content as voice.

[1077] Input: The generated dialogue (e.g., "That's interesting!")

[1078] Processing: Convert text data into speech using speech synthesis technology (e.g., Amazon Polly)

[1079] Output: Audio data (e.g., audio generated from a child's voice)

[1080] What it does: The device converts the text "That's interesting!" into speech data using Amazon Polly and outputs it through the speaker in a child's voice.

[1081] Step 6:

[1082] The user selects the type of voice (e.g., child's voice, grandchild's voice) on the settings screen.

[1083] Input: The voice type selected by the user in the settings screen

[1084] Processing: Change the settings of the speech synthesis engine based on the selected voice parameters

[1085] Output: Updated audio parameters

[1086] Specific operation: When the user selects "Grandchild's voice" on the settings screen, the device changes the Amazon Polly parameters to "Grandchild's voice" and reflects this setting from the next conversation.

[1087] Step 7:

[1088] The server and the terminal work together to continuously receive, analyze, and generate dialogue content while the user is watching television.

[1089] Input: New audio data received periodically

[1090] Processing: Repeat steps 1 to 6

[1091] Output: Continuously generated dialogue and its audio output

[1092] Specific operation: The server periodically receives new voice data, and the terminal analyzes the data in real time, generates dialogue content, and continuously outputs it to the user as voice.

[1093] This series of processes allows users to enjoy continuous interaction while watching television, making it possible to prevent dementia and assist hearing.

[1094] (Application example 1)

[1095] 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."

[1096] Preventing dementia and improving the quality of life of elderly people is important when they spend time alone. However, for elderly people with impaired vision or hearing, communication through television and other forms of entertainment can be difficult. Safety issues also exist, such as the inability to respond appropriately in the event of a sudden illness or accident. Therefore, technological solutions are needed to enable elderly people to live independently and with peace of mind.

[1097] 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.

[1098] In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on user settings, and means for monitoring the voice and activity of the elderly person and notifying them when an abnormality is detected. This enables elderly people to live a safe and fulfilling life through dialogue to prevent dementia, even when they are spending time alone.

[1099] "Audio data" is a general term for digital or analog data that records audio.

[1100] "Text data" refers to data that is generated by analyzing voice data and expressing it as a string of characters.

[1101] "Dialogue content" refers to the content of the conversation between a human and a system, generated based on text data.

[1102] "Means for receiving" refers to a device or software that acquires audio data and passes it to an application.

[1103] "Conversion means" refers to a function or device that converts voice data into text data.

[1104] "Generation means" refers to a function or device for generating dialogue content based on text data.

[1105] "Output means" refers to a function or device for reproducing the generated dialogue content as voice.

[1106] "Means for changing the type" refers to a function or device for changing the characteristics of the voice (for example, voice quality or speaking style) according to the user's settings.

[1107] "Monitoring means" refers to functions or devices that constantly observe the sounds and activities around the elderly person and collect data.

[1108] "Means for detecting abnormalities" refers to functions or devices that recognize unusual sounds or behavioral patterns and determine whether an abnormality has occurred.

[1109] "Means of notification" refers to functions or devices that promptly alert family members or emergency services when an abnormality is detected.

[1110] This invention provides an interactive assistant system for preventing dementia and ensuring the safety of elderly people while they are alone. This system has the functions of receiving voice data, converting it to text data, generating dialogue content, synthesizing voice, changing the type of voice based on user settings, and detecting and notifying abnormalities in the elderly.

[1111] The server is equipped with a microphone to receive audio data such as environmental sounds and the voices of elderly people. This audio data is then transmitted to the server via devices such as smart glasses or head-mounted displays.

[1112] The received voice data is then converted into text data using voice recognition technology, such as Google Speech Recognition API or IBM Watson.

[1113] A dialogue with the user is then generated based on the text data. A generative AI model is used to generate the dialogue, resulting in natural responses. The generated responses are randomly selected from multiple patterns, providing a more human-like dialogue.

[1114] The generated dialogue is converted into speech using speech synthesis technology. Speech synthesis engines such as gTTS (Google Text-to-Speech) and Amazon Polly are used for speech synthesis. This speech is output to the elderly through the device's speaker.

[1115] Additionally, it includes the ability to change the voice type based on user preferences: users can select the voice type (e.g., a child's voice or a grandchild's voice) in the settings screen, and the voice synthesis parameters will automatically adjust accordingly.

[1116] The system also constantly monitors the elderly person's voice and behavior to detect any abnormalities. For example, if the system detects an abnormal voice (such as a cry for help) or activity (such as the sound of someone falling), it will immediately notify family members or emergency services. Notification methods include email, SMS, and phone call.

[1117] In a specific scenario, if an elderly person says, "I'm a little tired today," the system converts this into text data and returns a generated response such as, "That's tough. It would be good for you to get some rest." An example of the prompt sentence in this case is as follows:

[1118] "When an elderly person says, 'I feel a little tired today,' the assistant responds, 'That's tough. It would be good for you to get some rest.'"

[1119] This system can help elderly people live safe and secure lives even when they are alone, and at the same time contribute to the prevention of dementia.

[1120] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1121] Step 1:

[1122] The server receives the voice data. When the user speaks, the voice data is picked up through the microphone of the device. The picked up voice data is sent to the server in real time.

[1123] Input: User's voice data

[1124] Data processing: Collection of voice data

[1125] Output: Collected audio data

[1126] Specific operation: When a user says, "I'm a little tired today," the voice is picked up by the device's microphone.

[1127] Step 2:

[1128] The server converts the received voice data into text data using voice recognition technology. The server uses voice recognition software such as Google Speech Recognition API or IBM Watson to analyze the voice data and generate text data.

[1129] Input: Received audio data

[1130] Data processing: Converting voice data into text

[1131] Output: Generated text data

[1132] Specific operation: The server receives the voice data "I'm a little tired today" and converts it into text data "I'm a little tired today."

[1133] Step 3:

[1134] The server generates dialogue content based on the text data, using a generative AI model to achieve natural dialogue. The server inputs the text data as a prompt, and generates the dialogue content based on the response obtained from the generative AI model.

[1135] Input: Converted text data

[1136] Data processing: Dialogue content generation using generative AI models

[1137] Output: Generated dialogue

[1138] Specific operation: The server inputs the text data "I'm a little tired today" as a prompt and generates the dialogue content "That's tough. It would be good for you to take a short rest."

[1139] Step 4:

[1140] The device outputs the generated dialogue content as voice. The server sends the generated dialogue content as text data to the device, which then converts it into voice data using a speech synthesis engine such as gTTS or Amazon Polly and outputs it.

[1141] Input: Text data of the generated dialogue

[1142] Data processing: Converting text data into speech

[1143] Output: Audio data

[1144] Specific operation: The device converts the text data "That's tough, you should take a short rest" into speech and outputs it through the speaker.

[1145] Step 5:

[1146] The device adjusts voice parameters based on the voice type selected by the user. The device checks the user's settings and adjusts the gTTS or Amazon Polly voice synthesis parameters to output the voice type desired by the user (for example, a child's voice or a grandchild's voice).

[1147] Input: User's voice settings

[1148] Data processing: Adjustment of speech synthesis parameters

[1149] Output: Modified audio data

[1150] Specific operation: When the user selects "Grandchild's voice" on the device's settings screen, the device adjusts the parameters of the voice synthesis engine to match the grandchild's voice and outputs the corresponding voice.

[1151] Step 6:

[1152] The server monitors the elderly person's voice and activity and notifies them if it detects any abnormalities. The server constantly analyzes voice data and notifies family members and emergency services if it detects any abnormal voice or activity.

[1153] Input: Elderly person's voice and activity data

[1154] Data processing: Analysis using anomaly detection algorithms

[1155] Output: Notification data (email, SMS, phone)

[1156] Specific operation: If the server detects an abnormal voice saying "help," it will immediately send an email notification to family members.

[1157] 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.

[1158] This invention aims to prevent dementia and improve quality of life by providing hearing assistance and activating communication while the elderly are watching television. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more appropriate and personalized dialogue.

[1159] The overall system configuration is as follows:

[1160] First, the server has a means for receiving audio data. It receives audio data (e.g., tv_audio_sample.wav) recorded while a user is watching television via the Internet and saves it in a specified directory.

[1161] Next, the terminal has a means for converting the received voice data into text data. The terminal analyzes the voice data using a voice recognition engine and generates text data. The generated text data is processed by a means for generating dialogue content. The terminal selects an appropriate response from a plurality of response patterns and realizes a dialogue with the user.

[1162] Furthermore, the system is equipped with an emotion engine that can recognize the user's emotions by analyzing the user's tone of voice, facial expressions, or biometric signals to determine whether the user is enjoying or dissatisfied.

[1163] The system also includes a means to tailor dialogue based on the perceived emotion: for example, if the user is determined to be sad, the dialogue may be altered to provide an encouraging response.

[1164] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[1165] While the user is watching TV, the system continuously receives and analyzes the TV's audio data, and generates and outputs dialogue content. This allows the user to constantly enjoy new dialogue, making it possible to prevent dementia and assist hearing. The emotion engine provides dialogue that takes the user's emotions into account, increasing user satisfaction.

[1166] This system allows elderly people to not only watch television, but also to have an active experience that involves constant interaction, which is expected to contribute to the prevention of dementia and the maintenance of mental health.

[1167] The processing flow will be explained below.

[1168] Step 1:

[1169] The server receives the audio data (e.g., "tv_audio_sample.wav") and saves it in the specified directory. The audio data was recorded while the user was watching TV.

[1170] Step 2:

[1171] The device retrieves the stored voice data and initializes the speech recognition engine (e.g., the speech_recognition library), which analyzes the voice data and prepares it for conversion to text data.

[1172] Step 3:

[1173] The device analyzes the acquired voice data and converts it into text data using a voice recognition engine. For example, it generates text data such as "Today's weather is sunny."

[1174] Step 4:

[1175] The device inputs the generated text data into a dialogue content generation engine to generate appropriate dialogue content. The dialogue content generation engine selects an appropriate response pattern from multiple response patterns and generates the response text "That's interesting!" as an example.

[1176] Step 5:

[1177] The device initializes the emotion engine and analyzes the user's emotions. The emotion engine obtains the user's tone of voice and facial expressions through the camera and microphone and determines the user's emotions.

[1178] Step 6:

[1179] The emotion engine adjusts the dialogue content based on the user's emotions. For example, if it determines that the user is having fun, the dialogue content will be positive, such as "That's great!"

[1180] Step 7:

[1181] The device converts the adjusted dialogue into speech using a speech synthesis engine (e.g., the pyttsx3 library), which uses a voice type (e.g., a child's voice) based on the user's settings.

[1182] Step 8:

[1183] The terminal outputs the generated voice to the user through a speaker, and the user can enjoy active dialogue while watching television by listening to the adjusted dialogue voice.

[1184] Step 9:

[1185] The user can select the type of voice on the settings screen. The user settings are sent to the server and are reflected in the next and subsequent conversations.

[1186] Step 10:

[1187] The above process is carried out continuously, allowing users to enjoy constantly new conversations while receiving dementia prevention and hearing support. The emotion engine also takes the user's emotions into consideration, increasing user satisfaction.

[1188] Example 2

[1189] 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."

[1190] There is a lack of technology that can assist elderly people in hearing loss and stimulate communication while watching television, aiming to prevent dementia and improve their quality of life. Conventional systems have difficulty providing dialogue that takes into account the user's emotions, and are unable to generate dialogue that is appropriate for each individual user.

[1191] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for recognizing the user's emotion, and means for adjusting the dialogue content based on the recognized emotion. This allows elderly people to enjoy dialogue that suits their individual emotions while watching television, thereby preventing dementia and improving their quality of life.

[1192] The "means for receiving voice data" refers to technology including a communication device and software for receiving voice data sent from a user.

[1193] The "means for converting voice data into text data" includes voice recognition technology and software that analyzes received voice data and converts it into corresponding text data.

[1194] "Means for generating dialogue content" refers to a generative AI model or dialogue generation engine for creating dialogue content based on text data generated from voice data.

[1195] The "means for outputting the generated dialogue content as voice" refers to a voice synthesis technology for converting text data into voice data and providing it to the user through a voice output device.

[1196] The "means for changing the type of voice" is a software function for changing the characteristics of the voice to be output (e.g., type of voice, pitch) according to the user's settings.

[1197] "Means for recognizing the user's emotions" refers to emotion recognition technology or software that analyzes the user's tone of voice, facial expressions, biometric signals, etc. to determine the user's emotional state.

[1198] The "means for adjusting the dialogue content based on the recognized emotion" is a dialogue adjustment technique for appropriately correcting or adjusting the dialogue content generated in accordance with the emotional state of the user.

[1199] "Audio data from a video device" refers to an audio signal output from a television or other audiovisual device.

[1200] "Content aimed at preventing dementia" refers to dialogue and information provision designed to maintain or improve the user's cognitive function.

[1201] The present invention relates to a system that provides hearing assistance and stimulates communication while elderly people watch television, aiming to prevent dementia and improve their quality of life. This system is characterized by its ability to consistently perform processes from receiving and analyzing voice data, generating dialogue content, and outputting the voice, to adjusting the dialogue based on the user's emotions. A specific embodiment of this system is described below.

[1202] First, the server receives audio data. The audio data is recorded while the user is watching TV and is sent to the server via the Internet. As an example, consider the audio file "tv_audio_sample.wav". This audio file is saved in a specified directory on the server.

[1203] Next, the device receives the voice data received from the server. The device analyzes the voice data using a voice recognition engine (e.g., Google Speech-to-Text API). Once the voice data is analyzed, text data is generated as a result. For example, the audio file "tv_audio_sample.wav" is converted into text data such as "The weather is sunny today."

[1204] After the text data is generated, the device uses a dialogue generation engine (e.g., GPT-3) to generate appropriate dialogue content, which is then saved as text, such as "That's great!"

[1205] The device also incorporates an emotion recognition engine (e.g., IBM Watson Tone Analyzer) that recognizes the user's emotions. This engine analyzes the user's tone of voice, facial expressions, and biometric signals to determine their emotional state, such as whether they are enjoying themselves or feeling dissatisfied. For example, if it determines that the user is happy, it adjusts the dialogue content generated based on that result. Specifically, if the user is happy, positive dialogue content is selected.

[1206] Finally, the generated dialogue is converted into speech by a speech synthesis engine. The device outputs this speech through a speaker. The type of voice can be changed based on the user's settings. For example, the user can select a child's voice to output the response.

[1207] This system allows elderly people to enjoy constantly new dialogues, helping to prevent dementia and assisting hearing. In addition, the emotion engine provides dialogue that takes the user's emotions into account, improving user satisfaction.

[1208] Specific examples

[1209] For example, the server receives and saves a television audio file called "tv_audio_sample.wav." The device analyzes this audio file and generates text data such as "The weather is sunny today." If the emotion engine determines that the user is in a good mood based on their tone of voice, the device generates a response such as "That's great!" and outputs the voice using the speech synthesis engine. This voice is output in a child's voice selected by the user, allowing the user to enjoy active interaction while watching TV.

[1210] Prompt Sentence Examples

[1211] The voice data "tv_audio_sample.wav" recorded while the user was watching TV was received. The text obtained using the speech recognition engine is "It's a nice day today." In this case, if the user's emotion is recognized as good mood, please show the appropriate response to generate.

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Step 1: Receiving and storing audio data

[1214] Input: Audio data recorded while the user is watching TV (e.g., tv_audio_sample.wav)

[1215] Processing: The server receives the audio data via the Internet.

[1216] Specific behavior:

[1217] The user records audio through a dedicated microphone.

[1218] The recorded audio data is sent from the terminal to the server.

[1219] The server detects the data reception trigger, captures the connected data stream, and saves it as a file.

[1220] Output: Audio data file saved in the specified directory on the server (e.g. / path / to / tv_audio_sample.wav)

[1221] Step 2: Analyzing the audio data and generating text data

[1222] Input: Audio data file saved on the server (e.g., / path / to / tv_audio_sample.wav)

[1223] Processing: The device uses a speech recognition engine to analyze the voice data and generate text data.

[1224] Specific behavior:

[1225] The terminal downloads the audio data from the specified directory on the server.

[1226] Initialize the speech recognition engine (e.g., Google Speech-to-Text API) and begin analyzing the audio data.

[1227] Once the analysis is complete, text data is generated and saved in temporary memory or as a file.

[1228] Output: Generated text data (e.g., "Today's weather is sunny")

[1229] Step 3: Dialogue generation and response selection

[1230] Input: Generated text data (e.g., "Today's weather is sunny")

[1231] Processing: The device uses a dialogue generation engine to generate appropriate dialogue content and selects the most appropriate response from multiple response candidates.

[1232] Specific behavior:

[1233] The terminal creates a prompt based on the generated text data.

[1234] The prompt is fed into a generative AI model (e.g., GPT-3) to generate an appropriate response.

[1235] Select the best response from multiple possible responses and save it as text.

[1236] Output: Selected dialogue text (e.g., "That's great!")

[1237] Step 4: Recognize emotions

[1238] Input: Data such as the user's tone of voice, facial expressions, and biometric signals

[1239] Processing: The device uses an emotion recognition engine to analyze the user's emotions.

[1240] Specific behavior:

[1241] The device collects emotion-related data from the microphone, camera, and biometric sensors.

[1242] An emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes this data to determine the user's emotional state.

[1243] The analysis results are saved in a database.

[1244] Output: Perceived user emotional state (e.g., good mood)

[1245] Step 5: Adjust the conversation

[1246] Input: Recognized user emotional state (e.g., in a good mood), selected dialogue text (e.g., "That's great!")

[1247] Processing: The device adjusts the generated dialogue content based on the recognized emotion.

[1248] Specific behavior:

[1249] The device receives the emotion recognition results and reevaluates the existing response text.

[1250] If necessary, modify your response to make it more appropriate.

[1251] The corrected response is saved as text and used as input data for the speech synthesis engine.

[1252] Output: Adjusted dialogue text (e.g., "That's really great!")

[1253] Step 6: Speech synthesis and response output

[1254] Input: Adjusted dialogue text (e.g., "That's really great!")

[1255] Processing: The device uses a speech synthesis engine to convert the generated text response into speech and output it to the user.

[1256] Specific behavior:

[1257] The device inputs the modified response text into a speech synthesis engine.

[1258] A speech synthesis engine converts the text data into speech data.

[1259] The generated audio is output to a speaker so that the user can hear it.

[1260] Output: A voice response played in a voice you set (e.g., "That's really great!" in a child's voice)

[1261] (Application example 2)

[1262] 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."

[1263] There is a need for methods to prevent elderly people from feeling isolated in their daily lives and to prevent cognitive decline. However, existing systems are unable to communicate with them based on their emotions, making it difficult to respond immediately in emergencies. Furthermore, there is a lack of systems equipped with monitoring functions that can respond immediately to abnormalities or emergencies within the home. As a result, it is difficult to create a safe and secure environment while ensuring the quality of life of the elderly.

[1264] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data, means for converting the received voice data into text data, means for generating dialogue content based on the text data, means for outputting the generated dialogue content as voice, means for changing the type of voice based on a user setting, means for analyzing the user's emotions, means for adjusting the dialogue content based on the analyzed emotions, and means for issuing an alarm in an emergency. This enables dialogue that is appropriate for the emotions and situation of the elderly, which not only contributes to maintaining and improving cognitive function but also enables a quick response in an emergency.

[1265] The "means for receiving audio data" refers to a device or program that has the function of taking audio information transmitted from the outside into the device.

[1266] The "means for converting received voice data into text data" refers to a device or program for converting voice information into text information using voice recognition technology.

[1267] The "means for generating dialogue content based on text data" refers to a device or program that automatically generates dialogue content with a user based on the converted character information.

[1268] The "means for outputting the generated dialogue content as voice" refers to a device or program for reproducing the text-based dialogue content using voice synthesis technology.

[1269] The "means for changing the type of voice based on the user's settings" refers to a device or program that adjusts the characteristics of the voice (tone of voice, speed, etc.) that is output in accordance with the settings that the user has specified in advance.

[1270] The "means for analyzing user emotions" refers to a device or program that identifies the user's emotional state from the user's voice or speech content and obtains the results.

[1271] The "means for adjusting the content of the dialogue based on the analyzed emotions" refers to a device or program that changes the content of the dialogue in a timely manner in accordance with the results of analyzing the user's emotions.

[1272] "Means for issuing an alarm in an emergency" refers to a device or program that detects a physical or psychological emergency in a user and issues a prompt warning.

[1273] "Audio data from video equipment" refers to audio information emitted from visual equipment such as televisions and displays.

[1274] "Content aimed at maintaining or improving cognitive function" is interactive content designed to maintain or improve the user's cognitive abilities.

[1275] This invention is a system that aims to maintain and improve cognitive function and ensure safety for elderly people in their daily lives without them feeling isolated. This system provides personalized dialogue to users through a series of processes including receiving and analyzing voice data, generating dialogue, analyzing emotions, and responding to emergencies.

[1276] The server has a means to receive audio data from an external source. For example, it retrieves audio files from the Internet and saves them in a specified directory. This means can be realized by a home device such as a smartphone or smart speaker. A standard HTTP request is used to receive the audio data.

[1277] The voice data stored on the server is converted into text data by a voice recognition engine. Existing voice recognition services such as Google Cloud Speech-to-Text and IBM Watson Speech to Text can be used for this purpose. The converted text data is used to generate dialogue with the user.

[1278] The device then runs an algorithm that generates dialogue based on the generated text data. The dialogue is then tailored to the user's emotions by selecting appropriate responses from a variety of patterns. The device uses emotion analysis engines such as EmoPy and the Microsoft Azure Emotion API.

[1279] The generated dialogue is output as voice using a speech synthesis engine, and the type of voice (tone of voice, speed, etc.) preset by the user is also supported. Google Cloud Text-to-Speech and Amazon Polly can be used for speech synthesis.

[1280] The system also has a means of issuing an alarm in an emergency. If an abnormality is detected in the user's voice or behavior, a notification is sent promptly to family members and medical personnel via the middleware, allowing for a smooth response in an emergency.

[1281] For example, if a user says, "I wonder what the weather will be like today?" the system goes through the following process:

[1282] 1. Receive audio data and save it as "tv_audio_sample.wav".

[1283] 2. The speech recognition engine converts the text into "I wonder what the weather will be like today?"

[1284] 3. The emotion analysis engine determines the user's emotion as "happy."

[1285] 4. Generate the corresponding response and output it as voice using the speech synthesis engine: "It's sunny today, what beautiful weather!"

[1286] Example prompt sentence:

[1287] Determine the emotion of the user's tone of voice and generate an appropriate response.

[1288] Input: "I wonder what the weather will be like today?"

[1289] Emotion: "happy"

[1290] Response: "It's a beautiful sunny day today!"

[1291] This system allows elderly people to enjoy appropriate dialogue that reflects their emotions, reducing feelings of isolation, maintaining and improving cognitive function, and enabling rapid response in emergencies.

[1292] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1293] Step 1:

[1294] The server receives audio data sent from an external source. This process downloads the audio data using an HTTP request and saves it locally as a file. The input is the URL of the audio data, and the output is an audio file (e.g., "tv_audio_sample.wav") saved to the local disk.

[1295] Step 2:

[1296] The server sends the saved audio file to a speech recognition engine, which converts the audio data into text data. This process uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to analyze the audio file and return text. The input is the audio file, and the output is text data (e.g., "I wonder what the weather will be like today?").

[1297] Step 3:

[1298] The device analyzes the text data obtained from the speech recognition engine to determine the user's intentions and emotions. This process uses an emotion analysis engine (e.g., EmoPy) to determine the emotion of the text data. The input is the text data, and the output is the determined emotion information (e.g., "happy").

[1299] Step 4:

[1300] The device generates dialogue content based on the analyzed emotions. This process uses a generative AI model to generate an appropriate response. The input is text data and emotion information, and the output is the generated dialogue content (e.g., "It's sunny today, the weather is very nice!").

[1301] Step 5:

[1302] The device sends the generated dialogue to a speech synthesis engine, which outputs it as speech. This process uses a speech synthesis service (e.g., Google Cloud Text-to-Speech) to convert the text into speech and play it back to the user. The input is the text of the generated dialogue, and the output is the synthesized speech.

[1303] Step 6:

[1304] The device changes the voice type based on the user's settings. This process adjusts the settings of the speech synthesis engine and outputs speech at the tone and speed specified by the user. The input is the user's settings information and the generated dialogue text, and the output is a customized voice.

[1305] Step 7:

[1306] The server performs a process to issue an alarm in the event of an emergency. This process detects abnormalities from the user's voice and behavioral analysis results, issues an alarm, and sends notifications to family members and medical personnel. The input is the user's behavior and voice analysis results, and the output is the issued alarm message.

[1307] Through this series of processing steps, the system provides interactive dialogue based on the user's emotions and situation, enabling them to maintain cognitive function and respond quickly in emergencies.

[1308] 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.

[1309] 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.

[1310] 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.

[1311] 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.

[1312] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1313] 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.

[1314] 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).

[1315] 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.

[1316] 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."

[1317] 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.

[1318] 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).

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] The following is further disclosed regarding the above embodiment.

[1330] (Claim 1)

[1331] means for receiving audio data;

[1332] means for converting received voice data into text data;

[1333] means for generating dialogue content based on text data;

[1334] means for outputting the generated dialogue content as voice;

[1335] A system including a means for changing the type of audio based on user settings.

[1336] (Claim 2)

[1337] 2. The system of claim 1, wherein the received audio data is television audio data.

[1338] (Claim 3)

[1339] 2. The system according to claim 1, wherein the dialogue content generated based on the text data is aimed at preventing dementia.

[1340] "Example 1"

[1341] (Claim 1)

[1342] means for receiving audio data;

[1343] means for storing the received audio data;

[1344] A means for converting voice data into text data using voice recognition technology;

[1345] a means for generating dialogue content based on the text data using a generative AI model;

[1346] means for outputting the generated dialogue content as voice;

[1347] A system including a means for changing the type of audio based on user settings.

[1348] (Claim 2)

[1349] 2. The system of claim 1, wherein the received audio data is audio data from a video device.

[1350] (Claim 3)

[1351] 2. The system according to claim 1, wherein the dialogue content generated based on the text data is aimed at preventing dementia.

[1352] "Application Example 1"

[1353] (Claim 1)

[1354] means for receiving audio data;

[1355] means for converting received voice data into text data;

[1356] means for generating dialogue content based on text data;

[1357] means for outputting the generated dialogue content as voice;

[1358] means for changing the type of voice based on a user setting;

[1359] A system that monitors the voice and activity of elderly people and includes a means to notify them if an abnormality is detected.

[1360] (Claim 2)

[1361] 10. The system of claim 1, wherein the received audio data is obtained from the audio environment surrounding the senior citizen.

[1362] (Claim 3)

[1363] 2. The system according to claim 1, wherein the dialogue content generated based on the text data is aimed at supporting the safety and health of elderly people.

[1364] "Example 2: Combining Emotion Engines"

[1365] (Claim 1)

[1366] means for receiving audio data;

[1367] means for converting received voice data into text data;

[1368] means for generating dialogue content based on text data;

[1369] means for outputting the generated dialogue content as voice;

[1370] means for changing the type of voice based on a user setting;

[1371] means for recognizing a user's emotion;

[1372] The system includes means for adjusting dialogue content based on the recognized emotion.

[1373] (Claim 2)

[1374] 2. The system of claim 1, wherein the received audio data is audio data from a video device.

[1375] (Claim 3)

[1376] 2. The system according to claim 1, wherein the dialogue content generated based on the text data is aimed at preventing dementia.

[1377] "Application example 2 when combining emotion engines"

[1378] (Claim 1)

[1379] means for receiving audio data;

[1380] means for converting received voice data into text data;

[1381] means for generating dialogue content based on text data;

[1382] means for outputting the generated dialogue content as voice;

[1383] means for changing the type of voice based on a user setting;

[1384] means for analyzing user emotions;

[1385] a means for adjusting the dialogue content based on the analyzed emotion;

[1386] The system includes a means for issuing an alarm in the event of an emergency.

[1387] (Claim 2)

[1388] 2. The system according to claim 1, wherein the received audio data is audio data from a video device.

[1389] (Claim 3)

[1390] 2. The system according to claim 1, wherein the dialogue content generated based on the text data is aimed at maintaining or improving cognitive function. [Explanation of symbols]

[1391] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving audio data; means for converting received voice data into text data; means for generating dialogue content based on text data; means for outputting the generated dialogue content as voice; A system including a means for changing the type of audio based on user settings.

2. 2. The system of claim 1, wherein the received audio data is television audio data.

3. 2. The system according to claim 1, wherein the dialogue content generated based on the text data is aimed at preventing dementia.

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