System

Abstract

A system is provided.SOLUTION: A system comprising: recording means for collecting ambient sound; converting means for converting the recorded sound data into a digital signal; compressing means for compressing the converted sound data; transmitting means for transmitting the compressed sound data to a server; generating means for generating visual information and a vibration pattern based on the identified sound; returning means for transmitting the generated visual information and vibration pattern to a terminal; and displaying means for displaying the returned visual information and vibrating according to the vibration pattern.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional voice recognition systems have had difficulty quickly and accurately analyzing surrounding sounds and visually presenting the results to users. Furthermore, the vibration notification function, which allows users to become aware of important sounds without constantly checking the device, was insufficient. This posed a challenge, particularly for the hearing impaired and users who prefer to visually recognize environmental sounds, as it was difficult to provide sufficient information. [Means for solving the problem]

[0005] The present invention provides a system that includes a terminal that collects ambient sounds, converts them into digital signals, compresses them, and transmits them to a server, and a server that analyzes the audio data, identifies specific sounds, and generates corresponding visual information and vibration patterns. Specifically, the system combines audio data collection, conversion, compression, transmission, identification, generation, return, and display, allowing users to visually check ambient sounds in real time and receive vibration notifications. This system solves conventional problems and provides a more comprehensive and intuitive user experience.

[0006] "Recording means" refers to a device or function for collecting surrounding sounds.

[0007] The "conversion means" is a function for converting recorded audio data into a digital signal.

[0008] The "compression means" is a function for compressing the converted audio data.

[0009] The "transmission means" is a function for transmitting compressed audio data to a server.

[0010] The "identification means" is a function that analyzes audio data in the server and identifies specific sounds.

[0011] The "generating means" is a function that generates visual information and vibration patterns based on the identified sound.

[0012] The "returning means" is a function for transmitting the generated visual information and vibration pattern to the terminal.

[0013] The "display means" is a function that displays the returned visual information and vibrates according to the vibration pattern.

[0014] A "machine learning algorithm" is a type of artificial intelligence technique that analyzes data and recognizes certain patterns or characteristics.

[0015] "Visual information" refers to data that conveys information visually to users, such as text and icons.

[0016] A "vibration pattern" is a sequence of vibrations, including intensity and duration, designed to convey a specific notification. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention provides a system for visually converting ambient sounds in real time and notifying the user of the converted sounds. Specific embodiments of the system will be described below.

[0039] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[0040] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0041] The server receives the audio data sent from the device. After receiving it, the audio data is decompressed and analyzed using a data analysis algorithm. During this analysis, the server identifies specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.).

[0042] The server generates corresponding visual information and vibration patterns based on the identified sounds. The visual information may include a text message or icon corresponding to the particular sound. For example, if a car horn sound is identified, the server generates the text "Car horn sound heard" and a car icon.

[0043] The server then transmits the generated visual information and vibration pattern to the device, which then displays the received visual information on its screen to inform the user of the surrounding situation.The device also vibrates according to the vibration pattern to notify the user of the occurrence of a specific sound.

[0044] As a concrete example, suppose a user is standing on a train platform. At this time, a device that collects surrounding sounds detects an announcement of a train's arrival. The audio data is sent to a server, where it is analyzed, generating a text message saying "A train is arriving" and a train icon. These are then sent to the device, which then displays them on the device's screen, and the device vibrates briefly to notify the user.

[0045] In this way, the present invention is a system that allows users to visually recognize surrounding sounds and provides necessary information in real time, thereby providing a more comprehensive and intuitive user experience for people with hearing impairments and users who want to visually recognize environmental sounds.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user launches an app on the device, which then activates the device's microphone and prepares to collect ambient sounds.

[0049] Step 2:

[0050] The device uses a microphone to collect ambient sounds in real time, and audio data is captured continuously.

[0051] Step 3:

[0052] The analog voice data collected by the terminal is converted into a digital signal using an AD converter.

[0053] Step 4:

[0054] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), thereby reducing the size of the transmitted data.

[0055] Step 5:

[0056] The device sends the compressed audio data to the server via the Internet, using protocols such as HTTP to transfer the data.

[0057] Step 6:

[0058] The server receives the compressed audio data sent from the terminal and prepares it to decode the received data into an appropriate format.

[0059] Step 7:

[0060] The server decompresses the compressed audio data and restores it to the original digital audio data.

[0061] Step 8:

[0062] The server analyzes the audio data using an analysis algorithm (e.g., a machine learning model or a voice recognition algorithm) to identify specific sounds (e.g., a car horn, a dog barking, a doorbell).

[0063] Step 9:

[0064] The server generates corresponding visual information (e.g., a text message or icon) and vibration patterns based on the identified sound.

[0065] Step 10:

[0066] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[0067] Step 11:

[0068] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[0069] Step 12:

[0070] The device displays the visual information it receives (e.g., a text message saying "I hear a car horn" and a car icon) on the screen.

[0071] Step 13:

[0072] The device vibrates according to the vibration pattern received by the terminal, and provides the user with a notification for a specific sound based on the vibration pattern.

[0073] This series of processes allows users to visually check surrounding sounds in real time and receive vibration notifications. For example, if a user is on a train platform, the device detects the train arrival announcement, analyzes it on the server, and generates appropriate visual information and vibration notifications to notify the user, allowing them to understand their surroundings.

[0074] Example 1

[0075] 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."

[0076] Conventional voice notification systems lack the ability to accurately identify sound types and notify users as visual information or vibration patterns, which makes it difficult for hearing-impaired users and users in noisy environments to effectively recognize surrounding sound information in real time.

[0077] 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.

[0078] In this invention, the server includes a decompression means for decompressing audio data, an identification means for analyzing the decompressed data and identifying specific sounds, and a generation means for generating visual information and vibration patterns based on the identified sounds, thereby enabling the server to accurately identify surrounding sounds and notify the user in real time as visual information and vibration patterns.

[0079] A "collection means" is a function or device for collecting ambient sounds.

[0080] "Conversion means" refers to a function or device that converts collected audio data into a digital signal.

[0081] "Compression means" refers to a function or device that compresses the converted audio data.

[0082] The "transmission means" is a function or device that transmits compressed audio data to a server via a data communication network.

[0083] The "decompression means" is a function or device that decompresses audio data received by the server.

[0084] The "identification means" is a function or device that analyzes the decompressed audio data and identifies a particular sound.

[0085] "Generating means" refers to a function or device that generates visual information and vibration patterns based on the identified sounds.

[0086] The "returning means" is a function or device that transmits the generated visual information and vibration pattern to the terminal.

[0087] "Display means" is a function or device that displays the returned visual information and vibrates according to a vibration pattern.

[0088] The present invention provides a system that visually converts ambient sounds in real time and notifies the user of the results. This system is realized by taking the following specific steps.

[0089] First, the user starts a device with a dedicated application installed. This device is equipped with a microphone for collecting ambient sounds. When the user starts the application, the device's microphone is enabled and begins to continuously collect ambient sounds.

[0090] The device then converts the collected audio into a digital signal. This digital signal conversion is the process of converting analog audio data into a digital format, and involves sampling and quantization. The digital audio data is then compressed using a compression algorithm. Examples of compression algorithms used include MP3 and AAC. This compressed data is then sent over the internet to a server.

[0091] The server receives the voice data sent from the device. After receiving it, the voice data is decompressed and restored to its original digital signal. The server then uses a data analysis algorithm to analyze the voice data and identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). This analysis is performed using a machine learning algorithm.

[0092] After the server identifies a specific sound, it generates corresponding visual information and vibration patterns based on the identified sound. For example, if a car horn sound is identified, the text "I hear a car horn" and a car icon are generated. If a dog bark is identified, the text "Dog barking" and a dog icon are generated. The generated visual information and vibration patterns are then sent back to the device via the Internet.

[0093] The device displays the received visual information on the screen to notify the user of the surrounding situation, and at the same time, the device vibrates according to a specified vibration pattern and notifies the user that a specific sound has occurred.

[0094] As a concrete example, consider a case where a user is on a train platform. At this time, the user's device collects train arrival announcements and sends the audio data to a server. When the server analyzes the audio data and identifies the train arrival announcement, it generates a text message saying "A train is arriving" and a train icon. This information is sent to the device and displayed on the device's screen, and at the same time, the device vibrates briefly to notify the user.

[0095] Specific examples of input prompts to generative AI models include the following:

[0096] Prompt: Describe a system that visually translates ambient sound into real-time notifications. Include specific hardware and software processes, and provide examples.

[0097] This system can provide real-time information to hearing-impaired users and those who want to visually recognize surrounding sounds in noisy environments, offering a more comprehensive and intuitive user experience.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] Your device activates the microphone to collect ambient sounds.

[0101] Input: The user launches the dedicated application.

[0102] Data processing: Acquire analog audio signals

[0103] Output: Acquired raw analog audio signal

[0104] Specific operation: The device launches the application in response to user operation and enables the built-in microphone, which then continuously collects ambient sounds.

[0105] Step 2:

[0106] The device converts the collected audio into a digital signal.

[0107] Input: Raw analog audio signal

[0108] Data processing: Analog to digital conversion through sampling and quantization

[0109] Output: Digital audio data

[0110] How it works: The analog-to-digital converter (ADC) circuit in the device converts the analog voice signal into digital data, which represents the voice as a bit string of 0s and 1s.

[0111] Step 3:

[0112] The terminal compresses the audio data.

[0113] Input: Digital audio data

[0114] Data processing: Compression using a compression algorithm (e.g. MP3, AAC, etc.)

[0115] Output: Compressed audio data

[0116] How it works: The device passes digital audio data to a compression algorithm, which compresses it to reduce its size. This compressed data is then sent to the server.

[0117] Step 4:

[0118] The terminal transmits the compressed data to the server.

[0119] Input: Compressed audio data

[0120] Data calculation: transmission over a data communication network

[0121] Output: Compressed data sent to the server

[0122] What it does: Your device uses your internet connection (Wi-Fi or mobile data) to send compressed audio data to the server. The data is split into packets and sent.

[0123] Step 5:

[0124] The server decompresses the received audio data.

[0125] Input: Compressed audio data

[0126] Data operation: Decompression by decompression algorithm

[0127] Output: Decompressed digital audio data

[0128] What it does: The server passes the compressed audio data it receives through a decompression algorithm, restoring it to the original digital audio data, which can then be analyzed.

[0129] Step 6:

[0130] The server analyzes the audio data and identifies specific sounds.

[0131] Input: Decompressed digital audio data

[0132] Data Computing: Machine Learning Algorithms for Speech Analysis and Sound Identification

[0133] Output: Identified specific sound information

[0134] What it does: The server runs a speech analysis algorithm to identify specific sounds (e.g., car horn, dog barking, doorbell, etc.) from the decompressed digital audio data. It then uses a machine learning model to extract sound features and match them with pre-trained sound patterns.

[0135] Step 7:

[0136] The server generates corresponding visual information and vibration patterns based on the identified sounds.

[0137] Input: Identified specific sound information

[0138] Data Computing: Generation of visual information (text and icons) and vibration patterns

[0139] Output: Visual information and vibration patterns

[0140] Specific behavior: The server generates visual information (e.g., a text message saying "I hear a dog barking" and a dog icon) and vibration patterns corresponding to the identified sound. Corresponding data is generated.

[0141] Step 8:

[0142] The server generates visual information and transmits vibration patterns to the terminal.

[0143] Input: Generated visual information and vibration patterns

[0144] Data calculation: Sending data to the terminal

[0145] Output: Sending visual information and vibration patterns to the device

[0146] How it works: The server uses an internet connection to send the generated visual information and vibration patterns to the device. The data is divided into packets and sent to the device.

[0147] Step 9:

[0148] The device displays the received visual information on the screen and notifies the user by vibrating.

[0149] Input: Received visual information and vibration patterns

[0150] Data calculation: Screen display and vibration pattern execution

[0151] Output: User notification

[0152] Specific operation: The device displays the received visual information on the screen and vibrates according to the specified vibration pattern, thereby notifying the user visually and tactilely that a specific sound has occurred. For example, a text message saying "Train is arriving" is displayed on the screen, a train icon is displayed, and the device vibrates briefly.

[0153] (Application example 1)

[0154] 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."

[0155] Conventional systems often make it difficult to visually recognize surrounding sounds. In particular, in certain situations in physical stores (e.g., sales start dates or in-store announcements), appropriate information provision was lacking for hearing-impaired users and users who prefer to visually understand surrounding sounds. Furthermore, there were limited means of providing context-sensitive information in real time.

[0156] 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.

[0157] In this invention, the server includes a recording means for collecting ambient sounds, a conversion means for converting the recorded audio data into a digital signal, a compression means for compressing the converted audio data, a transmission means for transmitting the compressed audio data to the server, an identification means in the server for analyzing the audio data and identifying a specific sound, a generation means for generating visual information and a vibration pattern based on the identified sound, a return means for transmitting the generated visual information and vibration pattern to the terminal, a display means for displaying the returned visual information and vibrating according to the vibration pattern, a means for generating the visual information and vibration pattern when the specific sound is related to a specific situation in the physical store (e.g., the start of a sale or an in-store announcement), and a means for providing the visual information and vibration pattern via a smartphone, a head-mounted display, or the like. This enables users to be notified in real time by appropriate visual information and vibration patterns in specific situations in the physical store.

[0158] "Ambient sounds" refers to any sounds that come from the surrounding environment and can be detected through the ear.

[0159] "Recording means" refers to a device or function that continuously collects ambient sounds and stores them as digital data.

[0160] "Conversion means" refers to a device or function that converts collected audio data into a digital signal.

[0161] The "compression means" refers to a device or function for efficiently compressing the converted audio data to reduce the amount of data.

[0162] "Transmission means" refers to a device or function that transmits compressed audio data to a server via the Internet.

[0163] The "identification means" refers to a device or function that analyzes audio data in the server and identifies a specific sound.

[0164] "Generating means" refers to a device or function that generates visual information and vibration patterns based on the identified sounds.

[0165] The "returning means" refers to a device or function that transmits the generated visual information and vibration pattern to the terminal.

[0166] "Display means" refers to a device or function that displays the returned visual information and vibrates according to a vibration pattern.

[0167] A "specific situation" refers to a specific event or state that occurs within a physical store (e.g., the start of a sale or an in-store announcement).

[0168] A "smartphone" is a type of mobile phone that has multiple functions such as voice calls, Internet access, and application execution.

[0169] A "head-mounted display" is a display device that can be worn on the user's head and has the function of presenting visual information.

[0170] A specific embodiment of the present invention will be described below: This system analyzes surrounding sounds in real time and provides visual information and vibration notifications to the user based on the results.

[0171] First, a user uses a device with a dedicated application. The device is equipped with a microphone for collecting ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[0172] The collected voice data is converted into a digital signal by a conversion means in the terminal, and then the digital signal is compressed by a compression means, and the compressed voice data is transmitted to a server via the Internet through a transmission means.

[0173] The server decompresses the received audio data and analyzes it using a recognition means, which uses a machine learning algorithm to identify specific sounds (e.g., a sale announcement, a new product arrival announcement, etc.). Based on the identified sound, the server generates corresponding visual information (e.g., a text message or icon) and vibration patterns using a generation means.

[0174] The generated visual information and vibration pattern are transmitted to the terminal using the return means, and the terminal displays the returned information to the user through the display means and notifies the user that a specific sound has been generated by the vibration.

[0175] As a concrete example, suppose a user is shopping in a physical store. An application collects ambient sounds and sends them to a server. When an announcement of a sale is detected in the store, the server generates a text message saying "Sale has started" and a sale icon and sends it to the device. This notifies the user by displaying a visual message on the device screen and a short vibration.

[0176] The system includes the following hardware and software:

[0177] Hardware: Your smartphone's microphone, speaker, display, and vibrator.

[0178] software:

[0179] Pyaudio: Audio recording.

[0180] Requests: Server communication.

[0181] haptics(tentative module): vibration notifications.

[0182] Python: A programming language.

[0183] Examples of specific generated AI prompt sentences are as follows:

[0184] "You are a user of an application that visually translates surrounding sounds in real time. Right now, while you are shopping in a supermarket, you hear an announcement that a sale has started. How would the application respond?"

[0185] As a result of the above aspects, the present invention can provide users with visual and vibrational notifications in specific sound environments within physical stores, thereby improving convenience and safety.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] When the user launches the application, the device's microphone is enabled.

[0189] Input: User action (application launch).

[0190] Output: The microphone is enabled and begins collecting ambient sounds.

[0191] Specific behavior: The application starts and the microphone device transitions to the active state.

[0192] Step 2:

[0193] The device continuously collects ambient sounds through a microphone.

[0194] Input: Ambient sounds.

[0195] Output: Recorded audio data.

[0196] What it does: The microphone captures ambient sounds and records them as audio signals.

[0197] Step 3:

[0198] The terminal converts the collected voice data into a digital signal.

[0199] Input: Recorded audio data (analog signal).

[0200] Output: Audio data converted into a digital signal.

[0201] What it does: Converts analog audio signals into digital data using the Pyaudio library.

[0202] Step 4:

[0203] The converted audio data is compressed using a compression algorithm.

[0204] Input: Audio data converted into a digital signal.

[0205] Output: Compressed audio data.

[0206] Specific operation: A codec is used to compress the audio data, reducing the amount of data.

[0207] Step 5:

[0208] The terminal transmits the compressed audio data to a server via the Internet.

[0209] Input: Compressed audio data.

[0210] Output: The data sent to the server.

[0211] What it does: Uses the Requests library to send compressed audio data to the server via an HTTP POST request.

[0212] Step 6:

[0213] The server decompresses the received audio data and analyzes it using the identification means.

[0214] Input: Compressed audio data sent to the server.

[0215] Output: The analyzed audio data.

[0216] What it does: It unpacks the compressed data on the server side and uses machine learning algorithms to identify specific sounds.

[0217] Step 7:

[0218] The server generates visual information and vibration patterns based on the identified sounds.

[0219] Input: Parsed audio data.

[0220] Output: Visual information and vibration patterns.

[0221] What it does: It runs an algorithm that generates text messages, icons, and vibration patterns.

[0222] Step 8:

[0223] The generated visual information and vibration pattern are transmitted to the terminal using a return means.

[0224] Input: Visual information and vibration patterns.

[0225] Output: Information sent back to the device.

[0226] Specific operation: Visual information and vibration patterns are sent from the server to the device using an HTTP response.

[0227] Step 9:

[0228] The terminal displays the returned visual information and vibrates according to the vibration pattern.

[0229] Input: Visual information and vibration patterns from the server.

[0230] Output: Visual and vibration notification to the user.

[0231] What it does: Shows text messages and icons on the display and vibrates for notifications.

[0232] 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.

[0233] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[0234] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the app, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0235] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0236] The server receives the audio data sent from the device, which is then decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., car horns, dogs barking, doorbells, etc.).

[0237] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to a server, which then evaluates the user's emotional state based on this emotion recognition information.

[0238] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, it will provide more intuitive and concise notification information, while if the user is relaxed, it will adjust to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[0239] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[0240] For example, imagine a user is on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the data to the server. The server recognizes the user's level of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[0241] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0242] The processing flow will be explained below.

[0243] Step 1:

[0244] The user launches the app on their device. The app activates the device's microphone and camera and prepares to collect ambient sounds and the user's emotions.

[0245] Step 2:

[0246] The device uses a microphone to collect ambient sounds in real time, while simultaneously using a camera and voice analysis functions to capture the user's facial expressions and voice tone, using facial recognition algorithms and voice emotion analysis algorithms.

[0247] Step 3:

[0248] The analog voice data collected by the device is converted into a digital signal using an AD converter. At the same time, the user's emotional data (facial expressions and voice features) is also digitized.

[0249] Step 4:

[0250] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), reducing the size of the transmitted data. Emotion data is also compressed.

[0251] Step 5:

[0252] The device sends the compressed voice data and emotion data to a server via the Internet, using protocols such as HTTP to transfer the data.

[0253] Step 6:

[0254] The server receives the compressed voice data and emotion data sent from the terminal, and prepares the received data for decoding into an appropriate format.

[0255] Step 7:

[0256] The server decompresses the compressed audio data and restores it to its original digital form. Similarly, the emotion data is also decompressed and restored to its original form.

[0257] Step 8:

[0258] The server analyzes the audio data using analytical algorithms (e.g., machine learning models or speech recognition algorithms) to identify specific sounds (e.g., car horns, dog barks, doorbells), while also analyzing the emotional data to assess the user's current emotional state.

[0259] Step 9:

[0260] Based on the identified sounds, the server generates corresponding visual information (e.g., text messages or icons) and vibration patterns, adjusting the information to take into account the user's emotional state. For example, if the user is tense, it generates an intuitive and simple notification, while if they are relaxed, it provides detailed information.

[0261] Step 10:

[0262] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[0263] Step 11:

[0264] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[0265] Step 12:

[0266] The device displays the received visual information (e.g., a text message saying "I can hear a car horn" and a car icon) on the screen. The displayed information is adjusted according to the user's emotions.

[0267] Step 13:

[0268] The device vibrates according to the received vibration pattern, which is also adjusted according to the user's emotions.

[0269] This allows users to visually check their surroundings in real time and receive notifications tailored to their emotional state. For example, if a user is feeling nervous, the system will provide a concise and intuitive notification, allowing them to grasp the situation more quickly.

[0270] Example 2

[0271] 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."

[0272] Many modern users need to quickly and accurately grasp surrounding sounds and situations, while also seeking optimal notifications tailored to their emotional state. However, conventional systems provide only a uniform notification method without considering the user's emotional state, limiting the user experience. This invention aims to convert surrounding sounds into visual information and vibration patterns in real time, and further optimize notifications tailored to the user's emotional state.

[0273] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a recording means for collecting ambient sounds; a conversion means for converting the recorded audio data into a digital signal; a compression means for compressing the converted audio data; a transmission means for transmitting the compressed audio data to the server; an identification means in the server for analyzing the audio data and identifying specific sounds; a collection means for collecting the user's facial expressions and tone of voice using a camera and audio analysis function and generating emotional data; an emotional data transmission means for transmitting the collected emotional data to the server; an evaluation means for evaluating the user's emotional state using the emotional data; a generation means for generating visual information and a vibration pattern based on the identified sound and the evaluated emotional state; a return means for transmitting the generated visual information and vibration pattern to the terminal; and a display means for displaying the returned visual information and vibrating according to the vibration pattern. This enables optimal notification according to the user's emotional state.

[0274] A "recording means" is a device for collecting ambient sounds.

[0275] A "conversion means" is a device that converts recorded audio data into a digital signal.

[0276] The "compression means" is a device that compresses the converted audio data.

[0277] The "transmitting means" is a device that transmits compressed audio data to the server.

[0278] The "identification means" is a device that analyzes audio data in the server and identifies specific sounds.

[0279] The "collection means" is a device that uses a camera and voice analysis function to collect the user's facial expressions and tone of voice and generate emotion data.

[0280] The "emotion data transmission means" is a device that transmits collected emotion data to a server.

[0281] An "evaluation means" is a device that uses emotional data to evaluate the emotional state of a user.

[0282] A "generating means" is a device that generates visual information and vibration patterns based on the identified sounds and assessed emotional state.

[0283] The "returning means" is a device that transmits the generated visual information and vibration pattern to the terminal.

[0284] The "display means" is a device that displays the returned visual information and vibrates according to a vibration pattern.

[0285] A "voice analysis algorithm" is a computational method for analyzing voice data.

[0286] An "emotion recognition API" is an application programming interface for assessing a user's emotional state.

[0287] "Visual information" refers to information such as images and text that notifies the user.

[0288] A "vibration pattern" is a type of vibration that occurs in response to a particular sound or the user's emotional state.

[0289] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system are described below.

[0290] In this system, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, and a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0291] In this system, the hardware used includes a built-in microphone and camera, and the software used includes voice analysis software (e.g., Google Speech-to-Text API) and facial expression recognition algorithms (e.g., Microsoft Azure's Face API). The collected voice data is converted into a digital signal by the device and then compressed using a compression algorithm (e.g., MP3, FLAC). The compressed data is then sent to a server over the Internet.

[0292] The server receives the voice data sent from the device, decompresses it, and then analyzes it using a voice analysis algorithm (e.g., Google Speech-to-Text API). This analysis identifies specific sounds (e.g., car horns, dog barks, doorbells, etc.). The device also analyzes the user's facial expressions and tone of voice in real time using its camera and voice analysis functions, and sends the data to the server. The server then uses emotion recognition APIs (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state.

[0293] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, the server will provide more intuitive and concise notification information, while if the user is relaxed, the server will adjust the notification to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[0294] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user of the occurrence of a specific sound. The notification content and vibration intensity are also adjusted according to the user's emotional state.

[0295] Specifically, imagine a user standing on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the results to the server. The server recognizes the user's state of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[0296] In this way, the present invention is a system that provides a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0297] Prompt Sentence Examples

[0298] Please explain the process: "The device collects audio and sends it to the server. The server then analyzes the audio and generates an appropriate notification, optimizing the content of the notification according to the user's emotional state."

[0299] The above is the "Mode for Carrying Out the Invention."

[0300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0301] Step 1: The user launches the application.

[0302] The user launches a dedicated application on their device. This application includes functionality to collect ambient sounds and recognize the user's emotions. When the application is launched, the device's microphone and camera are enabled and ready to collect data. The input is the user's action, and the output is the state that the app starts running on the device.

[0303] Step 2: The device collects surrounding sounds and the user's facial expressions.

[0304] The device uses a built-in microphone to continuously collect ambient sounds, while simultaneously using a camera and audio analysis capabilities to collect the user's facial expressions and tone of voice. The input is ambient sounds and the user's facial expressions, and the output is raw audio and facial expression data, which is used for further analysis.

[0305] Step 3: The device converts the voice data into a digital signal.

[0306] The device uses a conversion method to convert the collected voice data into a digital signal. The hardware used includes a built-in microphone and software that contains a voice analysis algorithm. The input is raw voice data and the output is a digital voice signal. This converted data is then used for data compression.

[0307] Step 4: The device compresses the audio data and sends it to the server.

[0308] The terminal compresses the digitally converted audio data using a compression algorithm (e.g., MP3, FLAC). The compressed audio data is then sent to a server via the Internet. The input is a digital audio signal, and the output is compressed audio data. This data is delivered to the server using a transmission means.

[0309] Step 5: The server decompresses and analyzes the audio data.

[0310] The server decompresses the received compressed audio data. The decompressed audio data is analyzed using a speech analysis algorithm (e.g., Google Speech-to-Text API) to identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). The input is the compressed audio data, and the output is the identified specific sound information.

[0311] Step 6: The device analyzes the user's facial expressions and tone of voice in real time.

[0312] The device uses a camera and voice analysis function to analyze the user's facial expressions and tone of voice in real time. The analysis results are generated as emotional data, which is then sent to the server. The input is the user's facial expressions and tone of voice, and the output is emotional data.

[0313] Step 7: The server evaluates the emotion data.

[0314] The server uses an emotion recognition API (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state based on the emotion data sent from the device. The input is emotion data, and the output is evaluation information about the user's emotional state.

[0315] Step 8: The server generates an optimized response.

[0316] The server generates optimal visual information and vibration patterns based on the identified sounds and the evaluated emotional state. For example, it generates a brief notification if the user is tense, and a detailed notification if the user is relaxed. The input is specific sound information and the evaluation information of the emotional state, and the output is visual information and vibration patterns.

[0317] Step 9: The server sends the generated information to the terminal.

[0318] The server sends the generated visual information and vibration patterns to the device using an appropriate protocol (e.g., HTTP, HTTPS). The input is the visual information and vibration patterns, and the output is the data sent to the device.

[0319] Step 10: The device displays the information and notifies the user by vibrating.

[0320] The device displays the received visual information on the screen and notifies the user by vibration using a vibration motor. The input is the visual information and vibration pattern received from the server, and the output is the screen display and vibration. The notification content and vibration intensity are adjusted according to the user's emotional state.

[0321] The above is the specific processing flow of the program of this system and the operation at each step.

[0322] (Application example 2)

[0323] 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."

[0324] While conventional security systems have the ability to collect ambient sounds and identify specific sounds, they are unable to optimize their responses by taking into account the user's emotional state. As a result, they are unable to provide appropriate notifications even when the user is feeling tense or scared, which can result in the ineffectiveness of security alerts. Furthermore, inappropriate notification methods can cause excessive stress to users and, conversely, increase their anxiety.

[0325] The specific processing by the specific 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 an identification means for analyzing audio data and identifying a specific sound, an emotion recognition means for recognizing the user's emotional state, and a response optimization means for optimizing visual information and vibration patterns based on the identified sound and the recognized emotional state of the user. This makes it possible to generate and provide appropriate visual and vibration notifications according to the user's emotional state.

[0326] "Ambient sounds" refers to all sounds occurring in the user's environment.

[0327] "Recording means" refers to a device or facility for collecting and recording ambient sounds.

[0328] "Conversion means" refers to a device or function for converting recorded audio data into a digital signal.

[0329] "Compression means" refers to a device or function for compressing the converted audio data.

[0330] "Transmission means" refers to a device or function for transmitting compressed audio data to a server.

[0331] "Identification means" refers to a device or function that analyzes audio data on the server and identifies a particular sound.

[0332] "Generating means" refers to a device or function for generating visual information and vibration patterns based on the identified sounds.

[0333] "Emotion recognition means" refers to a device or function for recognizing the emotional state of a user.

[0334] "Response Optimizer" refers to a device or function for optimizing visual information and vibration patterns based on the user's emotional state.

[0335] "Returning means" refers to a device or function for transmitting the generated and optimized visual information and vibration patterns to the terminal.

[0336] "Display means" refers to a device or function for displaying returned visual information and vibrating according to a vibration pattern.

[0337] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[0338] In this system, the user first uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0339] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0340] The server receives the audio data sent from the device, and after receiving it, it is decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., breaking glass, alarms, loud voices, etc.).

[0341] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to the server. The server evaluates the user's emotional state based on this emotion recognition information. The emotion recognition library used here is "EmotionRecognizer," and the voice analysis library is "AudioAnalyzer."

[0342] Based on this, the server takes into account the identified sound and the user's emotional state and generates corresponding visual information and vibration patterns. For example, if the user is surprised, it will provide more intuitive and simple notification information. On the other hand, if the user is relaxed, it will adjust to provide more detailed information. This generated visual information and vibration pattern is sent to the device.

[0343] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[0344] As a concrete example, imagine a user is alone at home and hears the sound of breaking window glass. The device's microphone detects this sound and sends the data to the server. The server analyzes the audio and identifies it as the sound of breaking glass. At the same time, the device captures the user's face with its camera and performs facial analysis to identify a surprised expression. The server uses this information to instantly generate a simple warning message and a strong vibration pattern. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly and strongly to notify the user.

[0345] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0346] Example prompt sentence:

[0347] When certain environmental sounds (breaking glass, alarms, loud voices) are detected, create an application that provides real-time visual notification of the sound and the user's emotional state (anxiety, fear). If the user is experiencing fear, provide a simple and intuitive warning message and a strong vibration pattern.

[0348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0349] Step 1:

[0350] The device collects ambient sounds using a microphone. The input is ambient sounds, and the output is voice data. This voice data is converted into a digital signal and compressed using a compression algorithm. The specific operation here is the process of continuously collecting voice data and converting it into a digital format.

[0351] Step 2:

[0352] The terminal transmits the compressed voice data to the server. The input is the compressed voice data, and the output is the data transmitted to the server. The specific operation is a process of transmitting the compressed voice data via the Internet.

[0353] Step 3:

[0354] The server decompresses the received audio data and analyzes it using an audio analysis algorithm. The input is compressed audio data, and the output is identification data of specific sounds. The specific operations are to decompress the audio data and identify specific sounds using the audio analysis algorithm.

[0355] Step 4:

[0356] The device captures the user's facial expressions with a camera and recognizes the user's emotional state using an emotion recognition algorithm. The input is a video of the user's facial expression, and the output is the user's emotional data. The specific operation is to capture facial expressions in real time and analyze the emotions using an emotion recognition algorithm.

[0357] Step 5:

[0358] The terminal transmits the recognized emotion data to the server. The input is emotion data, and the output is the emotion data transmitted to the server. The specific operation is a process of transmitting emotion data via the Internet.

[0359] Step 6:

[0360] The server analyzes the identified sound data and emotional information and generates optimal visual information and vibration patterns. The input is the sound identification data and emotional data, and the output is the optimized visual information and vibration patterns. The specific operation is to generate visual information and vibration patterns using a generative AI model based on the sound identification results and emotional state.

[0361] Step 7:

[0362] The server transmits the generated visual information and vibration patterns to the terminal. The input is the optimized visual information and vibration patterns, and the output is the data transmitted to the terminal. The specific operation is a process of transmitting the generated information via the Internet.

[0363] Step 8:

[0364] The device displays the received visual information on the screen and vibrates according to the vibration pattern. The input is the visual information and vibration pattern received from the server, and the output is a notification to the user. The specific operation is to display the visual information on the screen and vibrate according to the pattern using the built-in vibration function.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] [Second embodiment]

[0369] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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. 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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."

[0381] The present invention provides a system for visually converting ambient sounds in real time and notifying the user of the converted sounds. Specific embodiments of the system will be described below.

[0382] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[0383] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0384] The server receives the audio data sent from the device. After receiving it, the audio data is decompressed and analyzed using a data analysis algorithm. During this analysis, the server identifies specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.).

[0385] The server generates corresponding visual information and vibration patterns based on the identified sounds. The visual information may include a text message or icon corresponding to the particular sound. For example, if a car horn sound is identified, the server generates the text "Car horn sound heard" and a car icon.

[0386] The server then transmits the generated visual information and vibration pattern to the device, which then displays the received visual information on its screen to inform the user of the surrounding situation.The device also vibrates according to the vibration pattern to notify the user of the occurrence of a specific sound.

[0387] As a concrete example, suppose a user is standing on a train platform. At this time, a device that collects surrounding sounds detects an announcement of a train's arrival. The audio data is sent to a server, where it is analyzed, generating a text message saying "A train is arriving" and a train icon. These are then sent to the device, which then displays them on the device's screen, and the device vibrates briefly to notify the user.

[0388] In this way, the present invention is a system that allows users to visually recognize surrounding sounds and provides necessary information in real time, thereby providing a more comprehensive and intuitive user experience for people with hearing impairments and users who want to visually recognize environmental sounds.

[0389] The processing flow will be explained below.

[0390] Step 1:

[0391] The user launches an app on the device, which then activates the device's microphone and prepares to collect ambient sounds.

[0392] Step 2:

[0393] The device uses a microphone to collect ambient sounds in real time, and audio data is captured continuously.

[0394] Step 3:

[0395] The analog voice data collected by the terminal is converted into a digital signal using an AD converter.

[0396] Step 4:

[0397] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), thereby reducing the size of the transmitted data.

[0398] Step 5:

[0399] The device sends the compressed audio data to the server via the Internet, using protocols such as HTTP to transfer the data.

[0400] Step 6:

[0401] The server receives the compressed audio data sent from the terminal and prepares it to decode the received data into an appropriate format.

[0402] Step 7:

[0403] The server decompresses the compressed audio data and restores it to the original digital audio data.

[0404] Step 8:

[0405] The server analyzes the audio data using an analysis algorithm (e.g., a machine learning model or a voice recognition algorithm) to identify specific sounds (e.g., a car horn, a dog barking, a doorbell).

[0406] Step 9:

[0407] The server generates corresponding visual information (e.g., a text message or icon) and vibration patterns based on the identified sound.

[0408] Step 10:

[0409] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[0410] Step 11:

[0411] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[0412] Step 12:

[0413] The device displays the visual information it receives (e.g., a text message saying "I hear a car horn" and a car icon) on the screen.

[0414] Step 13:

[0415] The device vibrates according to the vibration pattern received by the terminal, and provides the user with a notification for a specific sound based on the vibration pattern.

[0416] This series of processes allows users to visually check surrounding sounds in real time and receive vibration notifications. For example, if a user is on a train platform, the device detects the train arrival announcement, analyzes it on the server, and generates appropriate visual information and vibration notifications to notify the user, allowing them to understand their surroundings.

[0417] Example 1

[0418] 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."

[0419] Conventional voice notification systems lack the ability to accurately identify sound types and notify users as visual information or vibration patterns, which makes it difficult for hearing-impaired users and users in noisy environments to effectively recognize surrounding sound information in real time.

[0420] 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.

[0421] In this invention, the server includes a decompression means for decompressing audio data, an identification means for analyzing the decompressed data and identifying specific sounds, and a generation means for generating visual information and vibration patterns based on the identified sounds, thereby enabling the server to accurately identify surrounding sounds and notify the user in real time as visual information and vibration patterns.

[0422] A "collection means" is a function or device for collecting ambient sounds.

[0423] "Conversion means" refers to a function or device that converts collected audio data into a digital signal.

[0424] "Compression means" refers to a function or device that compresses the converted audio data.

[0425] The "transmission means" is a function or device that transmits compressed audio data to a server via a data communication network.

[0426] The "decompression means" is a function or device that decompresses audio data received by the server.

[0427] The "identification means" is a function or device that analyzes the decompressed audio data and identifies a particular sound.

[0428] "Generating means" refers to a function or device that generates visual information and vibration patterns based on the identified sounds.

[0429] The "returning means" is a function or device that transmits the generated visual information and vibration pattern to the terminal.

[0430] "Display means" is a function or device that displays the returned visual information and vibrates according to a vibration pattern.

[0431] The present invention provides a system that visually converts ambient sounds in real time and notifies the user of the results. This system is realized by taking the following specific steps.

[0432] First, the user starts a device with a dedicated application installed. This device is equipped with a microphone for collecting ambient sounds. When the user starts the application, the device's microphone is enabled and begins to continuously collect ambient sounds.

[0433] The device then converts the collected audio into a digital signal. This digital signal conversion is the process of converting analog audio data into a digital format, and involves sampling and quantization. The digital audio data is then compressed using a compression algorithm. Examples of compression algorithms used include MP3 and AAC. This compressed data is then sent over the internet to a server.

[0434] The server receives the voice data sent from the device. After receiving it, the voice data is decompressed and restored to its original digital signal. The server then uses a data analysis algorithm to analyze the voice data and identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). This analysis is performed using a machine learning algorithm.

[0435] After the server identifies a specific sound, it generates corresponding visual information and vibration patterns based on the identified sound. For example, if a car horn sound is identified, the text "I hear a car horn" and a car icon are generated. If a dog bark is identified, the text "Dog barking" and a dog icon are generated. The generated visual information and vibration patterns are then sent back to the device via the Internet.

[0436] The device displays the received visual information on the screen to notify the user of the surrounding situation, and at the same time, the device vibrates according to a specified vibration pattern and notifies the user that a specific sound has occurred.

[0437] As a concrete example, consider a case where a user is on a train platform. At this time, the user's device collects train arrival announcements and sends the audio data to a server. When the server analyzes the audio data and identifies the train arrival announcement, it generates a text message saying "A train is arriving" and a train icon. This information is sent to the device and displayed on the device's screen, and at the same time, the device vibrates briefly to notify the user.

[0438] Specific examples of input prompts to generative AI models include the following:

[0439] Prompt: Describe a system that visually translates ambient sound into real-time notifications. Include specific hardware and software processes, and provide examples.

[0440] This system can provide real-time information to hearing-impaired users and those who want to visually recognize surrounding sounds in noisy environments, offering a more comprehensive and intuitive user experience.

[0441] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] Your device activates the microphone to collect ambient sounds.

[0444] Input: The user launches the dedicated application.

[0445] Data processing: Acquire analog audio signals

[0446] Output: Acquired raw analog audio signal

[0447] Specific operation: The device launches the application in response to user operation and enables the built-in microphone, which then continuously collects ambient sounds.

[0448] Step 2:

[0449] The device converts the collected audio into a digital signal.

[0450] Input: Raw analog audio signal

[0451] Data processing: Analog to digital conversion through sampling and quantization

[0452] Output: Digital audio data

[0453] How it works: The analog-to-digital converter (ADC) circuit in the device converts the analog voice signal into digital data, which represents the voice as a bit string of 0s and 1s.

[0454] Step 3:

[0455] The terminal compresses the audio data.

[0456] Input: Digital audio data

[0457] Data processing: Compression using a compression algorithm (e.g. MP3, AAC, etc.)

[0458] Output: Compressed audio data

[0459] How it works: The device passes digital audio data to a compression algorithm, which compresses it to reduce its size. This compressed data is then sent to the server.

[0460] Step 4:

[0461] The terminal transmits the compressed data to the server.

[0462] Input: Compressed audio data

[0463] Data calculation: transmission over a data communication network

[0464] Output: Compressed data sent to the server

[0465] What it does: Your device uses your internet connection (Wi-Fi or mobile data) to send compressed audio data to the server. The data is split into packets and sent.

[0466] Step 5:

[0467] The server decompresses the received audio data.

[0468] Input: Compressed audio data

[0469] Data operation: Decompression by decompression algorithm

[0470] Output: Decompressed digital audio data

[0471] What it does: The server passes the compressed audio data it receives through a decompression algorithm, restoring it to the original digital audio data, which can then be analyzed.

[0472] Step 6:

[0473] The server analyzes the audio data and identifies specific sounds.

[0474] Input: Decompressed digital audio data

[0475] Data Computing: Machine Learning Algorithms for Speech Analysis and Sound Identification

[0476] Output: Identified specific sound information

[0477] What it does: The server runs a speech analysis algorithm to identify specific sounds (e.g., car horn, dog barking, doorbell, etc.) from the decompressed digital audio data. It then uses a machine learning model to extract sound features and match them with pre-trained sound patterns.

[0478] Step 7:

[0479] The server generates corresponding visual information and vibration patterns based on the identified sounds.

[0480] Input: Identified specific sound information

[0481] Data Computing: Generation of visual information (text and icons) and vibration patterns

[0482] Output: Visual information and vibration patterns

[0483] Specific behavior: The server generates visual information (e.g., a text message saying "I hear a dog barking" and a dog icon) and vibration patterns corresponding to the identified sound. Corresponding data is generated.

[0484] Step 8:

[0485] The server generates visual information and transmits vibration patterns to the terminal.

[0486] Input: Generated visual information and vibration patterns

[0487] Data calculation: Sending data to the terminal

[0488] Output: Sending visual information and vibration patterns to the device

[0489] How it works: The server uses an internet connection to send the generated visual information and vibration patterns to the device. The data is divided into packets and sent to the device.

[0490] Step 9:

[0491] The device displays the received visual information on the screen and notifies the user by vibrating.

[0492] Input: Received visual information and vibration patterns

[0493] Data calculation: Screen display and vibration pattern execution

[0494] Output: User notification

[0495] Specific operation: The device displays the received visual information on the screen and vibrates according to the specified vibration pattern, thereby notifying the user visually and tactilely that a specific sound has occurred. For example, a text message saying "Train is arriving" is displayed on the screen, a train icon is displayed, and the device vibrates briefly.

[0496] (Application example 1)

[0497] 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."

[0498] Conventional systems often make it difficult to visually recognize surrounding sounds. In particular, in certain situations in physical stores (e.g., sales start dates or in-store announcements), appropriate information provision was lacking for hearing-impaired users and users who prefer to visually understand surrounding sounds. Furthermore, there were limited means of providing context-sensitive information in real time.

[0499] 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.

[0500] In this invention, the server includes a recording means for collecting ambient sounds, a conversion means for converting the recorded audio data into a digital signal, a compression means for compressing the converted audio data, a transmission means for transmitting the compressed audio data to the server, an identification means in the server for analyzing the audio data and identifying a specific sound, a generation means for generating visual information and a vibration pattern based on the identified sound, a return means for transmitting the generated visual information and vibration pattern to the terminal, a display means for displaying the returned visual information and vibrating according to the vibration pattern, a means for generating the visual information and vibration pattern when the specific sound is related to a specific situation in the physical store (e.g., the start of a sale or an in-store announcement), and a means for providing the visual information and vibration pattern via a smartphone, a head-mounted display, or the like. This enables users to be notified in real time by appropriate visual information and vibration patterns in specific situations in the physical store.

[0501] "Ambient sounds" refers to any sounds that come from the surrounding environment and can be detected through the ear.

[0502] "Recording means" refers to a device or function that continuously collects ambient sounds and stores them as digital data.

[0503] "Conversion means" refers to a device or function that converts collected audio data into a digital signal.

[0504] The "compression means" refers to a device or function for efficiently compressing the converted audio data to reduce the amount of data.

[0505] "Transmission means" refers to a device or function that transmits compressed audio data to a server via the Internet.

[0506] The "identification means" refers to a device or function that analyzes audio data in the server and identifies a specific sound.

[0507] "Generating means" refers to a device or function that generates visual information and vibration patterns based on the identified sounds.

[0508] The "returning means" refers to a device or function that transmits the generated visual information and vibration pattern to the terminal.

[0509] "Display means" refers to a device or function that displays the returned visual information and vibrates according to a vibration pattern.

[0510] A "specific situation" refers to a specific event or state that occurs within a physical store (e.g., the start of a sale or an in-store announcement).

[0511] A "smartphone" is a type of mobile phone that has multiple functions such as voice calls, Internet access, and application execution.

[0512] A "head-mounted display" is a display device that can be worn on the user's head and has the function of presenting visual information.

[0513] A specific embodiment of the present invention will be described below: This system analyzes surrounding sounds in real time and provides visual information and vibration notifications to the user based on the results.

[0514] First, a user uses a device with a dedicated application. The device is equipped with a microphone for collecting ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[0515] The collected voice data is converted into a digital signal by a conversion means in the terminal, and then the digital signal is compressed by a compression means, and the compressed voice data is transmitted to a server via the Internet through a transmission means.

[0516] The server decompresses the received audio data and analyzes it using a recognition means, which uses a machine learning algorithm to identify specific sounds (e.g., a sale announcement, a new product arrival announcement, etc.). Based on the identified sound, the server generates corresponding visual information (e.g., a text message or icon) and vibration patterns using a generation means.

[0517] The generated visual information and vibration pattern are transmitted to the terminal using the return means, and the terminal displays the returned information to the user through the display means and notifies the user that a specific sound has been generated by the vibration.

[0518] As a concrete example, suppose a user is shopping in a physical store. An application collects ambient sounds and sends them to a server. When an announcement of a sale is detected in the store, the server generates a text message saying "Sale has started" and a sale icon and sends it to the device. This notifies the user by displaying a visual message on the device screen and a short vibration.

[0519] The system includes the following hardware and software:

[0520] Hardware: Your smartphone's microphone, speaker, display, and vibrator.

[0521] software:

[0522] Pyaudio: Audio recording.

[0523] Requests: Server communication.

[0524] haptics(tentative module): vibration notifications.

[0525] Python: A programming language.

[0526] Examples of specific generated AI prompt sentences are as follows:

[0527] "You are a user of an application that visually translates surrounding sounds in real time. Right now, while you are shopping in a supermarket, you hear an announcement that a sale has started. How would the application respond?"

[0528] As a result of the above aspects, the present invention can provide users with visual and vibrational notifications in specific sound environments within physical stores, thereby improving convenience and safety.

[0529] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0530] Step 1:

[0531] When the user launches the application, the device's microphone is enabled.

[0532] Input: User action (application launch).

[0533] Output: The microphone is enabled and begins collecting ambient sounds.

[0534] Specific behavior: The application starts and the microphone device transitions to the active state.

[0535] Step 2:

[0536] The device continuously collects ambient sounds through a microphone.

[0537] Input: Ambient sounds.

[0538] Output: Recorded audio data.

[0539] What it does: The microphone captures ambient sounds and records them as audio signals.

[0540] Step 3:

[0541] The terminal converts the collected voice data into a digital signal.

[0542] Input: Recorded audio data (analog signal).

[0543] Output: Audio data converted into a digital signal.

[0544] What it does: Converts analog audio signals into digital data using the Pyaudio library.

[0545] Step 4:

[0546] The converted audio data is compressed using a compression algorithm.

[0547] Input: Audio data converted into a digital signal.

[0548] Output: Compressed audio data.

[0549] Specific operation: A codec is used to compress the audio data, reducing the amount of data.

[0550] Step 5:

[0551] The terminal transmits the compressed audio data to a server via the Internet.

[0552] Input: Compressed audio data.

[0553] Output: The data sent to the server.

[0554] What it does: Uses the Requests library to send compressed audio data to the server via an HTTP POST request.

[0555] Step 6:

[0556] The server decompresses the received audio data and analyzes it using the identification means.

[0557] Input: Compressed audio data sent to the server.

[0558] Output: The analyzed audio data.

[0559] What it does: It unpacks the compressed data on the server side and uses machine learning algorithms to identify specific sounds.

[0560] Step 7:

[0561] The server generates visual information and vibration patterns based on the identified sounds.

[0562] Input: Parsed audio data.

[0563] Output: Visual information and vibration patterns.

[0564] What it does: It runs an algorithm that generates text messages, icons, and vibration patterns.

[0565] Step 8:

[0566] The generated visual information and vibration pattern are transmitted to the terminal using a return means.

[0567] Input: Visual information and vibration patterns.

[0568] Output: Information sent back to the device.

[0569] Specific operation: Visual information and vibration patterns are sent from the server to the device using an HTTP response.

[0570] Step 9:

[0571] The terminal displays the returned visual information and vibrates according to the vibration pattern.

[0572] Input: Visual information and vibration patterns from the server.

[0573] Output: Visual and vibration notification to the user.

[0574] What it does: Shows text messages and icons on the display and vibrates for notifications.

[0575] 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.

[0576] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[0577] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the app, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0578] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0579] The server receives the audio data sent from the device, which is then decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., car horns, dogs barking, doorbells, etc.).

[0580] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to a server, which then evaluates the user's emotional state based on this emotion recognition information.

[0581] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, it will provide more intuitive and concise notification information, while if the user is relaxed, it will adjust to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[0582] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[0583] For example, imagine a user is on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the data to the server. The server recognizes the user's level of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[0584] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0585] The processing flow will be explained below.

[0586] Step 1:

[0587] The user launches the app on their device. The app activates the device's microphone and camera and prepares to collect ambient sounds and the user's emotions.

[0588] Step 2:

[0589] The device uses a microphone to collect ambient sounds in real time, while simultaneously using a camera and voice analysis functions to capture the user's facial expressions and voice tone, using facial recognition algorithms and voice emotion analysis algorithms.

[0590] Step 3:

[0591] The analog voice data collected by the device is converted into a digital signal using an AD converter. At the same time, the user's emotional data (facial expressions and voice features) is also digitized.

[0592] Step 4:

[0593] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), reducing the size of the transmitted data. Emotion data is also compressed.

[0594] Step 5:

[0595] The device sends the compressed voice data and emotion data to a server via the Internet, using protocols such as HTTP to transfer the data.

[0596] Step 6:

[0597] The server receives the compressed voice data and emotion data sent from the terminal, and prepares the received data for decoding into an appropriate format.

[0598] Step 7:

[0599] The server decompresses the compressed audio data and restores it to its original digital form. Similarly, the emotion data is also decompressed and restored to its original form.

[0600] Step 8:

[0601] The server analyzes the audio data using analytical algorithms (e.g., machine learning models or speech recognition algorithms) to identify specific sounds (e.g., car horns, dog barks, doorbells), while also analyzing the emotional data to assess the user's current emotional state.

[0602] Step 9:

[0603] Based on the identified sounds, the server generates corresponding visual information (e.g., text messages or icons) and vibration patterns, adjusting the information to take into account the user's emotional state. For example, if the user is tense, it generates an intuitive and simple notification, while if they are relaxed, it provides detailed information.

[0604] Step 10:

[0605] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[0606] Step 11:

[0607] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[0608] Step 12:

[0609] The device displays the received visual information (e.g., a text message saying "I can hear a car horn" and a car icon) on the screen. The displayed information is adjusted according to the user's emotions.

[0610] Step 13:

[0611] The device vibrates according to the received vibration pattern, which is also adjusted according to the user's emotions.

[0612] This allows users to visually check their surroundings in real time and receive notifications tailored to their emotional state. For example, if a user is feeling nervous, the system will provide a concise and intuitive notification, allowing them to grasp the situation more quickly.

[0613] Example 2

[0614] 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."

[0615] Many modern users need to quickly and accurately grasp surrounding sounds and situations, while also seeking optimal notifications tailored to their emotional state. However, conventional systems provide only a uniform notification method without considering the user's emotional state, limiting the user experience. This invention aims to convert surrounding sounds into visual information and vibration patterns in real time, and further optimize notifications tailored to the user's emotional state.

[0616] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a recording means for collecting ambient sounds; a conversion means for converting the recorded audio data into a digital signal; a compression means for compressing the converted audio data; a transmission means for transmitting the compressed audio data to the server; an identification means in the server for analyzing the audio data and identifying specific sounds; a collection means for collecting the user's facial expressions and tone of voice using a camera and audio analysis function and generating emotional data; an emotional data transmission means for transmitting the collected emotional data to the server; an evaluation means for evaluating the user's emotional state using the emotional data; a generation means for generating visual information and a vibration pattern based on the identified sound and the evaluated emotional state; a return means for transmitting the generated visual information and vibration pattern to the terminal; and a display means for displaying the returned visual information and vibrating according to the vibration pattern. This enables optimal notification according to the user's emotional state.

[0617] A "recording means" is a device for collecting ambient sounds.

[0618] A "conversion means" is a device that converts recorded audio data into a digital signal.

[0619] The "compression means" is a device that compresses the converted audio data.

[0620] The "transmitting means" is a device that transmits compressed audio data to the server.

[0621] The "identification means" is a device that analyzes audio data in the server and identifies specific sounds.

[0622] The "collection means" is a device that uses a camera and voice analysis function to collect the user's facial expressions and tone of voice and generate emotion data.

[0623] The "emotion data transmission means" is a device that transmits collected emotion data to a server.

[0624] An "evaluation means" is a device that uses emotional data to evaluate the emotional state of a user.

[0625] A "generating means" is a device that generates visual information and vibration patterns based on the identified sounds and assessed emotional state.

[0626] The "returning means" is a device that transmits the generated visual information and vibration pattern to the terminal.

[0627] The "display means" is a device that displays the returned visual information and vibrates according to a vibration pattern.

[0628] A "voice analysis algorithm" is a computational method for analyzing voice data.

[0629] An "emotion recognition API" is an application programming interface for assessing a user's emotional state.

[0630] "Visual information" refers to information such as images and text that notifies the user.

[0631] A "vibration pattern" is a type of vibration that occurs in response to a particular sound or the user's emotional state.

[0632] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system are described below.

[0633] In this system, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, and a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0634] In this system, the hardware used includes a built-in microphone and camera, and the software used includes voice analysis software (e.g., Google Speech-to-Text API) and facial expression recognition algorithms (e.g., Microsoft Azure's Face API). The collected voice data is converted into a digital signal by the device and then compressed using a compression algorithm (e.g., MP3, FLAC). The compressed data is then sent to a server over the Internet.

[0635] The server receives the voice data sent from the device, decompresses it, and then analyzes it using a voice analysis algorithm (e.g., Google Speech-to-Text API). This analysis identifies specific sounds (e.g., car horns, dog barks, doorbells, etc.). The device also analyzes the user's facial expressions and tone of voice in real time using its camera and voice analysis functions, and sends the data to the server. The server then uses emotion recognition APIs (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state.

[0636] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, the server will provide more intuitive and concise notification information, while if the user is relaxed, the server will adjust the notification to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[0637] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user of the occurrence of a specific sound. The notification content and vibration intensity are also adjusted according to the user's emotional state.

[0638] Specifically, imagine a user standing on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the results to the server. The server recognizes the user's state of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[0639] In this way, the present invention is a system that provides a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0640] Prompt Sentence Examples

[0641] Please explain the process: "The device collects audio and sends it to the server. The server then analyzes the audio and generates an appropriate notification, optimizing the content of the notification according to the user's emotional state."

[0642] The above is the "Mode for Carrying Out the Invention."

[0643] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0644] Step 1: The user launches the application.

[0645] The user launches a dedicated application on their device. This application includes functionality to collect ambient sounds and recognize the user's emotions. When the application is launched, the device's microphone and camera are enabled and ready to collect data. The input is the user's action, and the output is the state that the app starts running on the device.

[0646] Step 2: The device collects surrounding sounds and the user's facial expressions.

[0647] The device uses a built-in microphone to continuously collect ambient sounds, while simultaneously using a camera and audio analysis capabilities to collect the user's facial expressions and tone of voice. The input is ambient sounds and the user's facial expressions, and the output is raw audio and facial expression data, which is used for further analysis.

[0648] Step 3: The device converts the voice data into a digital signal.

[0649] The device uses a conversion method to convert the collected voice data into a digital signal. The hardware used includes a built-in microphone and software that contains a voice analysis algorithm. The input is raw voice data and the output is a digital voice signal. This converted data is then used for data compression.

[0650] Step 4: The device compresses the audio data and sends it to the server.

[0651] The terminal compresses the digitally converted audio data using a compression algorithm (e.g., MP3, FLAC). The compressed audio data is then sent to a server via the Internet. The input is a digital audio signal, and the output is compressed audio data. This data is delivered to the server using a transmission means.

[0652] Step 5: The server decompresses and analyzes the audio data.

[0653] The server decompresses the received compressed audio data. The decompressed audio data is analyzed using a speech analysis algorithm (e.g., Google Speech-to-Text API) to identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). The input is the compressed audio data, and the output is the identified specific sound information.

[0654] Step 6: The device analyzes the user's facial expressions and tone of voice in real time.

[0655] The device uses a camera and voice analysis function to analyze the user's facial expressions and tone of voice in real time. The analysis results are generated as emotional data, which is then sent to the server. The input is the user's facial expressions and tone of voice, and the output is emotional data.

[0656] Step 7: The server evaluates the emotion data.

[0657] The server uses an emotion recognition API (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state based on the emotion data sent from the device. The input is emotion data, and the output is evaluation information about the user's emotional state.

[0658] Step 8: The server generates an optimized response.

[0659] The server generates optimal visual information and vibration patterns based on the identified sounds and the evaluated emotional state. For example, it generates a brief notification if the user is tense, and a detailed notification if the user is relaxed. The input is specific sound information and the evaluation information of the emotional state, and the output is visual information and vibration patterns.

[0660] Step 9: The server sends the generated information to the terminal.

[0661] The server sends the generated visual information and vibration patterns to the device using an appropriate protocol (e.g., HTTP, HTTPS). The input is the visual information and vibration patterns, and the output is the data sent to the device.

[0662] Step 10: The device displays the information and notifies the user by vibrating.

[0663] The device displays the received visual information on the screen and notifies the user by vibration using a vibration motor. The input is the visual information and vibration pattern received from the server, and the output is the screen display and vibration. The notification content and vibration intensity are adjusted according to the user's emotional state.

[0664] The above is the specific processing flow of the program of this system and the operation at each step.

[0665] (Application example 2)

[0666] 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."

[0667] While conventional security systems have the ability to collect ambient sounds and identify specific sounds, they are unable to optimize their responses by taking into account the user's emotional state. As a result, they are unable to provide appropriate notifications even when the user is feeling tense or scared, which can result in the ineffectiveness of security alerts. Furthermore, inappropriate notification methods can cause excessive stress to users and, conversely, increase their anxiety.

[0668] The specific processing by the specific 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 an identification means for analyzing audio data and identifying a specific sound, an emotion recognition means for recognizing the user's emotional state, and a response optimization means for optimizing visual information and vibration patterns based on the identified sound and the recognized emotional state of the user. This makes it possible to generate and provide appropriate visual and vibration notifications according to the user's emotional state.

[0669] "Ambient sounds" refers to all sounds occurring in the user's environment.

[0670] "Recording means" refers to a device or facility for collecting and recording ambient sounds.

[0671] "Conversion means" refers to a device or function for converting recorded audio data into a digital signal.

[0672] "Compression means" refers to a device or function for compressing the converted audio data.

[0673] "Transmission means" refers to a device or function for transmitting compressed audio data to a server.

[0674] "Identification means" refers to a device or function that analyzes audio data on the server and identifies a particular sound.

[0675] "Generating means" refers to a device or function for generating visual information and vibration patterns based on the identified sounds.

[0676] "Emotion recognition means" refers to a device or function for recognizing the emotional state of a user.

[0677] "Response Optimizer" refers to a device or function for optimizing visual information and vibration patterns based on the user's emotional state.

[0678] "Returning means" refers to a device or function for transmitting the generated and optimized visual information and vibration patterns to the terminal.

[0679] "Display means" refers to a device or function for displaying returned visual information and vibrating according to a vibration pattern.

[0680] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[0681] In this system, the user first uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0682] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0683] The server receives the audio data sent from the device, and after receiving it, it is decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., breaking glass, alarms, loud voices, etc.).

[0684] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to the server. The server evaluates the user's emotional state based on this emotion recognition information. The emotion recognition library used here is "EmotionRecognizer," and the voice analysis library is "AudioAnalyzer."

[0685] Based on this, the server takes into account the identified sound and the user's emotional state and generates corresponding visual information and vibration patterns. For example, if the user is surprised, it will provide more intuitive and simple notification information. On the other hand, if the user is relaxed, it will adjust to provide more detailed information. This generated visual information and vibration pattern is sent to the device.

[0686] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[0687] As a concrete example, imagine a user is alone at home and hears the sound of breaking window glass. The device's microphone detects this sound and sends the data to the server. The server analyzes the audio and identifies it as the sound of breaking glass. At the same time, the device captures the user's face with its camera and performs facial analysis to identify a surprised expression. The server uses this information to instantly generate a simple warning message and a strong vibration pattern. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly and strongly to notify the user.

[0688] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0689] Example prompt sentence:

[0690] When certain environmental sounds (breaking glass, alarms, loud voices) are detected, create an application that provides real-time visual notification of the sound and the user's emotional state (anxiety, fear). If the user is experiencing fear, provide a simple and intuitive warning message and a strong vibration pattern.

[0691] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0692] Step 1:

[0693] The device collects ambient sounds using a microphone. The input is ambient sounds, and the output is voice data. This voice data is converted into a digital signal and compressed using a compression algorithm. The specific operation here is the process of continuously collecting voice data and converting it into a digital format.

[0694] Step 2:

[0695] The terminal transmits the compressed voice data to the server. The input is the compressed voice data, and the output is the data transmitted to the server. The specific operation is a process of transmitting the compressed voice data via the Internet.

[0696] Step 3:

[0697] The server decompresses the received audio data and analyzes it using an audio analysis algorithm. The input is compressed audio data, and the output is identification data of specific sounds. The specific operations are to decompress the audio data and identify specific sounds using the audio analysis algorithm.

[0698] Step 4:

[0699] The device captures the user's facial expressions with a camera and recognizes the user's emotional state using an emotion recognition algorithm. The input is a video of the user's facial expression, and the output is the user's emotional data. The specific operation is to capture facial expressions in real time and analyze the emotions using an emotion recognition algorithm.

[0700] Step 5:

[0701] The terminal transmits the recognized emotion data to the server. The input is emotion data, and the output is the emotion data transmitted to the server. The specific operation is a process of transmitting emotion data via the Internet.

[0702] Step 6:

[0703] The server analyzes the identified sound data and emotional information and generates optimal visual information and vibration patterns. The input is the sound identification data and emotional data, and the output is the optimized visual information and vibration patterns. The specific operation is to generate visual information and vibration patterns using a generative AI model based on the sound identification results and emotional state.

[0704] Step 7:

[0705] The server transmits the generated visual information and vibration patterns to the terminal. The input is the optimized visual information and vibration patterns, and the output is the data transmitted to the terminal. The specific operation is a process of transmitting the generated information via the Internet.

[0706] Step 8:

[0707] The device displays the received visual information on the screen and vibrates according to the vibration pattern. The input is the visual information and vibration pattern received from the server, and the output is a notification to the user. The specific operation is to display the visual information on the screen and vibrate according to the pattern using the built-in vibration function.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 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.

[0714] 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).

[0715] 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.

[0716] 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.

[0717] 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).

[0718] 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. 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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."

[0724] The present invention provides a system for visually converting ambient sounds in real time and notifying the user of the converted sounds. Specific embodiments of the system will be described below.

[0725] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[0726] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0727] The server receives the audio data sent from the device. After receiving it, the audio data is decompressed and analyzed using a data analysis algorithm. During this analysis, the server identifies specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.).

[0728] The server generates corresponding visual information and vibration patterns based on the identified sounds. The visual information may include a text message or icon corresponding to the particular sound. For example, if a car horn sound is identified, the server generates the text "Car horn sound heard" and a car icon.

[0729] The server then transmits the generated visual information and vibration pattern to the device, which then displays the received visual information on its screen to inform the user of the surrounding situation.The device also vibrates according to the vibration pattern to notify the user of the occurrence of a specific sound.

[0730] As a concrete example, suppose a user is standing on a train platform. At this time, a device that collects surrounding sounds detects an announcement of a train's arrival. The audio data is sent to a server, where it is analyzed, generating a text message saying "A train is arriving" and a train icon. These are then sent to the device, which then displays them on the device's screen, and the device vibrates briefly to notify the user.

[0731] In this way, the present invention is a system that allows users to visually recognize surrounding sounds and provides necessary information in real time, thereby providing a more comprehensive and intuitive user experience for people with hearing impairments and users who want to visually recognize environmental sounds.

[0732] The processing flow will be explained below.

[0733] Step 1:

[0734] The user launches an app on the device, which then activates the device's microphone and prepares to collect ambient sounds.

[0735] Step 2:

[0736] The device uses a microphone to collect ambient sounds in real time, and audio data is captured continuously.

[0737] Step 3:

[0738] The analog voice data collected by the terminal is converted into a digital signal using an AD converter.

[0739] Step 4:

[0740] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), thereby reducing the size of the transmitted data.

[0741] Step 5:

[0742] The device sends the compressed audio data to the server via the Internet, using protocols such as HTTP to transfer the data.

[0743] Step 6:

[0744] The server receives the compressed audio data sent from the terminal and prepares it to decode the received data into an appropriate format.

[0745] Step 7:

[0746] The server decompresses the compressed audio data and restores it to the original digital audio data.

[0747] Step 8:

[0748] The server analyzes the audio data using an analysis algorithm (e.g., a machine learning model or a voice recognition algorithm) to identify specific sounds (e.g., a car horn, a dog barking, a doorbell).

[0749] Step 9:

[0750] The server generates corresponding visual information (e.g., a text message or icon) and vibration patterns based on the identified sound.

[0751] Step 10:

[0752] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[0753] Step 11:

[0754] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[0755] Step 12:

[0756] The device displays the visual information it receives (e.g., a text message saying "I hear a car horn" and a car icon) on the screen.

[0757] Step 13:

[0758] The device vibrates according to the vibration pattern received by the terminal, and provides the user with a notification for a specific sound based on the vibration pattern.

[0759] This series of processes allows users to visually check surrounding sounds in real time and receive vibration notifications. For example, if a user is on a train platform, the device detects the train arrival announcement, analyzes it on the server, and generates appropriate visual information and vibration notifications to notify the user, allowing them to understand their surroundings.

[0760] Example 1

[0761] 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."

[0762] Conventional voice notification systems lack the ability to accurately identify sound types and notify users as visual information or vibration patterns, which makes it difficult for hearing-impaired users and users in noisy environments to effectively recognize surrounding sound information in real time.

[0763] 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.

[0764] In this invention, the server includes a decompression means for decompressing audio data, an identification means for analyzing the decompressed data and identifying specific sounds, and a generation means for generating visual information and vibration patterns based on the identified sounds, thereby enabling the server to accurately identify surrounding sounds and notify the user in real time as visual information and vibration patterns.

[0765] A "collection means" is a function or device for collecting ambient sounds.

[0766] "Conversion means" refers to a function or device that converts collected audio data into a digital signal.

[0767] "Compression means" refers to a function or device that compresses the converted audio data.

[0768] The "transmission means" is a function or device that transmits compressed audio data to a server via a data communication network.

[0769] The "decompression means" is a function or device that decompresses audio data received by the server.

[0770] The "identification means" is a function or device that analyzes the decompressed audio data and identifies a particular sound.

[0771] "Generating means" refers to a function or device that generates visual information and vibration patterns based on the identified sounds.

[0772] The "returning means" is a function or device that transmits the generated visual information and vibration pattern to the terminal.

[0773] "Display means" is a function or device that displays the returned visual information and vibrates according to a vibration pattern.

[0774] The present invention provides a system that visually converts ambient sounds in real time and notifies the user of the results. This system is realized by taking the following specific steps.

[0775] First, the user starts a device with a dedicated application installed. This device is equipped with a microphone for collecting ambient sounds. When the user starts the application, the device's microphone is enabled and begins to continuously collect ambient sounds.

[0776] The device then converts the collected audio into a digital signal. This digital signal conversion is the process of converting analog audio data into a digital format, and involves sampling and quantization. The digital audio data is then compressed using a compression algorithm. Examples of compression algorithms used include MP3 and AAC. This compressed data is then sent over the internet to a server.

[0777] The server receives the voice data sent from the device. After receiving it, the voice data is decompressed and restored to its original digital signal. The server then uses a data analysis algorithm to analyze the voice data and identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). This analysis is performed using a machine learning algorithm.

[0778] After the server identifies a specific sound, it generates corresponding visual information and vibration patterns based on the identified sound. For example, if a car horn sound is identified, the text "I hear a car horn" and a car icon are generated. If a dog bark is identified, the text "Dog barking" and a dog icon are generated. The generated visual information and vibration patterns are then sent back to the device via the Internet.

[0779] The device displays the received visual information on the screen to notify the user of the surrounding situation, and at the same time, the device vibrates according to a specified vibration pattern and notifies the user that a specific sound has occurred.

[0780] As a concrete example, consider a case where a user is on a train platform. At this time, the user's device collects train arrival announcements and sends the audio data to a server. When the server analyzes the audio data and identifies the train arrival announcement, it generates a text message saying "A train is arriving" and a train icon. This information is sent to the device and displayed on the device's screen, and at the same time, the device vibrates briefly to notify the user.

[0781] Specific examples of input prompts to generative AI models include the following:

[0782] Prompt: Describe a system that visually translates ambient sound into real-time notifications. Include specific hardware and software processes, and provide examples.

[0783] This system can provide real-time information to hearing-impaired users and those who want to visually recognize surrounding sounds in noisy environments, offering a more comprehensive and intuitive user experience.

[0784] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0785] Step 1:

[0786] Your device activates the microphone to collect ambient sounds.

[0787] Input: The user launches the dedicated application.

[0788] Data processing: Acquire analog audio signals

[0789] Output: Acquired raw analog audio signal

[0790] Specific operation: The device launches the application in response to user operation and enables the built-in microphone, which then continuously collects ambient sounds.

[0791] Step 2:

[0792] The device converts the collected audio into a digital signal.

[0793] Input: Raw analog audio signal

[0794] Data processing: Analog to digital conversion through sampling and quantization

[0795] Output: Digital audio data

[0796] How it works: The analog-to-digital converter (ADC) circuit in the device converts the analog voice signal into digital data, which represents the voice as a bit string of 0s and 1s.

[0797] Step 3:

[0798] The terminal compresses the audio data.

[0799] Input: Digital audio data

[0800] Data processing: Compression using a compression algorithm (e.g. MP3, AAC, etc.)

[0801] Output: Compressed audio data

[0802] How it works: The device passes digital audio data to a compression algorithm, which compresses it to reduce its size. This compressed data is then sent to the server.

[0803] Step 4:

[0804] The terminal transmits the compressed data to the server.

[0805] Input: Compressed audio data

[0806] Data calculation: transmission over a data communication network

[0807] Output: Compressed data sent to the server

[0808] What it does: Your device uses your internet connection (Wi-Fi or mobile data) to send compressed audio data to the server. The data is split into packets and sent.

[0809] Step 5:

[0810] The server decompresses the received audio data.

[0811] Input: Compressed audio data

[0812] Data operation: Decompression by decompression algorithm

[0813] Output: Decompressed digital audio data

[0814] What it does: The server passes the compressed audio data it receives through a decompression algorithm, restoring it to the original digital audio data, which can then be analyzed.

[0815] Step 6:

[0816] The server analyzes the audio data and identifies specific sounds.

[0817] Input: Decompressed digital audio data

[0818] Data Computing: Machine Learning Algorithms for Speech Analysis and Sound Identification

[0819] Output: Identified specific sound information

[0820] What it does: The server runs a speech analysis algorithm to identify specific sounds (e.g., car horn, dog barking, doorbell, etc.) from the decompressed digital audio data. It then uses a machine learning model to extract sound features and match them with pre-trained sound patterns.

[0821] Step 7:

[0822] The server generates corresponding visual information and vibration patterns based on the identified sounds.

[0823] Input: Identified specific sound information

[0824] Data Computing: Generation of visual information (text and icons) and vibration patterns

[0825] Output: Visual information and vibration patterns

[0826] Specific behavior: The server generates visual information (e.g., a text message saying "I hear a dog barking" and a dog icon) and vibration patterns corresponding to the identified sound. Corresponding data is generated.

[0827] Step 8:

[0828] The server generates visual information and transmits vibration patterns to the terminal.

[0829] Input: Generated visual information and vibration patterns

[0830] Data calculation: Sending data to the terminal

[0831] Output: Sending visual information and vibration patterns to the device

[0832] How it works: The server uses an internet connection to send the generated visual information and vibration patterns to the device. The data is divided into packets and sent to the device.

[0833] Step 9:

[0834] The device displays the received visual information on the screen and notifies the user by vibrating.

[0835] Input: Received visual information and vibration patterns

[0836] Data calculation: Screen display and vibration pattern execution

[0837] Output: User notification

[0838] Specific operation: The device displays the received visual information on the screen and vibrates according to the specified vibration pattern, thereby notifying the user visually and tactilely that a specific sound has occurred. For example, a text message saying "Train is arriving" is displayed on the screen, a train icon is displayed, and the device vibrates briefly.

[0839] (Application example 1)

[0840] 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."

[0841] Conventional systems often make it difficult to visually recognize surrounding sounds. In particular, in certain situations in physical stores (e.g., sales start dates or in-store announcements), appropriate information provision was lacking for hearing-impaired users and users who prefer to visually understand surrounding sounds. Furthermore, there were limited means of providing context-sensitive information in real time.

[0842] 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.

[0843] In this invention, the server includes a recording means for collecting ambient sounds, a conversion means for converting the recorded audio data into a digital signal, a compression means for compressing the converted audio data, a transmission means for transmitting the compressed audio data to the server, an identification means in the server for analyzing the audio data and identifying a specific sound, a generation means for generating visual information and a vibration pattern based on the identified sound, a return means for transmitting the generated visual information and vibration pattern to the terminal, a display means for displaying the returned visual information and vibrating according to the vibration pattern, a means for generating the visual information and vibration pattern when the specific sound is related to a specific situation in the physical store (e.g., the start of a sale or an in-store announcement), and a means for providing the visual information and vibration pattern via a smartphone, a head-mounted display, or the like. This enables users to be notified in real time by appropriate visual information and vibration patterns in specific situations in the physical store.

[0844] "Ambient sounds" refers to any sounds that come from the surrounding environment and can be detected through the ear.

[0845] "Recording means" refers to a device or function that continuously collects ambient sounds and stores them as digital data.

[0846] "Conversion means" refers to a device or function that converts collected audio data into a digital signal.

[0847] The "compression means" refers to a device or function for efficiently compressing the converted audio data to reduce the amount of data.

[0848] "Transmission means" refers to a device or function that transmits compressed audio data to a server via the Internet.

[0849] The "identification means" refers to a device or function that analyzes audio data in the server and identifies a specific sound.

[0850] "Generating means" refers to a device or function that generates visual information and vibration patterns based on the identified sounds.

[0851] The "returning means" refers to a device or function that transmits the generated visual information and vibration pattern to the terminal.

[0852] "Display means" refers to a device or function that displays the returned visual information and vibrates according to a vibration pattern.

[0853] A "specific situation" refers to a specific event or state that occurs within a physical store (e.g., the start of a sale or an in-store announcement).

[0854] A "smartphone" is a type of mobile phone that has multiple functions such as voice calls, Internet access, and application execution.

[0855] A "head-mounted display" is a display device that can be worn on the user's head and has the function of presenting visual information.

[0856] A specific embodiment of the present invention will be described below: This system analyzes surrounding sounds in real time and provides visual information and vibration notifications to the user based on the results.

[0857] First, a user uses a device with a dedicated application. The device is equipped with a microphone for collecting ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[0858] The collected voice data is converted into a digital signal by a conversion means in the terminal, and then the digital signal is compressed by a compression means, and the compressed voice data is transmitted to a server via the Internet through a transmission means.

[0859] The server decompresses the received audio data and analyzes it using a recognition means, which uses a machine learning algorithm to identify specific sounds (e.g., a sale announcement, a new product arrival announcement, etc.). Based on the identified sound, the server generates corresponding visual information (e.g., a text message or icon) and vibration patterns using a generation means.

[0860] The generated visual information and vibration pattern are transmitted to the terminal using the return means, and the terminal displays the returned information to the user through the display means and notifies the user that a specific sound has been generated by the vibration.

[0861] As a concrete example, suppose a user is shopping in a physical store. An application collects ambient sounds and sends them to a server. When an announcement of a sale is detected in the store, the server generates a text message saying "Sale has started" and a sale icon and sends it to the device. This notifies the user by displaying a visual message on the device screen and a short vibration.

[0862] The system includes the following hardware and software:

[0863] Hardware: Your smartphone's microphone, speaker, display, and vibrator.

[0864] software:

[0865] Pyaudio: Audio recording.

[0866] Requests: Server communication.

[0867] haptics(tentative module): vibration notifications.

[0868] Python: A programming language.

[0869] Examples of specific generated AI prompt sentences are as follows:

[0870] "You are a user of an application that visually translates surrounding sounds in real time. Right now, while you are shopping in a supermarket, you hear an announcement that a sale has started. How would the application respond?"

[0871] As a result of the above aspects, the present invention can provide users with visual and vibrational notifications in specific sound environments within physical stores, thereby improving convenience and safety.

[0872] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0873] Step 1:

[0874] When the user launches the application, the device's microphone is enabled.

[0875] Input: User action (application launch).

[0876] Output: The microphone is enabled and begins collecting ambient sounds.

[0877] Specific behavior: The application starts and the microphone device transitions to the active state.

[0878] Step 2:

[0879] The device continuously collects ambient sounds through a microphone.

[0880] Input: Ambient sounds.

[0881] Output: Recorded audio data.

[0882] What it does: The microphone captures ambient sounds and records them as audio signals.

[0883] Step 3:

[0884] The terminal converts the collected voice data into a digital signal.

[0885] Input: Recorded audio data (analog signal).

[0886] Output: Audio data converted into a digital signal.

[0887] What it does: Converts analog audio signals into digital data using the Pyaudio library.

[0888] Step 4:

[0889] The converted audio data is compressed using a compression algorithm.

[0890] Input: Audio data converted into a digital signal.

[0891] Output: Compressed audio data.

[0892] Specific operation: A codec is used to compress the audio data, reducing the amount of data.

[0893] Step 5:

[0894] The terminal transmits the compressed audio data to a server via the Internet.

[0895] Input: Compressed audio data.

[0896] Output: The data sent to the server.

[0897] What it does: Uses the Requests library to send compressed audio data to the server via an HTTP POST request.

[0898] Step 6:

[0899] The server decompresses the received audio data and analyzes it using the identification means.

[0900] Input: Compressed audio data sent to the server.

[0901] Output: The analyzed audio data.

[0902] What it does: It unpacks the compressed data on the server side and uses machine learning algorithms to identify specific sounds.

[0903] Step 7:

[0904] The server generates visual information and vibration patterns based on the identified sounds.

[0905] Input: Parsed audio data.

[0906] Output: Visual information and vibration patterns.

[0907] What it does: It runs an algorithm that generates text messages, icons, and vibration patterns.

[0908] Step 8:

[0909] The generated visual information and vibration pattern are transmitted to the terminal using a return means.

[0910] Input: Visual information and vibration patterns.

[0911] Output: Information sent back to the device.

[0912] Specific operation: Visual information and vibration patterns are sent from the server to the device using an HTTP response.

[0913] Step 9:

[0914] The terminal displays the returned visual information and vibrates according to the vibration pattern.

[0915] Input: Visual information and vibration patterns from the server.

[0916] Output: Visual and vibration notification to the user.

[0917] What it does: Shows text messages and icons on the display and vibrates for notifications.

[0918] 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.

[0919] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[0920] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the app, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0921] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[0922] The server receives the audio data sent from the device, which is then decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., car horns, dogs barking, doorbells, etc.).

[0923] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to a server, which then evaluates the user's emotional state based on this emotion recognition information.

[0924] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, it will provide more intuitive and concise notification information, while if the user is relaxed, it will adjust to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[0925] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[0926] For example, imagine a user is on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the data to the server. The server recognizes the user's level of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[0927] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0928] The processing flow will be explained below.

[0929] Step 1:

[0930] The user launches the app on their device. The app activates the device's microphone and camera and prepares to collect ambient sounds and the user's emotions.

[0931] Step 2:

[0932] The device uses a microphone to collect ambient sounds in real time, while simultaneously using a camera and voice analysis functions to capture the user's facial expressions and voice tone, using facial recognition algorithms and voice emotion analysis algorithms.

[0933] Step 3:

[0934] The analog voice data collected by the device is converted into a digital signal using an AD converter. At the same time, the user's emotional data (facial expressions and voice features) is also digitized.

[0935] Step 4:

[0936] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), reducing the size of the transmitted data. Emotion data is also compressed.

[0937] Step 5:

[0938] The device sends the compressed voice data and emotion data to a server via the Internet, using protocols such as HTTP to transfer the data.

[0939] Step 6:

[0940] The server receives the compressed voice data and emotion data sent from the terminal, and prepares the received data for decoding into an appropriate format.

[0941] Step 7:

[0942] The server decompresses the compressed audio data and restores it to its original digital form. Similarly, the emotion data is also decompressed and restored to its original form.

[0943] Step 8:

[0944] The server analyzes the audio data using analytical algorithms (e.g., machine learning models or speech recognition algorithms) to identify specific sounds (e.g., car horns, dog barks, doorbells), while also analyzing the emotional data to assess the user's current emotional state.

[0945] Step 9:

[0946] Based on the identified sounds, the server generates corresponding visual information (e.g., text messages or icons) and vibration patterns, adjusting the information to take into account the user's emotional state. For example, if the user is tense, it generates an intuitive and simple notification, while if they are relaxed, it provides detailed information.

[0947] Step 10:

[0948] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[0949] Step 11:

[0950] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[0951] Step 12:

[0952] The device displays the received visual information (e.g., a text message saying "I can hear a car horn" and a car icon) on the screen. The displayed information is adjusted according to the user's emotions.

[0953] Step 13:

[0954] The device vibrates according to the received vibration pattern, which is also adjusted according to the user's emotions.

[0955] This allows users to visually check their surroundings in real time and receive notifications tailored to their emotional state. For example, if a user is feeling nervous, the system will provide a concise and intuitive notification, allowing them to grasp the situation more quickly.

[0956] Example 2

[0957] 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."

[0958] Many modern users need to quickly and accurately grasp surrounding sounds and situations, while also seeking optimal notifications tailored to their emotional state. However, conventional systems provide only a uniform notification method without considering the user's emotional state, limiting the user experience. This invention aims to convert surrounding sounds into visual information and vibration patterns in real time, and further optimize notifications tailored to the user's emotional state.

[0959] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a recording means for collecting ambient sounds; a conversion means for converting the recorded audio data into a digital signal; a compression means for compressing the converted audio data; a transmission means for transmitting the compressed audio data to the server; an identification means in the server for analyzing the audio data and identifying specific sounds; a collection means for collecting the user's facial expressions and tone of voice using a camera and audio analysis function and generating emotional data; an emotional data transmission means for transmitting the collected emotional data to the server; an evaluation means for evaluating the user's emotional state using the emotional data; a generation means for generating visual information and a vibration pattern based on the identified sound and the evaluated emotional state; a return means for transmitting the generated visual information and vibration pattern to the terminal; and a display means for displaying the returned visual information and vibrating according to the vibration pattern. This enables optimal notification according to the user's emotional state.

[0960] A "recording means" is a device for collecting ambient sounds.

[0961] A "conversion means" is a device that converts recorded audio data into a digital signal.

[0962] The "compression means" is a device that compresses the converted audio data.

[0963] The "transmitting means" is a device that transmits compressed audio data to the server.

[0964] The "identification means" is a device that analyzes audio data in the server and identifies specific sounds.

[0965] The "collection means" is a device that uses a camera and voice analysis function to collect the user's facial expressions and tone of voice and generate emotion data.

[0966] The "emotion data transmission means" is a device that transmits collected emotion data to a server.

[0967] An "evaluation means" is a device that uses emotional data to evaluate the emotional state of a user.

[0968] A "generating means" is a device that generates visual information and vibration patterns based on the identified sounds and assessed emotional state.

[0969] The "returning means" is a device that transmits the generated visual information and vibration pattern to the terminal.

[0970] The "display means" is a device that displays the returned visual information and vibrates according to a vibration pattern.

[0971] A "voice analysis algorithm" is a computational method for analyzing voice data.

[0972] An "emotion recognition API" is an application programming interface for assessing a user's emotional state.

[0973] "Visual information" refers to information such as images and text that notifies the user.

[0974] A "vibration pattern" is a type of vibration that occurs in response to a particular sound or the user's emotional state.

[0975] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system are described below.

[0976] In this system, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, and a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[0977] In this system, the hardware used includes a built-in microphone and camera, and the software used includes voice analysis software (e.g., Google Speech-to-Text API) and facial expression recognition algorithms (e.g., Microsoft Azure's Face API). The collected voice data is converted into a digital signal by the device and then compressed using a compression algorithm (e.g., MP3, FLAC). The compressed data is then sent to a server over the Internet.

[0978] The server receives the voice data sent from the device, decompresses it, and then analyzes it using a voice analysis algorithm (e.g., Google Speech-to-Text API). This analysis identifies specific sounds (e.g., car horns, dog barks, doorbells, etc.). The device also analyzes the user's facial expressions and tone of voice in real time using its camera and voice analysis functions, and sends the data to the server. The server then uses emotion recognition APIs (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state.

[0979] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, the server will provide more intuitive and concise notification information, while if the user is relaxed, the server will adjust the notification to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[0980] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user of the occurrence of a specific sound. The notification content and vibration intensity are also adjusted according to the user's emotional state.

[0981] Specifically, imagine a user standing on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the results to the server. The server recognizes the user's state of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[0982] In this way, the present invention is a system that provides a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[0983] Prompt Sentence Examples

[0984] Please explain the process: "The device collects audio and sends it to the server. The server then analyzes the audio and generates an appropriate notification, optimizing the content of the notification according to the user's emotional state."

[0985] The above is the "Mode for Carrying Out the Invention."

[0986] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0987] Step 1: The user launches the application.

[0988] The user launches a dedicated application on their device. This application includes functionality to collect ambient sounds and recognize the user's emotions. When the application is launched, the device's microphone and camera are enabled and ready to collect data. The input is the user's action, and the output is the state that the app starts running on the device.

[0989] Step 2: The device collects surrounding sounds and the user's facial expressions.

[0990] The device uses a built-in microphone to continuously collect ambient sounds, while simultaneously using a camera and audio analysis capabilities to collect the user's facial expressions and tone of voice. The input is ambient sounds and the user's facial expressions, and the output is raw audio and facial expression data, which is used for further analysis.

[0991] Step 3: The device converts the voice data into a digital signal.

[0992] The device uses a conversion method to convert the collected voice data into a digital signal. The hardware used includes a built-in microphone and software that contains a voice analysis algorithm. The input is raw voice data and the output is a digital voice signal. This converted data is then used for data compression.

[0993] Step 4: The device compresses the audio data and sends it to the server.

[0994] The terminal compresses the digitally converted audio data using a compression algorithm (e.g., MP3, FLAC). The compressed audio data is then sent to a server via the Internet. The input is a digital audio signal, and the output is compressed audio data. This data is delivered to the server using a transmission means.

[0995] Step 5: The server decompresses and analyzes the audio data.

[0996] The server decompresses the received compressed audio data. The decompressed audio data is analyzed using a speech analysis algorithm (e.g., Google Speech-to-Text API) to identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). The input is the compressed audio data, and the output is the identified specific sound information.

[0997] Step 6: The device analyzes the user's facial expressions and tone of voice in real time.

[0998] The device uses a camera and voice analysis function to analyze the user's facial expressions and tone of voice in real time. The analysis results are generated as emotional data, which is then sent to the server. The input is the user's facial expressions and tone of voice, and the output is emotional data.

[0999] Step 7: The server evaluates the emotion data.

[1000] The server uses an emotion recognition API (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state based on the emotion data sent from the device. The input is emotion data, and the output is evaluation information about the user's emotional state.

[1001] Step 8: The server generates an optimized response.

[1002] The server generates optimal visual information and vibration patterns based on the identified sounds and the evaluated emotional state. For example, it generates a brief notification if the user is tense, and a detailed notification if the user is relaxed. The input is specific sound information and the evaluation information of the emotional state, and the output is visual information and vibration patterns.

[1003] Step 9: The server sends the generated information to the terminal.

[1004] The server sends the generated visual information and vibration patterns to the device using an appropriate protocol (e.g., HTTP, HTTPS). The input is the visual information and vibration patterns, and the output is the data sent to the device.

[1005] Step 10: The device displays the information and notifies the user by vibrating.

[1006] The device displays the received visual information on the screen and notifies the user by vibration using a vibration motor. The input is the visual information and vibration pattern received from the server, and the output is the screen display and vibration. The notification content and vibration intensity are adjusted according to the user's emotional state.

[1007] The above is the specific processing flow of the program of this system and the operation at each step.

[1008] (Application example 2)

[1009] 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."

[1010] While conventional security systems have the ability to collect ambient sounds and identify specific sounds, they are unable to optimize their responses by taking into account the user's emotional state. As a result, they are unable to provide appropriate notifications even when the user is feeling tense or scared, which can result in the ineffectiveness of security alerts. Furthermore, inappropriate notification methods can cause excessive stress to users and, conversely, increase their anxiety.

[1011] The specific processing by the specific 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 an identification means for analyzing audio data and identifying a specific sound, an emotion recognition means for recognizing the user's emotional state, and a response optimization means for optimizing visual information and vibration patterns based on the identified sound and the recognized emotional state of the user. This makes it possible to generate and provide appropriate visual and vibration notifications according to the user's emotional state.

[1012] "Ambient sounds" refers to all sounds occurring in the user's environment.

[1013] "Recording means" refers to a device or facility for collecting and recording ambient sounds.

[1014] "Conversion means" refers to a device or function for converting recorded audio data into a digital signal.

[1015] "Compression means" refers to a device or function for compressing the converted audio data.

[1016] "Transmission means" refers to a device or function for transmitting compressed audio data to a server.

[1017] "Identification means" refers to a device or function that analyzes audio data on the server and identifies a particular sound.

[1018] "Generating means" refers to a device or function for generating visual information and vibration patterns based on the identified sounds.

[1019] "Emotion recognition means" refers to a device or function for recognizing the emotional state of a user.

[1020] "Response Optimizer" refers to a device or function for optimizing visual information and vibration patterns based on the user's emotional state.

[1021] "Returning means" refers to a device or function for transmitting the generated and optimized visual information and vibration patterns to the terminal.

[1022] "Display means" refers to a device or function for displaying returned visual information and vibrating according to a vibration pattern.

[1023] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[1024] In this system, the user first uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[1025] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[1026] The server receives the audio data sent from the device, and after receiving it, it is decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., breaking glass, alarms, loud voices, etc.).

[1027] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to the server. The server evaluates the user's emotional state based on this emotion recognition information. The emotion recognition library used here is "EmotionRecognizer," and the voice analysis library is "AudioAnalyzer."

[1028] Based on this, the server takes into account the identified sound and the user's emotional state and generates corresponding visual information and vibration patterns. For example, if the user is surprised, it will provide more intuitive and simple notification information. On the other hand, if the user is relaxed, it will adjust to provide more detailed information. This generated visual information and vibration pattern is sent to the device.

[1029] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[1030] As a concrete example, imagine a user is alone at home and hears the sound of breaking window glass. The device's microphone detects this sound and sends the data to the server. The server analyzes the audio and identifies it as the sound of breaking glass. At the same time, the device captures the user's face with its camera and performs facial analysis to identify a surprised expression. The server uses this information to instantly generate a simple warning message and a strong vibration pattern. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly and strongly to notify the user.

[1031] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[1032] Example prompt sentence:

[1033] When certain environmental sounds (breaking glass, alarms, loud voices) are detected, create an application that provides real-time visual notification of the sound and the user's emotional state (anxiety, fear). If the user is experiencing fear, provide a simple and intuitive warning message and a strong vibration pattern.

[1034] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1035] Step 1:

[1036] The device collects ambient sounds using a microphone. The input is ambient sounds, and the output is voice data. This voice data is converted into a digital signal and compressed using a compression algorithm. The specific operation here is the process of continuously collecting voice data and converting it into a digital format.

[1037] Step 2:

[1038] The terminal transmits the compressed voice data to the server. The input is the compressed voice data, and the output is the data transmitted to the server. The specific operation is a process of transmitting the compressed voice data via the Internet.

[1039] Step 3:

[1040] The server decompresses the received audio data and analyzes it using an audio analysis algorithm. The input is compressed audio data, and the output is identification data of specific sounds. The specific operations are to decompress the audio data and identify specific sounds using the audio analysis algorithm.

[1041] Step 4:

[1042] The device captures the user's facial expressions with a camera and recognizes the user's emotional state using an emotion recognition algorithm. The input is a video of the user's facial expression, and the output is the user's emotional data. The specific operation is to capture facial expressions in real time and analyze the emotions using an emotion recognition algorithm.

[1043] Step 5:

[1044] The terminal transmits the recognized emotion data to the server. The input is emotion data, and the output is the emotion data transmitted to the server. The specific operation is a process of transmitting emotion data via the Internet.

[1045] Step 6:

[1046] The server analyzes the identified sound data and emotional information and generates optimal visual information and vibration patterns. The input is the sound identification data and emotional data, and the output is the optimized visual information and vibration patterns. The specific operation is to generate visual information and vibration patterns using a generative AI model based on the sound identification results and emotional state.

[1047] Step 7:

[1048] The server transmits the generated visual information and vibration patterns to the terminal. The input is the optimized visual information and vibration patterns, and the output is the data transmitted to the terminal. The specific operation is a process of transmitting the generated information via the Internet.

[1049] Step 8:

[1050] The device displays the received visual information on the screen and vibrates according to the vibration pattern. The input is the visual information and vibration pattern received from the server, and the output is a notification to the user. The specific operation is to display the visual information on the screen and vibrate according to the pattern using the built-in vibration function.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] [Fourth embodiment]

[1055] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1056] 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.

[1057] 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).

[1058] 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.

[1059] 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.

[1060] 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).

[1061] 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. 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.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] 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.

[1067] 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."

[1068] The present invention provides a system for visually converting ambient sounds in real time and notifying the user of the converted sounds. Specific embodiments of the system will be described below.

[1069] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[1070] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[1071] The server receives the audio data sent from the device. After receiving it, the audio data is decompressed and analyzed using a data analysis algorithm. During this analysis, the server identifies specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.).

[1072] The server generates corresponding visual information and vibration patterns based on the identified sounds. The visual information may include a text message or icon corresponding to the particular sound. For example, if a car horn sound is identified, the server generates the text "Car horn sound heard" and a car icon.

[1073] The server then transmits the generated visual information and vibration pattern to the device, which then displays the received visual information on its screen to inform the user of the surrounding situation.The device also vibrates according to the vibration pattern to notify the user of the occurrence of a specific sound.

[1074] As a concrete example, suppose a user is standing on a train platform. At this time, a device that collects surrounding sounds detects an announcement of a train's arrival. The audio data is sent to a server, where it is analyzed, generating a text message saying "A train is arriving" and a train icon. These are then sent to the device, which then displays them on the device's screen, and the device vibrates briefly to notify the user.

[1075] In this way, the present invention is a system that allows users to visually recognize surrounding sounds and provides necessary information in real time, thereby providing a more comprehensive and intuitive user experience for people with hearing impairments and users who want to visually recognize environmental sounds.

[1076] The processing flow will be explained below.

[1077] Step 1:

[1078] The user launches an app on the device, which then activates the device's microphone and prepares to collect ambient sounds.

[1079] Step 2:

[1080] The device uses a microphone to collect ambient sounds in real time, and audio data is captured continuously.

[1081] Step 3:

[1082] The analog voice data collected by the terminal is converted into a digital signal using an AD converter.

[1083] Step 4:

[1084] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), thereby reducing the size of the transmitted data.

[1085] Step 5:

[1086] The device sends the compressed audio data to the server via the Internet, using protocols such as HTTP to transfer the data.

[1087] Step 6:

[1088] The server receives the compressed audio data sent from the terminal and prepares it to decode the received data into an appropriate format.

[1089] Step 7:

[1090] The server decompresses the compressed audio data and restores it to the original digital audio data.

[1091] Step 8:

[1092] The server analyzes the audio data using an analysis algorithm (e.g., a machine learning model or a voice recognition algorithm) to identify specific sounds (e.g., a car horn, a dog barking, a doorbell).

[1093] Step 9:

[1094] The server generates corresponding visual information (e.g., a text message or icon) and vibration patterns based on the identified sound.

[1095] Step 10:

[1096] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[1097] Step 11:

[1098] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[1099] Step 12:

[1100] The device displays the visual information it receives (e.g., a text message saying "I hear a car horn" and a car icon) on the screen.

[1101] Step 13:

[1102] The device vibrates according to the vibration pattern received by the terminal, and provides the user with a notification for a specific sound based on the vibration pattern.

[1103] This series of processes allows users to visually check surrounding sounds in real time and receive vibration notifications. For example, if a user is on a train platform, the device detects the train arrival announcement, analyzes it on the server, and generates appropriate visual information and vibration notifications to notify the user, allowing them to understand their surroundings.

[1104] Example 1

[1105] 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."

[1106] Conventional voice notification systems lack the ability to accurately identify sound types and notify users as visual information or vibration patterns, which makes it difficult for hearing-impaired users and users in noisy environments to effectively recognize surrounding sound information in real time.

[1107] 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.

[1108] In this invention, the server includes a decompression means for decompressing audio data, an identification means for analyzing the decompressed data and identifying specific sounds, and a generation means for generating visual information and vibration patterns based on the identified sounds, thereby enabling the server to accurately identify surrounding sounds and notify the user in real time as visual information and vibration patterns.

[1109] A "collection means" is a function or device for collecting ambient sounds.

[1110] "Conversion means" refers to a function or device that converts collected audio data into a digital signal.

[1111] "Compression means" refers to a function or device that compresses the converted audio data.

[1112] The "transmission means" is a function or device that transmits compressed audio data to a server via a data communication network.

[1113] The "decompression means" is a function or device that decompresses audio data received by the server.

[1114] The "identification means" is a function or device that analyzes the decompressed audio data and identifies a particular sound.

[1115] "Generating means" refers to a function or device that generates visual information and vibration patterns based on the identified sounds.

[1116] The "returning means" is a function or device that transmits the generated visual information and vibration pattern to the terminal.

[1117] "Display means" is a function or device that displays the returned visual information and vibrates according to a vibration pattern.

[1118] The present invention provides a system that visually converts ambient sounds in real time and notifies the user of the results. This system is realized by taking the following specific steps.

[1119] First, the user starts a device with a dedicated application installed. This device is equipped with a microphone for collecting ambient sounds. When the user starts the application, the device's microphone is enabled and begins to continuously collect ambient sounds.

[1120] The device then converts the collected audio into a digital signal. This digital signal conversion is the process of converting analog audio data into a digital format, and involves sampling and quantization. The digital audio data is then compressed using a compression algorithm. Examples of compression algorithms used include MP3 and AAC. This compressed data is then sent over the internet to a server.

[1121] The server receives the voice data sent from the device. After receiving it, the voice data is decompressed and restored to its original digital signal. The server then uses a data analysis algorithm to analyze the voice data and identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). This analysis is performed using a machine learning algorithm.

[1122] After the server identifies a specific sound, it generates corresponding visual information and vibration patterns based on the identified sound. For example, if a car horn sound is identified, the text "I hear a car horn" and a car icon are generated. If a dog bark is identified, the text "Dog barking" and a dog icon are generated. The generated visual information and vibration patterns are then sent back to the device via the Internet.

[1123] The device displays the received visual information on the screen to notify the user of the surrounding situation, and at the same time, the device vibrates according to a specified vibration pattern and notifies the user that a specific sound has occurred.

[1124] As a concrete example, consider a case where a user is on a train platform. At this time, the user's device collects train arrival announcements and sends the audio data to a server. When the server analyzes the audio data and identifies the train arrival announcement, it generates a text message saying "A train is arriving" and a train icon. This information is sent to the device and displayed on the device's screen, and at the same time, the device vibrates briefly to notify the user.

[1125] Specific examples of input prompts to generative AI models include the following:

[1126] Prompt: Describe a system that visually translates ambient sound into real-time notifications. Include specific hardware and software processes, and provide examples.

[1127] This system can provide real-time information to hearing-impaired users and those who want to visually recognize surrounding sounds in noisy environments, offering a more comprehensive and intuitive user experience.

[1128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1129] Step 1:

[1130] Your device activates the microphone to collect ambient sounds.

[1131] Input: The user launches the dedicated application.

[1132] Data processing: Acquire analog audio signals

[1133] Output: Acquired raw analog audio signal

[1134] Specific operation: The device launches the application in response to user operation and enables the built-in microphone, which then continuously collects ambient sounds.

[1135] Step 2:

[1136] The device converts the collected audio into a digital signal.

[1137] Input: Raw analog audio signal

[1138] Data processing: Analog to digital conversion through sampling and quantization

[1139] Output: Digital audio data

[1140] How it works: The analog-to-digital converter (ADC) circuit in the device converts the analog voice signal into digital data, which represents the voice as a bit string of 0s and 1s.

[1141] Step 3:

[1142] The terminal compresses the audio data.

[1143] Input: Digital audio data

[1144] Data processing: Compression using a compression algorithm (e.g. MP3, AAC, etc.)

[1145] Output: Compressed audio data

[1146] How it works: The device passes digital audio data to a compression algorithm, which compresses it to reduce its size. This compressed data is then sent to the server.

[1147] Step 4:

[1148] The terminal transmits the compressed data to the server.

[1149] Input: Compressed audio data

[1150] Data calculation: transmission over a data communication network

[1151] Output: Compressed data sent to the server

[1152] What it does: Your device uses your internet connection (Wi-Fi or mobile data) to send compressed audio data to the server. The data is split into packets and sent.

[1153] Step 5:

[1154] The server decompresses the received audio data.

[1155] Input: Compressed audio data

[1156] Data operation: Decompression by decompression algorithm

[1157] Output: Decompressed digital audio data

[1158] What it does: The server passes the compressed audio data it receives through a decompression algorithm, restoring it to the original digital audio data, which can then be analyzed.

[1159] Step 6:

[1160] The server analyzes the audio data and identifies specific sounds.

[1161] Input: Decompressed digital audio data

[1162] Data Computing: Machine Learning Algorithms for Speech Analysis and Sound Identification

[1163] Output: Identified specific sound information

[1164] What it does: The server runs a speech analysis algorithm to identify specific sounds (e.g., car horn, dog barking, doorbell, etc.) from the decompressed digital audio data. It then uses a machine learning model to extract sound features and match them with pre-trained sound patterns.

[1165] Step 7:

[1166] The server generates corresponding visual information and vibration patterns based on the identified sounds.

[1167] Input: Identified specific sound information

[1168] Data Computing: Generation of visual information (text and icons) and vibration patterns

[1169] Output: Visual information and vibration patterns

[1170] Specific behavior: The server generates visual information (e.g., a text message saying "I hear a dog barking" and a dog icon) and vibration patterns corresponding to the identified sound. Corresponding data is generated.

[1171] Step 8:

[1172] The server generates visual information and transmits vibration patterns to the terminal.

[1173] Input: Generated visual information and vibration patterns

[1174] Data calculation: Sending data to the terminal

[1175] Output: Sending visual information and vibration patterns to the device

[1176] How it works: The server uses an internet connection to send the generated visual information and vibration patterns to the device. The data is divided into packets and sent to the device.

[1177] Step 9:

[1178] The device displays the received visual information on the screen and notifies the user by vibrating.

[1179] Input: Received visual information and vibration patterns

[1180] Data calculation: Screen display and vibration pattern execution

[1181] Output: User notification

[1182] Specific operation: The device displays the received visual information on the screen and vibrates according to the specified vibration pattern, thereby notifying the user visually and tactilely that a specific sound has occurred. For example, a text message saying "Train is arriving" is displayed on the screen, a train icon is displayed, and the device vibrates briefly.

[1183] (Application example 1)

[1184] 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."

[1185] Conventional systems often make it difficult to visually recognize surrounding sounds. In particular, in certain situations in physical stores (e.g., sales start dates or in-store announcements), appropriate information provision was lacking for hearing-impaired users and users who prefer to visually understand surrounding sounds. Furthermore, there were limited means of providing context-sensitive information in real time.

[1186] 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.

[1187] In this invention, the server includes a recording means for collecting ambient sounds, a conversion means for converting the recorded audio data into a digital signal, a compression means for compressing the converted audio data, a transmission means for transmitting the compressed audio data to the server, an identification means in the server for analyzing the audio data and identifying a specific sound, a generation means for generating visual information and a vibration pattern based on the identified sound, a return means for transmitting the generated visual information and vibration pattern to the terminal, a display means for displaying the returned visual information and vibrating according to the vibration pattern, a means for generating the visual information and vibration pattern when the specific sound is related to a specific situation in the physical store (e.g., the start of a sale or an in-store announcement), and a means for providing the visual information and vibration pattern via a smartphone, a head-mounted display, or the like. This enables users to be notified in real time by appropriate visual information and vibration patterns in specific situations in the physical store.

[1188] "Ambient sounds" refers to any sounds that come from the surrounding environment and can be detected through the ear.

[1189] "Recording means" refers to a device or function that continuously collects ambient sounds and stores them as digital data.

[1190] "Conversion means" refers to a device or function that converts collected audio data into a digital signal.

[1191] The "compression means" refers to a device or function for efficiently compressing the converted audio data to reduce the amount of data.

[1192] "Transmission means" refers to a device or function that transmits compressed audio data to a server via the Internet.

[1193] The "identification means" refers to a device or function that analyzes audio data in the server and identifies a specific sound.

[1194] "Generating means" refers to a device or function that generates visual information and vibration patterns based on the identified sounds.

[1195] The "returning means" refers to a device or function that transmits the generated visual information and vibration pattern to the terminal.

[1196] "Display means" refers to a device or function that displays the returned visual information and vibrates according to a vibration pattern.

[1197] A "specific situation" refers to a specific event or state that occurs within a physical store (e.g., the start of a sale or an in-store announcement).

[1198] A "smartphone" is a type of mobile phone that has multiple functions such as voice calls, Internet access, and application execution.

[1199] A "head-mounted display" is a display device that can be worn on the user's head and has the function of presenting visual information.

[1200] A specific embodiment of the present invention will be described below: This system analyzes surrounding sounds in real time and provides visual information and vibration notifications to the user based on the results.

[1201] First, a user uses a device with a dedicated application. The device is equipped with a microphone for collecting ambient sounds. When the user launches the application, the microphone is enabled and begins to continuously collect ambient sounds.

[1202] The collected voice data is converted into a digital signal by a conversion means in the terminal, and then the digital signal is compressed by a compression means, and the compressed voice data is transmitted to a server via the Internet through a transmission means.

[1203] The server decompresses the received audio data and analyzes it using a recognition means, which uses a machine learning algorithm to identify specific sounds (e.g., a sale announcement, a new product arrival announcement, etc.). Based on the identified sound, the server generates corresponding visual information (e.g., a text message or icon) and vibration patterns using a generation means.

[1204] The generated visual information and vibration pattern are transmitted to the terminal using the return means, and the terminal displays the returned information to the user through the display means and notifies the user that a specific sound has been generated by the vibration.

[1205] As a concrete example, suppose a user is shopping in a physical store. An application collects ambient sounds and sends them to a server. When an announcement of a sale is detected in the store, the server generates a text message saying "Sale has started" and a sale icon and sends it to the device. This notifies the user by displaying a visual message on the device screen and a short vibration.

[1206] The system includes the following hardware and software:

[1207] Hardware: Your smartphone's microphone, speaker, display, and vibrator.

[1208] software:

[1209] Pyaudio: Audio recording.

[1210] Requests: Server communication.

[1211] haptics(tentative module): vibration notifications.

[1212] Python: A programming language.

[1213] Examples of specific generated AI prompt sentences are as follows:

[1214] "You are a user of an application that visually translates surrounding sounds in real time. Right now, while you are shopping in a supermarket, you hear an announcement that a sale has started. How would the application respond?"

[1215] As a result of the above aspects, the present invention can provide users with visual and vibrational notifications in specific sound environments within physical stores, thereby improving convenience and safety.

[1216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1217] Step 1:

[1218] When the user launches the application, the device's microphone is enabled.

[1219] Input: User action (application launch).

[1220] Output: The microphone is enabled and begins collecting ambient sounds.

[1221] Specific behavior: The application starts and the microphone device transitions to the active state.

[1222] Step 2:

[1223] The device continuously collects ambient sounds through a microphone.

[1224] Input: Ambient sounds.

[1225] Output: Recorded audio data.

[1226] What it does: The microphone captures ambient sounds and records them as audio signals.

[1227] Step 3:

[1228] The terminal converts the collected voice data into a digital signal.

[1229] Input: Recorded audio data (analog signal).

[1230] Output: Audio data converted into a digital signal.

[1231] What it does: Converts analog audio signals into digital data using the Pyaudio library.

[1232] Step 4:

[1233] The converted audio data is compressed using a compression algorithm.

[1234] Input: Audio data converted into a digital signal.

[1235] Output: Compressed audio data.

[1236] Specific operation: A codec is used to compress the audio data, reducing the amount of data.

[1237] Step 5:

[1238] The terminal transmits the compressed audio data to a server via the Internet.

[1239] Input: Compressed audio data.

[1240] Output: The data sent to the server.

[1241] What it does: Uses the Requests library to send compressed audio data to the server via an HTTP POST request.

[1242] Step 6:

[1243] The server decompresses the received audio data and analyzes it using the identification means.

[1244] Input: Compressed audio data sent to the server.

[1245] Output: The analyzed audio data.

[1246] What it does: It unpacks the compressed data on the server side and uses machine learning algorithms to identify specific sounds.

[1247] Step 7:

[1248] The server generates visual information and vibration patterns based on the identified sounds.

[1249] Input: Parsed audio data.

[1250] Output: Visual information and vibration patterns.

[1251] What it does: It runs an algorithm that generates text messages, icons, and vibration patterns.

[1252] Step 8:

[1253] The generated visual information and vibration pattern are transmitted to the terminal using a return means.

[1254] Input: Visual information and vibration patterns.

[1255] Output: Information sent back to the device.

[1256] Specific operation: Visual information and vibration patterns are sent from the server to the device using an HTTP response.

[1257] Step 9:

[1258] The terminal displays the returned visual information and vibrates according to the vibration pattern.

[1259] Input: Visual information and vibration patterns from the server.

[1260] Output: Visual and vibration notification to the user.

[1261] What it does: Shows text messages and icons on the display and vibrates for notifications.

[1262] 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.

[1263] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[1264] First, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the app, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[1265] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[1266] The server receives the audio data sent from the device, which is then decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., car horns, dogs barking, doorbells, etc.).

[1267] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to a server, which then evaluates the user's emotional state based on this emotion recognition information.

[1268] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, it will provide more intuitive and concise notification information, while if the user is relaxed, it will adjust to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[1269] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[1270] For example, imagine a user is on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the data to the server. The server recognizes the user's level of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[1271] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[1272] The processing flow will be explained below.

[1273] Step 1:

[1274] The user launches the app on their device. The app activates the device's microphone and camera and prepares to collect ambient sounds and the user's emotions.

[1275] Step 2:

[1276] The device uses a microphone to collect ambient sounds in real time, while simultaneously using a camera and voice analysis functions to capture the user's facial expressions and voice tone, using facial recognition algorithms and voice emotion analysis algorithms.

[1277] Step 3:

[1278] The analog voice data collected by the device is converted into a digital signal using an AD converter. At the same time, the user's emotional data (facial expressions and voice features) is also digitized.

[1279] Step 4:

[1280] The device compresses the digital audio data using a compression algorithm (e.g., MP3 or AAC codec), reducing the size of the transmitted data. Emotion data is also compressed.

[1281] Step 5:

[1282] The device sends the compressed voice data and emotion data to a server via the Internet, using protocols such as HTTP to transfer the data.

[1283] Step 6:

[1284] The server receives the compressed voice data and emotion data sent from the terminal, and prepares the received data for decoding into an appropriate format.

[1285] Step 7:

[1286] The server decompresses the compressed audio data and restores it to its original digital form. Similarly, the emotion data is also decompressed and restored to its original form.

[1287] Step 8:

[1288] The server analyzes the audio data using analytical algorithms (e.g., machine learning models or speech recognition algorithms) to identify specific sounds (e.g., car horns, dog barks, doorbells), while also analyzing the emotional data to assess the user's current emotional state.

[1289] Step 9:

[1290] Based on the identified sounds, the server generates corresponding visual information (e.g., text messages or icons) and vibration patterns, adjusting the information to take into account the user's emotional state. For example, if the user is tense, it generates an intuitive and simple notification, while if they are relaxed, it provides detailed information.

[1291] Step 10:

[1292] The server sends the generated data, including the visual information and vibration patterns, to the device, which is then sent again via HTTP protocol.

[1293] Step 11:

[1294] The device receives the visual information and vibration patterns sent from the server, and the received data is processed appropriately within the app.

[1295] Step 12:

[1296] The device displays the received visual information (e.g., a text message saying "I can hear a car horn" and a car icon) on the screen. The displayed information is adjusted according to the user's emotions.

[1297] Step 13:

[1298] The device vibrates according to the received vibration pattern, which is also adjusted according to the user's emotions.

[1299] This allows users to visually check their surroundings in real time and receive notifications tailored to their emotional state. For example, if a user is feeling nervous, the system will provide a concise and intuitive notification, allowing them to grasp the situation more quickly.

[1300] Example 2

[1301] 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."

[1302] Many modern users need to quickly and accurately grasp surrounding sounds and situations, while also seeking optimal notifications tailored to their emotional state. However, conventional systems provide only a uniform notification method without considering the user's emotional state, limiting the user experience. This invention aims to convert surrounding sounds into visual information and vibration patterns in real time, and further optimize notifications tailored to the user's emotional state.

[1303] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a recording means for collecting ambient sounds; a conversion means for converting the recorded audio data into a digital signal; a compression means for compressing the converted audio data; a transmission means for transmitting the compressed audio data to the server; an identification means in the server for analyzing the audio data and identifying specific sounds; a collection means for collecting the user's facial expressions and tone of voice using a camera and audio analysis function and generating emotional data; an emotional data transmission means for transmitting the collected emotional data to the server; an evaluation means for evaluating the user's emotional state using the emotional data; a generation means for generating visual information and a vibration pattern based on the identified sound and the evaluated emotional state; a return means for transmitting the generated visual information and vibration pattern to the terminal; and a display means for displaying the returned visual information and vibrating according to the vibration pattern. This enables optimal notification according to the user's emotional state.

[1304] A "recording means" is a device for collecting ambient sounds.

[1305] A "conversion means" is a device that converts recorded audio data into a digital signal.

[1306] The "compression means" is a device that compresses the converted audio data.

[1307] The "transmitting means" is a device that transmits compressed audio data to the server.

[1308] The "identification means" is a device that analyzes audio data in the server and identifies specific sounds.

[1309] The "collection means" is a device that uses a camera and voice analysis function to collect the user's facial expressions and tone of voice and generate emotion data.

[1310] The "emotion data transmission means" is a device that transmits collected emotion data to a server.

[1311] An "evaluation means" is a device that uses emotional data to evaluate the emotional state of a user.

[1312] A "generating means" is a device that generates visual information and vibration patterns based on the identified sounds and assessed emotional state.

[1313] The "returning means" is a device that transmits the generated visual information and vibration pattern to the terminal.

[1314] The "display means" is a device that displays the returned visual information and vibrates according to a vibration pattern.

[1315] A "voice analysis algorithm" is a computational method for analyzing voice data.

[1316] An "emotion recognition API" is an application programming interface for assessing a user's emotional state.

[1317] "Visual information" refers to information such as images and text that notifies the user.

[1318] A "vibration pattern" is a type of vibration that occurs in response to a particular sound or the user's emotional state.

[1319] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system are described below.

[1320] In this system, the user uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, and a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[1321] In this system, the hardware used includes a built-in microphone and camera, and the software used includes voice analysis software (e.g., Google Speech-to-Text API) and facial expression recognition algorithms (e.g., Microsoft Azure's Face API). The collected voice data is converted into a digital signal by the device and then compressed using a compression algorithm (e.g., MP3, FLAC). The compressed data is then sent to a server over the Internet.

[1322] The server receives the voice data sent from the device, decompresses it, and then analyzes it using a voice analysis algorithm (e.g., Google Speech-to-Text API). This analysis identifies specific sounds (e.g., car horns, dog barks, doorbells, etc.). The device also analyzes the user's facial expressions and tone of voice in real time using its camera and voice analysis functions, and sends the data to the server. The server then uses emotion recognition APIs (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state.

[1323] The server generates corresponding visual information and vibration patterns based on the identified sounds, taking into account the user's emotional state. For example, if the user is nervous, the server will provide more intuitive and concise notification information, while if the user is relaxed, the server will adjust the notification to provide more detailed information. The generated visual information and vibration patterns are then sent to the device.

[1324] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user of the occurrence of a specific sound. The notification content and vibration intensity are also adjusted according to the user's emotional state.

[1325] Specifically, imagine a user standing on a train platform. The device detects a train arrival announcement and sends the data to the server. The server analyzes the voice and generates information indicating the train's arrival. At the same time, the device analyzes the user's emotions and sends the results to the server. The server recognizes the user's state of tension and generates a more intuitive text message and simple icon. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly to notify the user.

[1326] In this way, the present invention is a system that provides a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[1327] Prompt Sentence Examples

[1328] Please explain the process: "The device collects audio and sends it to the server. The server then analyzes the audio and generates an appropriate notification, optimizing the content of the notification according to the user's emotional state."

[1329] The above is the "Mode for Carrying Out the Invention."

[1330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1331] Step 1: The user launches the application.

[1332] The user launches a dedicated application on their device. This application includes functionality to collect ambient sounds and recognize the user's emotions. When the application is launched, the device's microphone and camera are enabled and ready to collect data. The input is the user's action, and the output is the state that the app starts running on the device.

[1333] Step 2: The device collects surrounding sounds and the user's facial expressions.

[1334] The device uses a built-in microphone to continuously collect ambient sounds, while simultaneously using a camera and audio analysis capabilities to collect the user's facial expressions and tone of voice. The input is ambient sounds and the user's facial expressions, and the output is raw audio and facial expression data, which is used for further analysis.

[1335] Step 3: The device converts the voice data into a digital signal.

[1336] The device uses a conversion method to convert the collected voice data into a digital signal. The hardware used includes a built-in microphone and software that contains a voice analysis algorithm. The input is raw voice data and the output is a digital voice signal. This converted data is then used for data compression.

[1337] Step 4: The device compresses the audio data and sends it to the server.

[1338] The terminal compresses the digitally converted audio data using a compression algorithm (e.g., MP3, FLAC). The compressed audio data is then sent to a server via the Internet. The input is a digital audio signal, and the output is compressed audio data. This data is delivered to the server using a transmission means.

[1339] Step 5: The server decompresses and analyzes the audio data.

[1340] The server decompresses the received compressed audio data. The decompressed audio data is analyzed using a speech analysis algorithm (e.g., Google Speech-to-Text API) to identify specific sounds (e.g., car horn, dog barking, doorbell ringing, etc.). The input is the compressed audio data, and the output is the identified specific sound information.

[1341] Step 6: The device analyzes the user's facial expressions and tone of voice in real time.

[1342] The device uses a camera and voice analysis function to analyze the user's facial expressions and tone of voice in real time. The analysis results are generated as emotional data, which is then sent to the server. The input is the user's facial expressions and tone of voice, and the output is emotional data.

[1343] Step 7: The server evaluates the emotion data.

[1344] The server uses an emotion recognition API (e.g., Amazon Rekognition, Microsoft Azure Emotion API) to evaluate the user's emotional state based on the emotion data sent from the device. The input is emotion data, and the output is evaluation information about the user's emotional state.

[1345] Step 8: The server generates an optimized response.

[1346] The server generates optimal visual information and vibration patterns based on the identified sounds and the evaluated emotional state. For example, it generates a brief notification if the user is tense, and a detailed notification if the user is relaxed. The input is specific sound information and the evaluation information of the emotional state, and the output is visual information and vibration patterns.

[1347] Step 9: The server sends the generated information to the terminal.

[1348] The server sends the generated visual information and vibration patterns to the device using an appropriate protocol (e.g., HTTP, HTTPS). The input is the visual information and vibration patterns, and the output is the data sent to the device.

[1349] Step 10: The device displays the information and notifies the user by vibrating.

[1350] The device displays the received visual information on the screen and notifies the user by vibration using a vibration motor. The input is the visual information and vibration pattern received from the server, and the output is the screen display and vibration. The notification content and vibration intensity are adjusted according to the user's emotional state.

[1351] The above is the specific processing flow of the program of this system and the operation at each step.

[1352] (Application example 2)

[1353] 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."

[1354] While conventional security systems have the ability to collect ambient sounds and identify specific sounds, they are unable to optimize their responses by taking into account the user's emotional state. As a result, they are unable to provide appropriate notifications even when the user is feeling tense or scared, which can result in the ineffectiveness of security alerts. Furthermore, inappropriate notification methods can cause excessive stress to users and, conversely, increase their anxiety.

[1355] The specific processing by the specific 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 an identification means for analyzing audio data and identifying a specific sound, an emotion recognition means for recognizing the user's emotional state, and a response optimization means for optimizing visual information and vibration patterns based on the identified sound and the recognized emotional state of the user. This makes it possible to generate and provide appropriate visual and vibration notifications according to the user's emotional state.

[1356] "Ambient sounds" refers to all sounds occurring in the user's environment.

[1357] "Recording means" refers to a device or facility for collecting and recording ambient sounds.

[1358] "Conversion means" refers to a device or function for converting recorded audio data into a digital signal.

[1359] "Compression means" refers to a device or function for compressing the converted audio data.

[1360] "Transmission means" refers to a device or function for transmitting compressed audio data to a server.

[1361] "Identification means" refers to a device or function that analyzes audio data on the server and identifies a particular sound.

[1362] "Generating means" refers to a device or function for generating visual information and vibration patterns based on the identified sounds.

[1363] "Emotion recognition means" refers to a device or function for recognizing the emotional state of a user.

[1364] "Response Optimizer" refers to a device or function for optimizing visual information and vibration patterns based on the user's emotional state.

[1365] "Returning means" refers to a device or function for transmitting the generated and optimized visual information and vibration patterns to the terminal.

[1366] "Display means" refers to a device or function for displaying returned visual information and vibrating according to a vibration pattern.

[1367] The present invention provides a system that visually converts ambient sounds in real time and notifies the user, and also has an emotion recognition function that recognizes the user's emotions and optimizes the system's response. Specific embodiments of the system will be described below.

[1368] In this system, the user first uses a device with a dedicated application installed. This device is equipped with a microphone to collect ambient sounds, as well as a camera and voice analysis function to recognize the user's emotions. When the user launches the application, the microphone is enabled and continues to collect ambient sounds. The camera and voice analysis function are also enabled, preparing to recognize emotions from the user's facial expressions and tone of voice.

[1369] The device then converts the collected audio into a digital signal, compresses the audio data using a compression algorithm, and transmits the compressed data over the Internet to a server.

[1370] The server receives the audio data sent from the device, and after receiving it, it is decompressed and analyzed using a voice analysis algorithm to identify specific sounds (e.g., breaking glass, alarms, loud voices, etc.).

[1371] Furthermore, the device analyzes the user's facial expressions and tone of voice in real time using a camera and voice analysis function, and sends the data to the server. The server evaluates the user's emotional state based on this emotion recognition information. The emotion recognition library used here is "EmotionRecognizer," and the voice analysis library is "AudioAnalyzer."

[1372] Based on this, the server takes into account the identified sound and the user's emotional state and generates corresponding visual information and vibration patterns. For example, if the user is surprised, it will provide more intuitive and simple notification information. On the other hand, if the user is relaxed, it will adjust to provide more detailed information. This generated visual information and vibration pattern is sent to the device.

[1373] The device displays the received visual information on the screen to inform the user of the surrounding situation. At the same time, the device vibrates according to a vibration pattern and notifies the user when a specific sound is generated. The device also adjusts the displayed information and vibration intensity according to the user's emotions.

[1374] As a concrete example, imagine a user is alone at home and hears the sound of breaking window glass. The device's microphone detects this sound and sends the data to the server. The server analyzes the audio and identifies it as the sound of breaking glass. At the same time, the device captures the user's face with its camera and performs facial analysis to identify a surprised expression. The server uses this information to instantly generate a simple warning message and a strong vibration pattern. This information is sent to the device and displayed on the device's screen, and the device also vibrates briefly and strongly to notify the user.

[1375] In this way, the present invention is a system that can provide a more comprehensive and intuitive user experience by gaining insight into the user's emotions and optimizing the information provided based on that insight.

[1376] Example prompt sentence:

[1377] When certain environmental sounds (breaking glass, alarms, loud voices) are detected, create an application that provides real-time visual notification of the sound and the user's emotional state (anxiety, fear). If the user is experiencing fear, provide a simple and intuitive warning message and a strong vibration pattern.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] The device collects ambient sounds using a microphone. The input is ambient sounds, and the output is voice data. This voice data is converted into a digital signal and compressed using a compression algorithm. The specific operation here is the process of continuously collecting voice data and converting it into a digital format.

[1381] Step 2:

[1382] The terminal transmits the compressed voice data to the server. The input is the compressed voice data, and the output is the data transmitted to the server. The specific operation is a process of transmitting the compressed voice data via the Internet.

[1383] Step 3:

[1384] The server decompresses the received audio data and analyzes it using an audio analysis algorithm. The input is compressed audio data, and the output is identification data of specific sounds. The specific operations are to decompress the audio data and identify specific sounds using the audio analysis algorithm.

[1385] Step 4:

[1386] The device captures the user's facial expressions with a camera and recognizes the user's emotional state using an emotion recognition algorithm. The input is a video of the user's facial expression, and the output is the user's emotional data. The specific operation is to capture facial expressions in real time and analyze the emotions using an emotion recognition algorithm.

[1387] Step 5:

[1388] The terminal transmits the recognized emotion data to the server. The input is emotion data, and the output is the emotion data transmitted to the server. The specific operation is a process of transmitting emotion data via the Internet.

[1389] Step 6:

[1390] The server analyzes the identified sound data and emotional information and generates optimal visual information and vibration patterns. The input is the sound identification data and emotional data, and the output is the optimized visual information and vibration patterns. The specific operation is to generate visual information and vibration patterns using a generative AI model based on the sound identification results and emotional state.

[1391] Step 7:

[1392] The server transmits the generated visual information and vibration patterns to the terminal. The input is the optimized visual information and vibration patterns, and the output is the data transmitted to the terminal. The specific operation is a process of transmitting the generated information via the Internet.

[1393] Step 8:

[1394] The device displays the received visual information on the screen and vibrates according to the vibration pattern. The input is the visual information and vibration pattern received from the server, and the output is a notification to the user. The specific operation is to display the visual information on the screen and vibrate according to the pattern using the built-in vibration function.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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).

[1402] 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.

[1403] 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."

[1404] 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.

[1405] 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).

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] The following is further disclosed regarding the above embodiment.

[1417] (Claim 1)

[1418] a recording means for collecting ambient sounds;

[1419] a conversion means for converting the recorded audio data into a digital signal;

[1420] compression means for compressing the converted audio data;

[1421] a transmitting means for transmitting the compressed audio data to a server;

[1422] an identification means in the server that analyzes the audio data and identifies a specific sound;

[1423] generating means for generating visual information and vibration patterns based on the identified sounds;

[1424] a return means for transmitting the generated visual information and vibration pattern to a terminal;

[1425] The system includes a display means for displaying the returned visual information and vibrating according to a vibration pattern.

[1426] (Claim 2)

[1427] 10. The system of claim 1, wherein the identifying means analyzes the audio data using a machine learning algorithm.

[1428] (Claim 3)

[1429] 10. The system of claim 1, wherein said generating means generates text and iconic visual information corresponding to the identified sounds.

[1430] "Example 1"

[1431] (Claim 1)

[1432] a collecting means for collecting ambient sounds;

[1433] a conversion means for converting the collected voice data into a digital signal;

[1434] compression means for compressing the converted audio data;

[1435] a transmitting means for transmitting the compressed audio data to a server via a data communication network;

[1436] a decompression means for decompressing the audio data in the server;

[1437] an identification means for analyzing the decompressed data and identifying a particular sound;

[1438] generating means for generating visual information and vibration patterns based on the identified sounds;

[1439] a return means for transmitting the generated visual information and vibration pattern to a terminal;

[1440] The system includes a display means for displaying the returned visual information and vibrating according to a vibration pattern.

[1441] (Claim 2)

[1442] 10. The system of claim 1, wherein the identifying means analyzes the audio data using a machine learning algorithm.

[1443] (Claim 3)

[1444] 10. The system of claim 1, wherein the generating means generates text and iconic visual information corresponding to the identified sounds.

[1445] "Application Example 1"

[1446] (Claim 1)

[1447] a recording means for collecting ambient sounds;

[1448] a conversion means for converting the recorded audio data into a digital signal;

[1449] compression means for compressing the converted audio data;

[1450] a transmitting means for transmitting the compressed audio data to a server;

[1451] an identification means in the server that analyzes the audio data and identifies a specific sound;

[1452] generating means for generating visual information and vibration patterns based on the identified sounds;

[1453] a return means for transmitting the generated visual information and vibration pattern to a terminal;

[1454] a display means for displaying the returned visual information and vibrating according to a vibration pattern;

[1455] means for generating visual information and vibration patterns when a particular sound is associated with a particular situation in a physical store (e.g., the start of a sale or an in-store announcement);

[1456] A system that includes a means for providing visual information and vibration patterns, such as a smartphone or head-mounted display.

[1457] (Claim 2)

[1458] 10. The system of claim 1, wherein the identifying means uses a machine learning algorithm to analyze the audio data and generate visual information corresponding to the particular situation.

[1459] (Claim 3)

[1460] 2. The system of claim 1, wherein the generating means generates visual information of text, icons, and vibration patterns in specific situations corresponding to the identified sounds.

[1461] "Example 2: Combining Emotion Engines"

[1462] (Claim 1)

[1463] a recording means for collecting ambient sounds;

[1464] a conversion means for converting the recorded audio data into a digital signal;

[1465] compression means for compressing the converted audio data;

[1466] a transmitting means for transmitting the compressed audio data to a server;

[1467] an identification means in the server that analyzes the audio data and identifies a specific sound;

[1468] a collection means for collecting facial expressions and tone of voice of a user using a camera and a voice analysis function to generate emotion data;

[1469] emotion data transmission means for transmitting the collected emotion data to a server;

[1470] evaluation means for evaluating the emotional state of the user using the emotion data;

[1471] generating means for generating visual information and vibration patterns based on the identified sounds and the assessed emotional state;

[1472] a return means for transmitting the generated visual information and vibration pattern to a terminal;

[1473] The system includes a display means for displaying the returned visual information and vibrating according to a vibration pattern.

[1474] (Claim 2)

[1475] 2. The system of claim 1, wherein the identifying means analyzes the voice data using a machine learning algorithm and evaluates the emotion data using a machine learning algorithm.

[1476] (Claim 3)

[1477] 10. The system of claim 1, wherein the generating means generates text and iconic visual information corresponding to the identified sounds and the assessed emotional states.

[1478] "Application example 2 when combining emotion engines"

[1479] (Claim 1)

[1480] a recording means for collecting ambient sounds;

[1481] a conversion means for converting the recorded audio data into a digital signal;

[1482] compression means for compressing the converted audio data;

[1483] a transmitting means for transmitting the compressed audio data to a server;

[1484] an identification means in the server that analyzes the audio data and identifies a specific sound;

[1485] generating means for generating visual information and vibration patterns based on the identified sounds;

[1486] an emotion recognition means for recognizing an emotion of a user in parallel with the collection of voice data by the recording means;

[1487] response optimization means for optimizing visual information and vibration patterns based on the emotional state of the user obtained from the emotion recognition means;

[1488] A return means for transmitting the generated and optimized visual information and vibration pattern to a terminal;

[1489] The system includes a display means for displaying the returned visual information and vibrating according to a vibration pattern.

[1490] (Claim 2)

[1491] 10. The system of claim 1, wherein the identifying means analyzes the audio data using a machine learning algorithm.

[1492] (Claim 3)

[1493] 2. The system of claim 1, wherein the generating means generates text and iconic visual information corresponding to the identified sounds.

[1494] (Claim 4)

[1495] 2. The system of claim 1, wherein the emotion recognition means recognizes emotions by analyzing the user's facial expressions and vocal tone. [Explanation of symbols]

[1496] 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. a recording means for collecting ambient sounds; a conversion means for converting the recorded audio data into a digital signal; compression means for compressing the converted audio data; a transmitting means for transmitting the compressed audio data to a server; an identification means in the server that analyzes the audio data and identifies a specific sound; generating means for generating visual information and vibration patterns based on the identified sounds; a return means for transmitting the generated visual information and vibration pattern to a terminal; The system includes a display means for displaying the returned visual information and vibrating according to a vibration pattern.

2. 10. The system of claim 1, wherein the identifying means analyzes the audio data using a machine learning algorithm.

3. 2. The system of claim 1, wherein said generating means generates text and iconic visual information corresponding to the identified sounds.

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