System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting voice data, a voice recognition means for converting the collected voice data into text, a natural language processing means for analyzing the text data and detecting an abnormality, a means for issuing a warning when the abnormality is detected, a means for starting recording and video recording when the abnormality is detected, and a means for notifying the police according to the seriousness of the abnormality.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] Occupations that involve contact with many people, such as train station staff and retail store staff, are at increased risk of intimidation and violence from rude people, which can lead to a decline in work motivation and an increased risk of quitting their jobs. Such a work environment has a significant impact on the safety and mental and physical health of workers. There is also concern that delayed response in emergencies could lead to serious problems. Therefore, there is a need for a system that can prevent these risks and ensure the safety and security of users. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system including a means for collecting voice data, a speech recognition means for converting the collected voice data into text, a natural language processing means for analyzing the converted text data and detecting abnormalities, a means for issuing an alarm when an abnormality is detected, a means for starting audio and video recording when an abnormality is detected, and a means for reporting the abnormality to the police depending on its severity. This allows users to detect signs of trouble or crime in real time and respond quickly. Furthermore, storing the data in cloud storage makes it easy to store and share evidence, ensuring the safety of users.

[0006] "Audio data" refers to data in which an audio signal is recorded in digital format.

[0007] A "means of collection" is a device or device function that captures audio data in real time and converts it into digital form.

[0008] A "voice recognizer" is a system that has software and algorithms for converting voice data into text form.

[0009] "Text data" is data in text format generated from voice data by voice recognition.

[0010] "Natural language processing means" refers to a system that has software and algorithms for analyzing text data and understanding, classifying, and analyzing the sentiment of that data.

[0011] "Abnormal" refers to behavior that deviates from normal communication, such as overbearing language or violent behavior.

[0012] The "means for issuing a warning when an abnormality is detected" refers to a device or a function of the device for issuing a warning by voice or text to the user and the other party when an abnormality is detected.

[0013] "Means for initiating audio and video recording" refers to a device or device function that initiates recording and saving of audio and video when an abnormality is detected.

[0014] "Means for reporting to the police" refers to a device or a function of the device for quickly contacting a public institution such as the police depending on the severity of the abnormality.

[0015] "Cloud storage" is an online storage service that allows you to store and access data over the Internet. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the present invention will be described in detail.

[0038] System Overview

[0039] This system is primarily composed of "terminals" and "servers." "Terminals" are devices owned by users (smartphones, tablets, dedicated devices, etc.), and "servers" are computers for data processing installed in a cloud environment.

[0040] Program processing explanation

[0041] Audio input and preprocessing

[0042] The device uses a microphone to collect sounds around the user and processes the audio data in real time. Specifically, the device converts the audio signal into a digital format and performs noise reduction and filtering. The audio data is then sent to a server via Wi-Fi or mobile network.

[0043] Speech recognition and text conversion

[0044] The server processes the received voice data and converts it into text using a speech recognition engine. This speech recognition engine uses deep learning technology to convert voice signals into text data with high accuracy. The resulting text data then proceeds to the next processing stage within the server.

[0045] Natural Language Processing and Anomaly Detection

[0046] The server analyzes the text data and applies natural language processing (NLP) algorithms to detect anomalies. Specifically, it performs syntactic analysis, semantic analysis, and sentiment analysis to detect aggressive language or abusive behavior. If an anomaly is detected, a notification is sent to the user's device with details about the anomaly.

[0047] Sending a warning

[0048] If an abnormality is detected, the device will issue a warning message to the user and the other party. In the case of a voice warning, the warning message is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, the message is displayed on the screen.

[0049] Start recording and recording

[0050] When an abnormality is detected, the device will automatically start recording and video recording, the video camera and microphone will be immediately turned on, and the collected data will be saved in local memory and cloud storage in real time, which can be used for later analysis or as evidence.

[0051] Report to the police

[0052] If the abnormality is deemed serious, the server automatically contacts the police, providing the user's location, current situation, and evidence (audio and video) so that the police can respond quickly.

[0053] Specific examples

[0054] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server then converts it into text using voice recognition and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0055] summary

[0056] This invention is a system that uses speech recognition and natural language processing technology to detect abnormalities in real time and ensure the safety of users. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The device uses a microphone to collect audio signals from the user's surroundings. The device then converts the audio signals into a digital format (e.g., PCM format) and processes them in real time, performing noise reduction and filtering to improve sound quality.

[0060] Step 2:

[0061] The device encodes the collected audio data and sends it to a server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[0062] Step 3:

[0063] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then stored in a buffer for further processing.

[0064] Step 4:

[0065] The server analyzes the text data using natural language processing (NLP) algorithms. Specifically, it performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior (such as overbearing language or violent expressions). The analysis results are processed in real time.

[0066] Step 5:

[0067] If the server detects an anomaly, it notifies the device of that information. The notification includes detailed information about the anomaly (for example, whether it was "aggressive language") and a recommended next action (for example, issuing a warning or starting audio or video recording).

[0068] Step 6:

[0069] Based on the notification received by the device, a warning is issued to the user and the other party. For example, in the case of a voice warning, a message such as "This conversation is being recorded, please refrain from inappropriate behavior" is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, a message is displayed on the display.

[0070] Step 7:

[0071] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected audio and video data will be uploaded to cloud storage in real time, and also temporarily stored in local memory for later analysis or as evidence.

[0072] Step 8:

[0073] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video). The police can respond quickly based on this information.

[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] In modern society, to ensure user safety, it is necessary to quickly detect abnormal speech and behavior or danger, especially in public places or environments where danger is anticipated, and to take appropriate measures. However, existing systems have difficulty performing a series of processes in real time, such as collecting voice, analyzing, detecting abnormalities, notifying, recording and filming, and reporting to the police, and are therefore unable to adequately respond to ensure user safety. To solve this problem, a new system that combines highly accurate speech recognition and natural language processing technology is needed.

[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 means including a voice recognition engine for converting voice data into text, natural language processing algorithm means for analyzing the converted text and detecting abnormalities, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to monitor the audio environment around the user in real time, quickly detect abnormal behavior or danger, and take appropriate action.

[0079] A "terminal" is a device owned by a user (such as a smartphone, tablet, or dedicated device) that collects, preprocesses, and transmits voice data.

[0080] "Audio data" refers to data obtained by converting audio signals collected around the user into digital format.

[0081] "Noise reduction" is a process that removes unnecessary noise from audio data.

[0082] "Filtering" is data processing that emphasizes or removes specific frequencies or signal components.

[0083] A "network" is a communication method, such as Wi-Fi or mobile communications, for transmitting data between a terminal and a server.

[0084] A "server" is a computer installed in the cloud for data processing, which analyzes received voice data, detects abnormalities, and takes appropriate action.

[0085] A "speech recognition engine" is software or algorithms for converting voice data into text.

[0086] A "natural language processing algorithm" is a technology for analyzing text data and understanding its content, and is used to detect anomalies.

[0087] A "user interface" is a device that includes a display screen and an operation unit that allows a user to directly operate or check something.

[0088] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[0089] "Encryption" is the process of converting data using a specific algorithm in order to send and receive the data securely.

[0090] "Recording" is the act of recording audio data.

[0091] "Recording" is the act of recording video data.

[0092] "Reporting to the police" means contacting the police when an abnormality or emergency occurs and providing the situation and evidential data.

[0093] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. An embodiment of the present invention will be described in detail below.

[0094] This system is primarily composed of terminals and servers. Terminals are devices owned by users (smartphones, tablets, dedicated devices, etc.), and servers are computers for data processing installed in a cloud environment.

[0095] Audio input and preprocessing

[0096] The device is equipped with a microphone that collects sounds around the user. This audio signal is converted into a digital format. The device then performs noise reduction and filtering to obtain clear audio data. Active noise cancellation technology is used for noise reduction.

[0097] Sending audio data

[0098] The device sends the preprocessed audio data to a server in a cloud environment via a network (Wi-Fi or mobile network). The transmitted data is securely encrypted using SSL / TLS.

[0099] Speech recognition and text conversion

[0100] The server processes the received voice data using a deep learning-based voice recognition engine and converts it into text. This voice recognition engine uses a commonly available voice recognition API (e.g., Google® Cloud Speech-to-Text API).

[0101] Natural Language Processing and Anomaly Detection

[0102] The server applies natural language processing (NLP) algorithms to the text data, performing syntactic analysis, semantic analysis, and sentiment analysis to detect anomalies. NLP algorithms use models for topic modeling and sentiment analysis (e.g., BERT, GPT-3 (registered trademark)).

[0103] Sending a warning

[0104] If an abnormality is detected, the device will issue a warning message. For voice warnings, the device's speech synthesis engine (e.g., Google Cloud Text-to-Speech) will generate the warning message and play it through the speaker. For text warnings, the message will be displayed on the device's display.

[0105] Start recording and recording

[0106] When an abnormality is detected, the device will automatically start recording and turn on the device's video camera and microphone, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time.

[0107] Report to the police

[0108] Depending on the severity of the abnormality, the server will automatically contact the police and provide the user's location information, current situation, and evidence data (audio and video), enabling a prompt response.

[0109] Specific examples

[0110] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0111] Prompt sentence for generative AI model

[0112] The system should generate a program that monitors the user's voice environment in real time and detects anomalies using speech recognition and natural language processing. Specific functions should include voice input and preprocessing, voice data transmission, speech recognition and text conversion, natural language processing and anomaly detection, issuing a warning, starting audio and video recording, and reporting to the police. Please also provide examples of specific technologies and frameworks.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1: Audio Input and Preprocessing

[0115] The device collects the audio around the user using a microphone. The input is the user's surrounding environmental sound. The collected audio signal is converted into a digital format (A / D conversion). The device's DSP (Digital Signal Processor) then processes the audio signal, performing noise reduction (active noise canceling technology) and filtering. The output is clean digital audio data.

[0116] Step 2: Sending audio data

[0117] The device sends pre-processed audio data to the server via the network (Wi-Fi or mobile network). The input is clean digital audio data. The transmitted data is encrypted by SSL / TLS. The output is the encrypted audio data reaching the server.

[0118] Step 3: Speech recognition and text conversion

[0119] The voice data received by the server is processed by a voice recognition engine (e.g., voice recognition API) using deep learning technology and converted into text data. The input is encrypted voice data. The server decrypts the voice data and inputs it into the voice recognition engine. The output is text data.

[0120] Step 4: Natural Language Processing and Anomaly Detection

[0121] The server applies natural language processing (NLP) algorithms to the text data. The input is text-converted audio data. The server performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior or coercive remarks. The output is a report of whether an anomaly exists and detailed information if one is found.

[0122] Step 5: Sending an alert

[0123] When the device receives a notification of an anomaly detection, it issues a warning message. The input is the anomaly detection notification. In the case of an audio warning, the device uses a speech synthesis engine (e.g., Google Cloud Text-to-Speech) to generate a warning message and plays it through the speaker. In the case of a text warning, the message is displayed on the device display. The output is a voice or text warning message.

[0124] Step 6: Start recording

[0125] When the device receives an anomaly detection notification, it automatically starts recording and audio. The input is the anomaly detection notification. The device turns on the video camera and microphone and saves the collected data in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time. The output is the recorded and audio data.

[0126] Step 7: Report to the police

[0127] The server assesses the severity of the anomaly and, if deemed serious, automatically contacts the police. The input is the analyzed anomaly data and related audio and video data. The server communicates with the police system through an API, providing the user's location, current situation, and evidence data. The output is a police report and the provided information.

[0128] (Application example 1)

[0129] 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."

[0130] Recent advances in voice recognition and surveillance technologies have created a demand for systems that can ensure user safety. However, existing systems often lack sufficient accuracy in anomaly detection, making it difficult to respond immediately in emergencies. Real-time notifications that take user location information into account and secure data storage in cloud storage are also required. Furthermore, there are few systems that integrate advanced voice data analysis using generative AI models with anomaly detection.

[0131] 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.

[0132] In this invention, the server includes means for collecting voice data, speech recognition means for converting the collected voice data into text, natural language processing means for analyzing the converted text data and detecting abnormalities, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for notifying the police depending on the severity of the abnormality, means for acquiring and notifying the user's location information when an abnormality occurs, means for saving data in cloud storage, means for analyzing and responding to the voice data using a generative AI model, and means for inputting prompt sentences to detect abnormalities. This enables highly accurate anomaly detection in real time and appropriate responses.

[0133] "Means for collecting audio data" refers to a device or method that collects audio around the user in real time through a microphone.

[0134] "Speech recognition means for converting collected voice data into text" refers to software or technology for converting collected voice data into digital form and converting it into text data with high accuracy.

[0135] "Natural language processing means for analyzing text data and detecting anomalies" refers to a natural language processing algorithm for analyzing text data and detecting anomalous patterns or content.

[0136] The "means for issuing a warning when an abnormality is detected" refers to a speech synthesis engine or a text message display method that issues a warning message to the user and the other party when an abnormality is detected.

[0137] The "means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically starting audio and video recording in response to a detected abnormality.

[0138] The "means for reporting to the police depending on the severity of the abnormality" is a communication means for reporting the user's location information and evidential data to the police when the abnormality is determined to be serious.

[0139] "Means for acquiring and notifying the user's location information when an abnormality occurs" refers to a mechanism for acquiring the user's location information in real time when an abnormality occurs and notifying relevant organizations and parties.

[0140] "Means for storing data in cloud storage" refers to an online storage service for safely storing collected audio and video data in a cloud environment.

[0141] "Means for analyzing voice data and responding using a generative AI model" refers to a technology that uses a generative AI model that employs deep learning technology to analyze voice data with high accuracy and generate an appropriate response.

[0142] "Means for detecting anomalies by inputting prompt sentences" refers to a method that uses text prompts that are input into a generative AI model to detect specific anomalies.

[0143] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the invention will be described.

[0144] System Configuration

[0145] This system is primarily composed of "terminals" and "servers." "Terminals" can be smartphones, tablets, or dedicated devices. "Servers" are computers for data processing installed in a cloud environment.

[0146] Hardware and Software Use Cases

[0147] Device: A smartphone with a built-in microphone, camera, and speaker.

[0148] Server: A cloud computing environment (e.g., Amazon Web Services or Google Cloud Platform).

[0149] Speech recognition engine: Google's Cloud Speech-to-Text.

[0150] Natural Language Processing (NLP) engine: Proprietary NLP models using SpaCy, NLTK, and TensorFlow.

[0151] Generative AI models: such as GPT-3 from OpenAI (registered trademark).

[0152] Cloud storage: Amazon S3 or Google Cloud Storage.

[0153] Data Processing Overview

[0154] 1. Audio input and preprocessing: The device microphone collects the user's surrounding audio in real time, performs noise reduction, and then converts the collected audio data into a digital format, which is then sent to a server via Wi-Fi or mobile network.

[0155] 2. Speech recognition and text conversion: The server processes the received audio data and converts it to text using a speech recognition engine such as Google's Cloud Speech-to-Text. The text data then proceeds to the next processing stage.

[0156] 3. Natural Language Processing and Anomaly Detection: The server analyzes the text data and applies natural language processing algorithms using SpaCy and TensorFlow. Syntax analysis, semantic analysis, and sentiment analysis are performed to detect aggressive language and abusive behavior. If an anomaly is detected, a notification is sent to the user's device with detailed information.

[0157] 4. Sending a warning: If an abnormality is detected, the device will send a warning message to the user and the other party. For voice warnings, the device will use a speech synthesis engine to generate the warning message and play it through the speaker. For text warnings, the device will show the message on the display.

[0158] 5. Start recording: When an abnormality is detected, the device will automatically start recording. The video camera and microphone will be turned on immediately, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3 or Google Cloud Storage) in real time.

[0159] 6. Report to the police: If the abnormality is deemed serious, the server will automatically contact the police, providing the user's location, current situation, and evidence data (audio and video).

[0160] Specific examples

[0161] For example, if a user is walking in a park and someone suddenly calls out, "Help!", this audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the word "help." As an abnormality is detected, the device issues an audio warning to the other party saying, "An abnormality has been detected. A warning has been issued." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0162] Prompt Sentence Examples

[0163] "Detect audio of someone around you making coercive remarks."

[0164] Make sure the word "help" is included in your voice input.

[0165] "Detect text containing violent language and report the details."

[0166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0167] Step 1:

[0168] Audio input and preprocessing

[0169] The device collects the user's surrounding sounds in real time using a microphone, converts the collected analog audio signals into a digital format, and performs noise reduction and filtering to obtain clean audio data, which is then transmitted to a server via Wi-Fi or a mobile network.

[0170] Input: Ambient analog audio signal

[0171] Output: Clean audio data in digital format

[0172] Step 2:

[0173] Speech recognition and text conversion

[0174] The server receives the voice data sent from the device and converts it into text using a speech recognition engine (such as Google's Cloud Speech-to-Text). This process utilizes a deep learning model to convert voice signals into text with high accuracy.

[0175] Input: Digital audio data

[0176] Output: Text data

[0177] Step 3:

[0178] Natural Language Processing and Anomaly Detection

[0179] The server receives the text data and analyzes it using a natural language processing (NLP) engine (for example, a model using SpaCy or TensorFlow). This analysis includes syntactic analysis, semantic analysis, and sentiment analysis. If an abnormal pattern is detected (for example, strong language or violent content), an anomaly is detected and an alert is generated.

[0180] Input: Text data

[0181] Output: Anomaly detection results (alert information)

[0182] Step 4:

[0183] Sending a warning

[0184] If an abnormality is detected, the device will issue a warning message to the user and other relevant parties. As a voice warning, the device generates a warning message using a speech synthesis engine and plays it through the speaker. As a text warning, the device displays the warning message on the screen.

[0185] Input: Anomaly detection result (alert information)

[0186] Output: Warning message (audio or text)

[0187] Step 5:

[0188] Start recording and recording

[0189] When an abnormality is detected, the device will automatically start recording and turn on the video camera and microphone, and the collected video and audio data will be saved in real time to cloud storage (e.g., Amazon S3 or Google Cloud Storage).

[0190] Input: Anomaly detection result (alert information)

[0191] Output: Audio and video data

[0192] Step 6:

[0193] Report to the police

[0194] If the abnormality is deemed serious, the server automatically notifies the police, providing them with the user's location, current situation, and collected audio and video data, allowing the police to respond quickly.

[0195] Input: Anomaly detection results (alert information) and user location information

[0196] Output: Police report (location and evidence data)

[0197] Examples of prompt statements

[0198] By feeding prompts into a generative AI model (e.g., GPT-3), the system can detect specific anomalous patterns. Here are some examples of prompts:

[0199] "Detect audio of someone around you making coercive remarks."

[0200] Make sure the word "help" is included in your voice input.

[0201] "Detect text containing violent language and report the details."

[0202] 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.

[0203] The present invention provides a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger, thereby ensuring the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[0204] System Overview

[0205] This system consists of a "terminal," a "server," and an "emotion engine." The "terminal" corresponds to a device owned by the user (smartphone, tablet, dedicated device, etc.), and the "server" is a computer for data processing installed in a cloud environment. The "emotion engine" has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[0206] Program processing explanation

[0207] Audio input and preprocessing

[0208] The device uses a microphone to collect audio from the user's surroundings, converts the audio signal into a digital format, and then uses noise reduction and filtering to make it clearer. The audio data is then encoded and sent to a server using a secure communication protocol.

[0209] Speech recognition and text conversion

[0210] The server decodes the received voice data and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice data into text data with high accuracy. The converted text data is then passed on to the next processing stage.

[0211] Natural Language Processing and Anomaly Detection

[0212] The server analyzes the text data using natural language processing (NLP) algorithms. It performs syntax analysis, semantic analysis, and sentiment analysis to detect coercive language and violent behavior. If an anomaly is detected based on the results of this analysis, detailed information is sent to the device.

[0213] Emotion recognition by emotion engine

[0214] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes voice parameters such as tone, pitch, and tempo to recognize emotions such as anger, sadness, and joy. This information is returned to the server and reflected in the anomaly detection process.

[0215] Sending a warning

[0216] If an abnormality is detected, the device will issue a warning to the user and the other party. For voice warnings, a speech synthesis engine is used to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior" and play it through the speaker. For text warnings, a message is displayed on the display. It is also possible to issue different warning messages depending on the user's emotional state using an emotion engine.

[0217] Start recording and recording

[0218] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time and temporarily stored in local memory for later analysis or as evidence.

[0219] Report to the police

[0220] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video), allowing the police to respond quickly.

[0221] Specific examples

[0222] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This speech is collected by the device and sent to the server. The server then converts it into text using a speech recognition engine, detecting the "overbearing language." At the same time, an emotion engine recognizes the user's anger. Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0223] summary

[0224] This invention is a system that combines speech recognition and natural language processing technologies, and also introduces an emotion engine to detect abnormalities in real time and ensure user safety. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[0225] The processing flow will be explained below.

[0226] Step 1:

[0227] The device uses a microphone to collect audio signals from the user's surroundings, converts them into a digital format, and performs real-time pre-amplification, noise reduction, and filtering to improve sound quality.

[0228] Step 2:

[0229] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[0230] Step 3:

[0231] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then immediately stored in a buffer for NLP analysis.

[0232] Step 4:

[0233] The server analyzes the text data using natural language processing (NLP) algorithms. Syntax analysis, semantic analysis, and sentiment analysis are performed in succession to detect abnormal behavior (such as aggressive language or violent expressions). This analysis involves analyzing the frequency of occurrence of specific keywords and phrases and context.

[0234] Step 5:

[0235] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine identifies the emotional state based on parameters such as tone, pitch, and tempo of the voice. The identified emotional state is sent to the server and reflected in the anomaly detection process.

[0236] Step 6:

[0237] If the server detects an anomaly based on the text and emotional state, it notifies the device with detailed information about the anomaly (e.g., "aggressive language" or "angry emotion") and a recommended next action (e.g., issuing a warning or starting audio or video recording).

[0238] Step 7:

[0239] Based on the notification received by the device, a warning is issued to the user and the other party. In the case of a voice warning, a voice synthesis engine is used to generate a message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," which is played through the speaker. If the emotion engine recognizes "anger," a warning with an even stronger tone is issued.

[0240] Step 8:

[0241] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time, while also being temporarily stored in local memory for later analysis or as evidence.

[0242] Step 9:

[0243] If the abnormality is deemed serious, the server automatically notifies the police. The report includes the user's location (GPS data), current situation, and evidence data (audio and video), allowing the police to respond quickly.

[0244] Example 2

[0245] 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."

[0246] Conventional technologies have difficulty monitoring the user's surroundings in real time and quickly detecting abnormal behavior or danger. They also lack the ability to understand the user's emotional state through emotion analysis and respond appropriately based on that information. Furthermore, they lack the ability to record audio or video after detecting an abnormality, making it difficult to quickly report the incident to the police.

[0247] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, means for converting the collected voice data into a digital format and performing preprocessing, means for encoding the preprocessed voice data and transmitting it to the server, means for converting the received voice data into text, means for analyzing the text data and detecting anomalies, means for analyzing emotions when anomalies are detected, means for issuing an alarm when an anomaly is detected, means for starting audio and video recording when an anomaly is detected, and means for reporting to the police depending on the severity of the anomaly. This enables real-time monitoring of the user's surrounding environment and rapid detection of abnormal speech and behavior or danger. Furthermore, emotion analysis can be used to understand the user's emotional state in detail and to take appropriate action based on that information. Furthermore, audio and video recording is automatically performed after an anomaly is detected, enabling rapid reporting to the police if necessary.

[0248] "Audio Data" means a digital representation of sound collected using a Device's microphone.

[0249] "Converting to digital form" refers to converting collected audio data into a digital signal that can be analyzed and processed.

[0250] "Preprocessing" refers to processing to improve the quality of collected audio data by performing noise reduction and filtering.

[0251] "Encoding" means converting audio data into a specific format so that it can be sent to a server via a secure communications protocol.

[0252] "Speech recognition means" refers to a system that analyzes voice data and converts the voice into text.

[0253] "Natural language processing means" refers to algorithms or programs that analyze text data and detect abnormal behavior or danger from its content.

[0254] "Means for analyzing emotions" refers to algorithms or programs that analyze the tone, pitch, tempo, etc. of a user's voice to determine the user's emotional state.

[0255] The "means for issuing a warning" is a system that issues a warning message to the user and the other party by voice or text when an abnormality is detected.

[0256] The "means for starting audio and video recording" refers to a system that automatically activates the device's microphone and camera to collect audio and video data when an abnormality is detected.

[0257] The "means for reporting to the police" is a system that automatically reports to the police depending on the severity of the abnormality and provides the user's location information and collected evidence data.

[0258] The present invention relates to a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger to ensure the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[0259] System Overview

[0260] This system consists of a terminal, a server, and an emotion engine. The terminal is a device held by the user (smartphone, tablet, dedicated device, etc.), and the server is a computer for data processing installed in a cloud environment. The emotion engine has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[0261] Hardware and software used

[0262] The hardware used includes devices such as smartphones and tablets. These devices also have built-in microphones and cameras. Servers are located in a cloud computing environment and perform data processing and analysis. The software used includes audio editing software (e.g., Audacity), speech recognition engines (e.g., Google Cloud Speech-to-Text API), natural language processing tools (e.g., Spacy, NLTK), emotion engines (e.g., IBM Watson® Tone Analyzer), and speech synthesis engines (e.g., Amazon Polly).

[0263] Audio input and preprocessing

[0264] The device uses a microphone to collect audio around the user. For example, if the user has a smartphone, the smartphone's built-in microphone is used. Since the collected audio is difficult to use as is, it is converted into a digital format and noise reduction and filtering are performed. This process may involve the use of audio input libraries such as "PulseAudio" or "ALSA." The cleared audio data is then sent to the server using a secure communication protocol (e.g., TLS / SSL).

[0265] Speech recognition and text conversion

[0266] The server decodes the voice data sent from the device and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice into text data with high accuracy. For example, if a user says "Help!" to ask for help, the speech recognition engine generates the text "Help." This text data is passed to the next stage of natural language processing.

[0267] Natural Language Processing and Anomaly Detection

[0268] The server analyzes the text data using natural language processing algorithms. Tools such as Spacy and NLTK are used for analysis, and syntactic analysis, semantic analysis, and sentiment analysis are performed. For example, if the other person uses an overbearing phrase like "What are you looking at?", the server analyzes the text and detects the overbearing phrase. This information is used for anomaly detection, and detailed information about the detected anomaly is sent to the device.

[0269] Emotion recognition by emotion engine

[0270] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes parameters such as tone, pitch, and tempo of the voice to recognize emotions such as anger, sadness, and joy. For example, if the other person is angry, the emotion of anger is recognized. This information is sent back to the server and reflected in the anomaly detection process.

[0271] Sending a warning

[0272] If an abnormality is detected, the device will issue a warning to the user and the other party. A voice warning uses a speech synthesis engine to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior." This message is played through the device's speaker. A text warning displays a message on the device's display.

[0273] Start recording and recording

[0274] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on to collect audio and video. For example, the device's camera and microphone will be activated and the audio and video from the scene will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[0275] Report to the police

[0276] If the abnormality is deemed serious, the server automatically notifies the police. The user's location information is obtained from GPS data, and an emergency call is made including the current situation and evidence data (audio and video). This allows the police to respond quickly. For example, if the server determines that the user is in danger, it immediately notifies the police and provides the necessary evidence along with the GPS location information.

[0277] Prompt Sentence Examples

[0278] Below are examples of prompt sentences that input voice data into a generative AI model to perform tasks that meet the following conditions:

[0279] The user is being monitored in real time for their current audio environment. Enter this audio data and perform a task that meets the following criteria:

[0280] 1. Convert audio data to text

[0281] 2. Analyze the converted text using natural language processing to detect abnormal behavior and dangerous language

[0282] 3. Analyze emotional states using an emotion engine

[0283] 4. If an abnormality is detected, a warning message is generated and notified to the user and the other party.

[0284] 5. If necessary, start recording and save to cloud storage.

[0285] 6. Automatically notify the police if a serious abnormality is detected

[0286] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0287] Step 1:

[0288] The device collects audio around the user using a microphone. This collected audio data is input to the device in analog format. An audio input library such as "PulseAudio" or "ALSA" is then used to convert this analog audio data into digital format. The result of this conversion process is digital audio data output.

[0289] Step 2:

[0290] The device preprocesses the digital audio data. Specifically, it uses software such as Audacity or Waves Noise Reduction to reduce noise and perform filtering. This preprocessing improves the quality of the audio data, making it easier to analyze. After preprocessing, the audio data is output as clear audio.

[0291] Step 3:

[0292] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., TLS / SSL). This encoding process converts the audio data into a format that can be transferred efficiently and securely. The encoded audio data is then sent to the server.

[0293] Step 4:

[0294] The server decodes the encoded audio data it receives. The result of the decoding process is the original digital audio data. The reconstructed audio data is output.

[0295] Step 5:

[0296] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to convert the decoded voice data into text. This process uses deep learning technology to convert voice into text with high accuracy. For example, if the voice input is "Help!", the text data "Help" is output.

[0297] Step 6:

[0298] The server analyzes the text data using natural language processing algorithms (e.g., Spacy, NLTK). The analysis includes syntactic analysis, semantic analysis, and sentiment analysis. For example, if text data containing the overbearing phrase "What are you looking at!" is input, it will detect this as dangerous behavior. The analysis results will output information indicating that abnormal behavior has been detected.

[0299] Step 7:

[0300] The device uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from voice data in real time. The emotion engine analyzes parameters such as voice tone, pitch, and tempo to identify emotions such as anger, sadness, and joy. For example, if anger is detected, the emotion information for "anger" is output and sent to the server.

[0301] Step 8:

[0302] If the device detects an abnormality, it will issue a warning to the user and the other party. It uses a speech synthesis engine (e.g., Amazon Polly) to generate a voice warning message such as, "This conversation is being recorded. Please refrain from inappropriate behavior." The generated voice message is played from the device's speaker. At the same time, a text message is also displayed on the display.

[0303] Step 9:

[0304] If the device detects an abnormality, it will automatically start recording. At this time, the video camera and microphone will immediately turn on to collect audio and video from the scene. The data obtained through recording will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[0305] Step 10:

[0306] The server will then notify the police depending on the severity of the anomaly. The user's location is retrieved from GPS data, and an emergency call is sent, including audio and video evidence, allowing the police to respond quickly.

[0307] Through the above processing steps, this system is able to monitor the user's surrounding environment in real time, quickly and accurately detect abnormal behavior or danger, and take appropriate action.

[0308] (Application example 2)

[0309] 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."

[0310] In modern brick-and-mortar stores, troubles and dangerous situations can arise between customers and staff, or between customers themselves. If such situations escalate, they can not only disrupt store operations but also pose significant risks to both parties. Conventional surveillance cameras and alarm systems have difficulty detecting signs of trouble in advance and lack the means to respond immediately. Therefore, there is a need for a system that can detect abnormal behavior and heightened emotions in real time and take appropriate measures.

[0311] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, voice recognition means for converting the collected voice data into text, natural language processing means for analyzing the text data and detecting abnormalities, emotion recognition means for analyzing the text data and recognizing the user's emotional state, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for saving the collected voice data and video recording data in a cloud environment, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to detect abnormal behavior or dangerous situations in the store in real time and immediately issue an alert to prevent trouble and secure necessary evidence data.

[0312] "Audio data" refers to data in which sounds present around the user are recorded in digital format.

[0313] "Speech recognition means" refers to the technology or device used to convert collected voice data into text.

[0314] "Natural language processing tools" refer to techniques and algorithms for analyzing text data and extracting grammatical and semantic information.

[0315] "Emotion recognition means" refers to technologies and algorithms for analyzing voice data and text data to recognize a user's emotional state.

[0316] "Means for issuing a warning when an abnormality is detected" refers to a system or function for notifying users or other parties of a warning in real time.

[0317] "Means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically recording audio and video when an abnormality occurs.

[0318] "Means of storing data in a cloud environment" refers to the technology and systems used to store collected data on a remote server via the Internet.

[0319] "Means for reporting to the police" refers to a system or function that automatically reports to the police in an emergency depending on the severity of the abnormality.

[0320] This invention provides a system that detects abnormal behavior or danger in real time during interactions between customers and staff, or between customers themselves, in a brick-and-mortar store, and responds appropriately. This system detects abnormalities by collecting and analyzing voice data and performing emotion recognition, issues a warning, starts recording or filming as necessary, stores the data in a cloud environment, and can even notify the police in some cases.

[0321] Program processing overview

[0322] The system uses the following main hardware and software:

[0323] Hardware:

[0324] Smartphone (microphone, camera)

[0325] Cloud Server

[0326] Smart device (if needed)

[0327] software:

[0328] Speech recognition engine (e.g., Google Cloud Speech-to-Text API, Amazon Transcribe)

[0329] Natural language processing engines (e.g., Google Cloud Natural Language API, spaCy)

[0330] Emotion recognition engine (e.g., IBM Watson Tone Analyzer, Microsoft® Emotion API)

[0331] Real-time communication protocols (e.g. web sockets, MQTT)

[0332] The server first collects voice data from within the physical store using the smartphone's microphone. The collected voice data is sent to the cloud server in real time. The cloud server then converts the voice data into text using a voice recognition engine. Next, it uses natural language processing to analyze the text data and extract grammatical and semantic information. It also uses an emotion recognition engine to evaluate the user's emotional state from the text and voice data and detect abnormalities. If an abnormality is detected, the server issues a warning to the user and the other party in real time, starts audio and video recording as necessary, and saves the data in cloud storage. It can also automatically notify the police depending on the severity of the abnormality.

[0333] Specific examples

[0334] For example, consider the case where one afternoon in a brick-and-mortar store, Customer A yells at Staff B, "Why are you treating me like that?" The customer's voice is collected by the smartphone's microphone and sent to a cloud server. A speech recognition engine converts the speech into text, and natural language processing detects overbearing language. At the same time, an emotion recognition engine recognizes Customer A's anger. This triggers the system to issue a warning message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," and begin recording and filming. This data is then stored in cloud storage, and in some cases, the police may be notified.

[0335] Prompt Sentence Examples

[0336] Please explain your system for detecting abnormal speech and behavior or danger. This system is used in physical stores to prevent trouble between customers and staff, or between customers. It combines speech recognition, natural language processing, and emotion recognition to detect abnormalities and, if necessary, issue a warning or record audio or video. Please also provide detailed descriptions of the application you have created and the hardware and software you use.

[0337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0338] Step 1:

[0339] The device collects the user's surrounding sounds using a microphone, converts the collected audio data into a digital format, and applies a noise-canceling filter. The input is the surrounding audio, and the output is the noise-removed digital audio data.

[0340] Step 2:

[0341] The device encodes the noise-removed digital audio data and sends it to the cloud server using a secure communication protocol (HTTPS / WebSocket).The input is the noise-removed digital audio data, and the output is the encoded audio data sent to the cloud server.

[0342] Step 3:

[0343] The server decodes the received encoded voice data and converts it into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API). The input is the encoded voice data, and the output is the converted text data.

[0344] Step 4:

[0345] The server analyzes the text data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API, spaCy), extracts grammatical and semantic information, and detects anomalies. The input is the text data, and the output is the analyzed text data and the results of anomaly detection.

[0346] Step 5:

[0347] The server analyzes the analyzed text data using an emotion recognition engine (e.g., IBM Watson Tone Analyzer, Microsoft Emotion API) to evaluate the user's emotional state. The input is the analyzed text data, and the output is the evaluation result of the user's emotional state.

[0348] Step 6:

[0349] If an anomaly is detected, the server sends a real-time warning to the device. It generates a warning message and displays it on the device's display or issues an audio warning through the speaker. The input is the result of anomaly detection and emotion evaluation, and the output is a warning notification.

[0350] Step 7:

[0351] The terminal automatically starts recording audio and video when an abnormality is detected. The input is a warning notification, and the output is audio and video data.

[0352] Step 8:

[0353] The device uploads the collected audio and video data to cloud storage and temporarily stores it in local memory as needed. The input is the audio and video data, and the output is the data stored in cloud storage.

[0354] Step 9:

[0355] The server automatically generates and sends an emergency call to the police, including the user's location information and collected evidence data (audio and video). The input is the result of anomaly detection and emotion evaluation, as well as location information. The output is the emergency call sent to the police.

[0356] 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.

[0357] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0358] 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.

[0359] [Second embodiment]

[0360] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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).

[0366] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0371] 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."

[0372] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the present invention will be described in detail.

[0373] System Overview

[0374] This system is primarily composed of "terminals" and "servers." "Terminals" are devices owned by users (smartphones, tablets, dedicated devices, etc.), and "servers" are computers for data processing installed in a cloud environment.

[0375] Program processing explanation

[0376] Audio input and preprocessing

[0377] The device uses a microphone to collect sounds around the user and processes the audio data in real time. Specifically, the device converts the audio signal into a digital format and performs noise reduction and filtering. The audio data is then sent to a server via Wi-Fi or mobile network.

[0378] Speech recognition and text conversion

[0379] The server processes the received voice data and converts it into text using a speech recognition engine. This speech recognition engine uses deep learning technology to convert voice signals into text data with high accuracy. The resulting text data then proceeds to the next processing stage within the server.

[0380] Natural Language Processing and Anomaly Detection

[0381] The server analyzes the text data and applies natural language processing (NLP) algorithms to detect anomalies. Specifically, it performs syntactic analysis, semantic analysis, and sentiment analysis to detect aggressive language or abusive behavior. If an anomaly is detected, a notification is sent to the user's device with details about the anomaly.

[0382] Sending a warning

[0383] If an abnormality is detected, the device will issue a warning message to the user and the other party. In the case of a voice warning, the warning message is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, the message is displayed on the screen.

[0384] Start recording and recording

[0385] When an abnormality is detected, the device will automatically start recording and video recording, the video camera and microphone will be immediately turned on, and the collected data will be saved in local memory and cloud storage in real time, which can be used for later analysis or as evidence.

[0386] Report to the police

[0387] If the abnormality is deemed serious, the server automatically contacts the police, providing the user's location, current situation, and evidence (audio and video) so that the police can respond quickly.

[0388] Specific examples

[0389] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server then converts it into text using voice recognition and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0390] summary

[0391] This invention is a system that uses speech recognition and natural language processing technology to detect abnormalities in real time and ensure the safety of users. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[0392] The processing flow will be explained below.

[0393] Step 1:

[0394] The device uses a microphone to collect audio signals from the user's surroundings. The device then converts the audio signals into a digital format (e.g., PCM format) and processes them in real time, performing noise reduction and filtering to improve sound quality.

[0395] Step 2:

[0396] The device encodes the collected audio data and sends it to a server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[0397] Step 3:

[0398] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then stored in a buffer for further processing.

[0399] Step 4:

[0400] The server analyzes the text data using natural language processing (NLP) algorithms. Specifically, it performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior (such as overbearing language or violent expressions). The analysis results are processed in real time.

[0401] Step 5:

[0402] If the server detects an anomaly, it notifies the device of that information. The notification includes detailed information about the anomaly (for example, whether it was "aggressive language") and a recommended next action (for example, issuing a warning or starting audio or video recording).

[0403] Step 6:

[0404] Based on the notification received by the device, a warning is issued to the user and the other party. For example, in the case of a voice warning, a message such as "This conversation is being recorded, please refrain from inappropriate behavior" is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, a message is displayed on the display.

[0405] Step 7:

[0406] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected audio and video data will be uploaded to cloud storage in real time, and also temporarily stored in local memory for later analysis or as evidence.

[0407] Step 8:

[0408] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video). The police can respond quickly based on this information.

[0409] Example 1

[0410] 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."

[0411] In modern society, to ensure user safety, it is necessary to quickly detect abnormal speech and behavior or danger, especially in public places or environments where danger is anticipated, and to take appropriate measures. However, existing systems have difficulty performing a series of processes in real time, such as collecting voice, analyzing, detecting abnormalities, notifying, recording and filming, and reporting to the police, and are therefore unable to adequately respond to ensure user safety. To solve this problem, a new system that combines highly accurate speech recognition and natural language processing technology is needed.

[0412] 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.

[0413] In this invention, the server includes means including a voice recognition engine for converting voice data into text, natural language processing algorithm means for analyzing the converted text and detecting abnormalities, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to monitor the audio environment around the user in real time, quickly detect abnormal behavior or danger, and take appropriate action.

[0414] A "terminal" is a device owned by a user (such as a smartphone, tablet, or dedicated device) that collects, preprocesses, and transmits voice data.

[0415] "Audio data" refers to data obtained by converting audio signals collected around the user into digital format.

[0416] "Noise reduction" is a process that removes unnecessary noise from audio data.

[0417] "Filtering" is data processing that emphasizes or removes specific frequencies or signal components.

[0418] A "network" is a communication method, such as Wi-Fi or mobile communications, for transmitting data between a terminal and a server.

[0419] A "server" is a computer installed in the cloud for data processing, which analyzes received voice data, detects abnormalities, and takes appropriate action.

[0420] A "speech recognition engine" is software or algorithms for converting voice data into text.

[0421] A "natural language processing algorithm" is a technology for analyzing text data and understanding its content, and is used to detect anomalies.

[0422] A "user interface" is a device that includes a display screen and an operation unit that allows a user to directly operate or check something.

[0423] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[0424] "Encryption" is the process of converting data using a specific algorithm in order to send and receive the data securely.

[0425] "Recording" is the act of recording audio data.

[0426] "Recording" is the act of recording video data.

[0427] "Reporting to the police" means contacting the police when an abnormality or emergency occurs and providing the situation and evidential data.

[0428] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. An embodiment of the present invention will be described in detail below.

[0429] This system is primarily composed of terminals and servers. Terminals are devices owned by users (smartphones, tablets, dedicated devices, etc.), and servers are computers for data processing installed in a cloud environment.

[0430] Audio input and preprocessing

[0431] The device is equipped with a microphone that collects sounds around the user. This audio signal is converted into a digital format. The device then performs noise reduction and filtering to obtain clear audio data. Active noise cancellation technology is used for noise reduction.

[0432] Sending audio data

[0433] The device sends the preprocessed audio data to a server in a cloud environment via a network (Wi-Fi or mobile network). The transmitted data is securely encrypted using SSL / TLS.

[0434] Speech recognition and text conversion

[0435] The server processes the received voice data using a deep learning-based speech recognition engine and converts it into text. This speech recognition engine uses a commonly available speech recognition API (e.g., Google Cloud Speech-to-Text API).

[0436] Natural Language Processing and Anomaly Detection

[0437] The server applies natural language processing (NLP) algorithms to the text data, performing syntactic analysis, semantic analysis, and sentiment analysis to detect anomalies. NLP algorithms use models for topic modeling and sentiment analysis (e.g., BERT, GPT-3).

[0438] Sending a warning

[0439] If an abnormality is detected, the device will issue a warning message. For voice warnings, the device's speech synthesis engine (e.g., Google Cloud Text-to-Speech) will generate the warning message and play it through the speaker. For text warnings, the message will be displayed on the device's display.

[0440] Start recording and recording

[0441] When an abnormality is detected, the device will automatically start recording and turn on the device's video camera and microphone, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time.

[0442] Report to the police

[0443] Depending on the severity of the abnormality, the server will automatically contact the police and provide the user's location information, current situation, and evidence data (audio and video), enabling a prompt response.

[0444] Specific examples

[0445] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0446] Prompt sentence for generative AI model

[0447] The system should generate a program that monitors the user's voice environment in real time and detects anomalies using speech recognition and natural language processing. Specific functions should include voice input and preprocessing, voice data transmission, speech recognition and text conversion, natural language processing and anomaly detection, issuing a warning, starting audio and video recording, and reporting to the police. Please also provide examples of specific technologies and frameworks.

[0448] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0449] Step 1: Audio Input and Preprocessing

[0450] The device collects the audio around the user using a microphone. The input is the user's surrounding environmental sound. The collected audio signal is converted into a digital format (A / D conversion). The device's DSP (Digital Signal Processor) then processes the audio signal, performing noise reduction (active noise canceling technology) and filtering. The output is clean digital audio data.

[0451] Step 2: Sending audio data

[0452] The device sends pre-processed audio data to the server via the network (Wi-Fi or mobile network). The input is clean digital audio data. The transmitted data is encrypted by SSL / TLS. The output is the encrypted audio data reaching the server.

[0453] Step 3: Speech recognition and text conversion

[0454] The voice data received by the server is processed by a voice recognition engine (e.g., voice recognition API) using deep learning technology and converted into text data. The input is encrypted voice data. The server decrypts the voice data and inputs it into the voice recognition engine. The output is text data.

[0455] Step 4: Natural Language Processing and Anomaly Detection

[0456] The server applies natural language processing (NLP) algorithms to the text data. The input is text-converted audio data. The server performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior or coercive remarks. The output is a report of whether an anomaly exists and detailed information if one is found.

[0457] Step 5: Sending an alert

[0458] When the device receives a notification of an anomaly detection, it issues a warning message. The input is the anomaly detection notification. In the case of an audio warning, the device uses a speech synthesis engine (e.g., Google Cloud Text-to-Speech) to generate a warning message and plays it through the speaker. In the case of a text warning, the message is displayed on the device display. The output is a voice or text warning message.

[0459] Step 6: Start recording

[0460] When the device receives an anomaly detection notification, it automatically starts recording and audio. The input is the anomaly detection notification. The device turns on the video camera and microphone and saves the collected data in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time. The output is the recorded and audio data.

[0461] Step 7: Report to the police

[0462] The server assesses the severity of the anomaly and, if deemed serious, automatically contacts the police. The input is the analyzed anomaly data and related audio and video data. The server communicates with the police system through an API, providing the user's location, current situation, and evidence data. The output is a police report and the provided information.

[0463] (Application example 1)

[0464] 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."

[0465] Recent advances in voice recognition and surveillance technologies have created a demand for systems that can ensure user safety. However, existing systems often lack sufficient accuracy in anomaly detection, making it difficult to respond immediately in emergencies. Real-time notifications that take user location information into account and secure data storage in cloud storage are also required. Furthermore, there are few systems that integrate advanced voice data analysis using generative AI models with anomaly detection.

[0466] 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.

[0467] In this invention, the server includes means for collecting voice data, speech recognition means for converting the collected voice data into text, natural language processing means for analyzing the converted text data and detecting abnormalities, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for notifying the police depending on the severity of the abnormality, means for acquiring and notifying the user's location information when an abnormality occurs, means for saving data in cloud storage, means for analyzing and responding to the voice data using a generative AI model, and means for inputting prompt sentences to detect abnormalities. This enables highly accurate anomaly detection in real time and appropriate responses.

[0468] "Means for collecting audio data" refers to a device or method that collects audio around the user in real time through a microphone.

[0469] "Speech recognition means for converting collected voice data into text" refers to software or technology for converting collected voice data into digital form and converting it into text data with high accuracy.

[0470] "Natural language processing means for analyzing text data and detecting anomalies" refers to a natural language processing algorithm for analyzing text data and detecting anomalous patterns or content.

[0471] The "means for issuing a warning when an abnormality is detected" refers to a speech synthesis engine or a text message display method that issues a warning message to the user and the other party when an abnormality is detected.

[0472] The "means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically starting audio and video recording in response to a detected abnormality.

[0473] The "means for reporting to the police depending on the severity of the abnormality" is a communication means for reporting the user's location information and evidential data to the police when the abnormality is determined to be serious.

[0474] "Means for acquiring and notifying the user's location information when an abnormality occurs" refers to a mechanism for acquiring the user's location information in real time when an abnormality occurs and notifying relevant organizations and parties.

[0475] "Means for storing data in cloud storage" refers to an online storage service for safely storing collected audio and video data in a cloud environment.

[0476] "Means for analyzing voice data and responding using a generative AI model" refers to a technology that uses a generative AI model that employs deep learning technology to analyze voice data with high accuracy and generate an appropriate response.

[0477] "Means for detecting anomalies by inputting prompt sentences" refers to a method that uses text prompts that are input into a generative AI model to detect specific anomalies.

[0478] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the invention will be described.

[0479] System Configuration

[0480] This system is primarily composed of "terminals" and "servers." "Terminals" can be smartphones, tablets, or dedicated devices. "Servers" are computers for data processing installed in a cloud environment.

[0481] Hardware and Software Use Cases

[0482] Device: A smartphone with a built-in microphone, camera, and speaker.

[0483] Server: A cloud computing environment (e.g., Amazon Web Services or Google Cloud Platform).

[0484] Speech recognition engine: Google's Cloud Speech-to-Text.

[0485] Natural Language Processing (NLP) engine: Proprietary NLP models using SpaCy, NLTK, and TensorFlow.

[0486] Generative AI models: such as OpenAI's GPT-3.

[0487] Cloud storage: Amazon S3 or Google Cloud Storage.

[0488] Data Processing Overview

[0489] 1. Audio input and preprocessing: The device microphone collects the user's surrounding audio in real time, performs noise reduction, and then converts the collected audio data into a digital format, which is then sent to a server via Wi-Fi or mobile network.

[0490] 2. Speech recognition and text conversion: The server processes the received audio data and converts it to text using a speech recognition engine such as Google's Cloud Speech-to-Text. The text data then proceeds to the next processing stage.

[0491] 3. Natural Language Processing and Anomaly Detection: The server analyzes the text data and applies natural language processing algorithms using SpaCy and TensorFlow. Syntax analysis, semantic analysis, and sentiment analysis are performed to detect aggressive language and abusive behavior. If an anomaly is detected, a notification is sent to the user's device with detailed information.

[0492] 4. Sending a warning: If an abnormality is detected, the device will send a warning message to the user and the other party. For voice warnings, the device will use a speech synthesis engine to generate the warning message and play it through the speaker. For text warnings, the device will show the message on the display.

[0493] 5. Start recording: When an abnormality is detected, the device will automatically start recording. The video camera and microphone will be turned on immediately, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3 or Google Cloud Storage) in real time.

[0494] 6. Report to the police: If the abnormality is deemed serious, the server will automatically contact the police, providing the user's location, current situation, and evidence data (audio and video).

[0495] Specific examples

[0496] For example, if a user is walking in a park and someone suddenly calls out, "Help!", this audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the word "help." As an abnormality is detected, the device issues an audio warning to the other party saying, "An abnormality has been detected. A warning has been issued." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0497] Prompt Sentence Examples

[0498] "Detect audio of someone around you making coercive remarks."

[0499] Make sure the word "help" is included in your voice input.

[0500] "Detect text containing violent language and report the details."

[0501] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0502] Step 1:

[0503] Audio input and preprocessing

[0504] The device collects the user's surrounding sounds in real time using a microphone, converts the collected analog audio signals into a digital format, and performs noise reduction and filtering to obtain clean audio data, which is then transmitted to a server via Wi-Fi or a mobile network.

[0505] Input: Ambient analog audio signal

[0506] Output: Clean audio data in digital format

[0507] Step 2:

[0508] Speech recognition and text conversion

[0509] The server receives the voice data sent from the device and converts it into text using a speech recognition engine (such as Google's Cloud Speech-to-Text). This process utilizes a deep learning model to convert voice signals into text with high accuracy.

[0510] Input: Digital audio data

[0511] Output: Text data

[0512] Step 3:

[0513] Natural Language Processing and Anomaly Detection

[0514] The server receives the text data and analyzes it using a natural language processing (NLP) engine (for example, a model using SpaCy or TensorFlow). This analysis includes syntactic analysis, semantic analysis, and sentiment analysis. If an abnormal pattern is detected (for example, strong language or violent content), an anomaly is detected and an alert is generated.

[0515] Input: Text data

[0516] Output: Anomaly detection results (alert information)

[0517] Step 4:

[0518] Sending a warning

[0519] If an abnormality is detected, the device will issue a warning message to the user and other relevant parties. As a voice warning, the device generates a warning message using a speech synthesis engine and plays it through the speaker. As a text warning, the device displays the warning message on the screen.

[0520] Input: Anomaly detection result (alert information)

[0521] Output: Warning message (audio or text)

[0522] Step 5:

[0523] Start recording and recording

[0524] When an abnormality is detected, the device will automatically start recording and turn on the video camera and microphone, and the collected video and audio data will be saved in real time to cloud storage (e.g., Amazon S3 or Google Cloud Storage).

[0525] Input: Anomaly detection result (alert information)

[0526] Output: Audio and video data

[0527] Step 6:

[0528] Report to the police

[0529] If the abnormality is deemed serious, the server automatically notifies the police, providing them with the user's location, current situation, and collected audio and video data, allowing the police to respond quickly.

[0530] Input: Anomaly detection results (alert information) and user location information

[0531] Output: Police report (location and evidence data)

[0532] Examples of prompt statements

[0533] By feeding prompts into a generative AI model (e.g., GPT-3), the system can detect specific anomalous patterns. Here are some examples of prompts:

[0534] "Detect audio of someone around you making coercive remarks."

[0535] Make sure the word "help" is included in your voice input.

[0536] "Detect text containing violent language and report the details."

[0537] 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.

[0538] The present invention provides a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger, thereby ensuring the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[0539] System Overview

[0540] This system consists of a "terminal," a "server," and an "emotion engine." The "terminal" corresponds to a device owned by the user (smartphone, tablet, dedicated device, etc.), and the "server" is a computer for data processing installed in a cloud environment. The "emotion engine" has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[0541] Program processing explanation

[0542] Audio input and preprocessing

[0543] The device uses a microphone to collect audio from the user's surroundings, converts the audio signal into a digital format, and then uses noise reduction and filtering to make it clearer. The audio data is then encoded and sent to a server using a secure communication protocol.

[0544] Speech recognition and text conversion

[0545] The server decodes the received voice data and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice data into text data with high accuracy. The converted text data is then passed on to the next processing stage.

[0546] Natural Language Processing and Anomaly Detection

[0547] The server analyzes the text data using natural language processing (NLP) algorithms. It performs syntax analysis, semantic analysis, and sentiment analysis to detect coercive language and violent behavior. If an anomaly is detected based on the results of this analysis, detailed information is sent to the device.

[0548] Emotion recognition by emotion engine

[0549] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes voice parameters such as tone, pitch, and tempo to recognize emotions such as anger, sadness, and joy. This information is returned to the server and reflected in the anomaly detection process.

[0550] Sending a warning

[0551] If an abnormality is detected, the device will issue a warning to the user and the other party. For voice warnings, a speech synthesis engine is used to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior" and play it through the speaker. For text warnings, a message is displayed on the display. It is also possible to issue different warning messages depending on the user's emotional state using an emotion engine.

[0552] Start recording and recording

[0553] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time and temporarily stored in local memory for later analysis or as evidence.

[0554] Report to the police

[0555] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video), allowing the police to respond quickly.

[0556] Specific examples

[0557] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This speech is collected by the device and sent to the server. The server then converts it into text using a speech recognition engine, detecting the "overbearing language." At the same time, an emotion engine recognizes the user's anger. Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0558] summary

[0559] This invention is a system that combines speech recognition and natural language processing technologies, and also introduces an emotion engine to detect abnormalities in real time and ensure user safety. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] The device uses a microphone to collect audio signals from the user's surroundings, converts them into a digital format, and performs real-time pre-amplification, noise reduction, and filtering to improve sound quality.

[0563] Step 2:

[0564] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[0565] Step 3:

[0566] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then immediately stored in a buffer for NLP analysis.

[0567] Step 4:

[0568] The server analyzes the text data using natural language processing (NLP) algorithms. Syntax analysis, semantic analysis, and sentiment analysis are performed in succession to detect abnormal behavior (such as aggressive language or violent expressions). This analysis involves analyzing the frequency of occurrence of specific keywords and phrases and context.

[0569] Step 5:

[0570] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine identifies the emotional state based on parameters such as tone, pitch, and tempo of the voice. The identified emotional state is sent to the server and reflected in the anomaly detection process.

[0571] Step 6:

[0572] If the server detects an anomaly based on the text and emotional state, it notifies the device with detailed information about the anomaly (e.g., "aggressive language" or "angry emotion") and a recommended next action (e.g., issuing a warning or starting audio or video recording).

[0573] Step 7:

[0574] Based on the notification received by the device, a warning is issued to the user and the other party. In the case of a voice warning, a voice synthesis engine is used to generate a message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," which is played through the speaker. If the emotion engine recognizes "anger," a warning with an even stronger tone is issued.

[0575] Step 8:

[0576] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time, while also being temporarily stored in local memory for later analysis or as evidence.

[0577] Step 9:

[0578] If the abnormality is deemed serious, the server automatically notifies the police. The report includes the user's location (GPS data), current situation, and evidence data (audio and video), allowing the police to respond quickly.

[0579] Example 2

[0580] 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."

[0581] Conventional technologies have difficulty monitoring the user's surroundings in real time and quickly detecting abnormal behavior or danger. They also lack the ability to understand the user's emotional state through emotion analysis and respond appropriately based on that information. Furthermore, they lack the ability to record audio or video after detecting an abnormality, making it difficult to quickly report the incident to the police.

[0582] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, means for converting the collected voice data into a digital format and performing preprocessing, means for encoding the preprocessed voice data and transmitting it to the server, means for converting the received voice data into text, means for analyzing the text data and detecting anomalies, means for analyzing emotions when anomalies are detected, means for issuing an alarm when an anomaly is detected, means for starting audio and video recording when an anomaly is detected, and means for reporting to the police depending on the severity of the anomaly. This enables real-time monitoring of the user's surrounding environment and rapid detection of abnormal speech and behavior or danger. Furthermore, emotion analysis can be used to understand the user's emotional state in detail and to take appropriate action based on that information. Furthermore, audio and video recording is automatically performed after an anomaly is detected, enabling rapid reporting to the police if necessary.

[0583] "Audio Data" means a digital representation of sound collected using a Device's microphone.

[0584] "Converting to digital form" refers to converting collected audio data into a digital signal that can be analyzed and processed.

[0585] "Preprocessing" refers to processing to improve the quality of collected audio data by performing noise reduction and filtering.

[0586] "Encoding" means converting audio data into a specific format so that it can be sent to a server via a secure communications protocol.

[0587] "Speech recognition means" refers to a system that analyzes voice data and converts the voice into text.

[0588] "Natural language processing means" refers to algorithms or programs that analyze text data and detect abnormal behavior or danger from its content.

[0589] "Means for analyzing emotions" refers to algorithms or programs that analyze the tone, pitch, tempo, etc. of a user's voice to determine the user's emotional state.

[0590] The "means for issuing a warning" is a system that issues a warning message to the user and the other party by voice or text when an abnormality is detected.

[0591] The "means for starting audio and video recording" refers to a system that automatically activates the device's microphone and camera to collect audio and video data when an abnormality is detected.

[0592] The "means for reporting to the police" is a system that automatically reports to the police depending on the severity of the abnormality and provides the user's location information and collected evidence data.

[0593] The present invention relates to a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger to ensure the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[0594] System Overview

[0595] This system consists of a terminal, a server, and an emotion engine. The terminal is a device held by the user (smartphone, tablet, dedicated device, etc.), and the server is a computer for data processing installed in a cloud environment. The emotion engine has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[0596] Hardware and software used

[0597] The hardware used includes devices such as smartphones and tablets. These devices also have built-in microphones and cameras. Servers are located in a cloud computing environment and perform data processing and analysis. The software used includes audio editing software (e.g., Audacity), speech recognition engines (e.g., Google Cloud Speech-to-Text API), natural language processing tools (e.g., Spacy, NLTK), emotion engines (e.g., IBM Watson Tone Analyzer), and speech synthesis engines (e.g., Amazon Polly).

[0598] Audio input and preprocessing

[0599] The device uses a microphone to collect audio around the user. For example, if the user has a smartphone, the smartphone's built-in microphone is used. Since the collected audio is difficult to use as is, it is converted into a digital format and noise reduction and filtering are performed. This process may involve the use of audio input libraries such as "PulseAudio" or "ALSA." The cleared audio data is then sent to the server using a secure communication protocol (e.g., TLS / SSL).

[0600] Speech recognition and text conversion

[0601] The server decodes the voice data sent from the device and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice into text data with high accuracy. For example, if a user says "Help!" to ask for help, the speech recognition engine generates the text "Help." This text data is passed to the next stage of natural language processing.

[0602] Natural Language Processing and Anomaly Detection

[0603] The server analyzes the text data using natural language processing algorithms. Tools such as Spacy and NLTK are used for analysis, and syntactic analysis, semantic analysis, and sentiment analysis are performed. For example, if the other person uses an overbearing phrase like "What are you looking at?", the server analyzes the text and detects the overbearing phrase. This information is used for anomaly detection, and detailed information about the detected anomaly is sent to the device.

[0604] Emotion recognition by emotion engine

[0605] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes parameters such as tone, pitch, and tempo of the voice to recognize emotions such as anger, sadness, and joy. For example, if the other person is angry, the emotion of anger is recognized. This information is sent back to the server and reflected in the anomaly detection process.

[0606] Sending a warning

[0607] If an abnormality is detected, the device will issue a warning to the user and the other party. A voice warning uses a speech synthesis engine to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior." This message is played through the device's speaker. A text warning displays a message on the device's display.

[0608] Start recording and recording

[0609] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on to collect audio and video. For example, the device's camera and microphone will be activated and the audio and video from the scene will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[0610] Report to the police

[0611] If the abnormality is deemed serious, the server automatically notifies the police. The user's location information is obtained from GPS data, and an emergency call is made including the current situation and evidence data (audio and video). This allows the police to respond quickly. For example, if the server determines that the user is in danger, it immediately notifies the police and provides the necessary evidence along with the GPS location information.

[0612] Prompt Sentence Examples

[0613] Below are examples of prompt sentences that input voice data into a generative AI model to perform tasks that meet the following conditions:

[0614] The user is being monitored in real time for their current audio environment. Enter this audio data and perform a task that meets the following criteria:

[0615] 1. Convert audio data to text

[0616] 2. Analyze the converted text using natural language processing to detect abnormal behavior and dangerous language

[0617] 3. Analyze emotional states using an emotion engine

[0618] 4. If an abnormality is detected, a warning message is generated and notified to the user and the other party.

[0619] 5. If necessary, start recording and save to cloud storage.

[0620] 6. Automatically notify the police if a serious abnormality is detected

[0621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0622] Step 1:

[0623] The device collects audio around the user using a microphone. This collected audio data is input to the device in analog format. An audio input library such as "PulseAudio" or "ALSA" is then used to convert this analog audio data into digital format. The result of this conversion process is digital audio data output.

[0624] Step 2:

[0625] The device preprocesses the digital audio data. Specifically, it uses software such as Audacity or Waves Noise Reduction to reduce noise and perform filtering. This preprocessing improves the quality of the audio data, making it easier to analyze. After preprocessing, the audio data is output as clear audio.

[0626] Step 3:

[0627] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., TLS / SSL). This encoding process converts the audio data into a format that can be transferred efficiently and securely. The encoded audio data is then sent to the server.

[0628] Step 4:

[0629] The server decodes the encoded audio data it receives. The result of the decoding process is the original digital audio data. The reconstructed audio data is output.

[0630] Step 5:

[0631] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to convert the decoded voice data into text. This process uses deep learning technology to convert voice into text with high accuracy. For example, if the voice input is "Help!", the text data "Help" is output.

[0632] Step 6:

[0633] The server analyzes the text data using natural language processing algorithms (e.g., Spacy, NLTK). The analysis includes syntactic analysis, semantic analysis, and sentiment analysis. For example, if text data containing the overbearing phrase "What are you looking at!" is input, it will detect this as dangerous behavior. The analysis results will output information indicating that abnormal behavior has been detected.

[0634] Step 7:

[0635] The device uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from voice data in real time. The emotion engine analyzes parameters such as voice tone, pitch, and tempo to identify emotions such as anger, sadness, and joy. For example, if anger is detected, the emotion information for "anger" is output and sent to the server.

[0636] Step 8:

[0637] If the device detects an abnormality, it will issue a warning to the user and the other party. It uses a speech synthesis engine (e.g., Amazon Polly) to generate a voice warning message such as, "This conversation is being recorded. Please refrain from inappropriate behavior." The generated voice message is played from the device's speaker. At the same time, a text message is also displayed on the display.

[0638] Step 9:

[0639] If the device detects an abnormality, it will automatically start recording. At this time, the video camera and microphone will immediately turn on to collect audio and video from the scene. The data obtained through recording will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[0640] Step 10:

[0641] The server will then notify the police depending on the severity of the anomaly. The user's location is retrieved from GPS data, and an emergency call is sent, including audio and video evidence, allowing the police to respond quickly.

[0642] Through the above processing steps, this system is able to monitor the user's surrounding environment in real time, quickly and accurately detect abnormal behavior or danger, and take appropriate action.

[0643] (Application example 2)

[0644] 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."

[0645] In modern brick-and-mortar stores, troubles and dangerous situations can arise between customers and staff, or between customers themselves. If such situations escalate, they can not only disrupt store operations but also pose significant risks to both parties. Conventional surveillance cameras and alarm systems have difficulty detecting signs of trouble in advance and lack the means to respond immediately. Therefore, there is a need for a system that can detect abnormal behavior and heightened emotions in real time and take appropriate measures.

[0646] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, voice recognition means for converting the collected voice data into text, natural language processing means for analyzing the text data and detecting abnormalities, emotion recognition means for analyzing the text data and recognizing the user's emotional state, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for saving the collected voice data and video recording data in a cloud environment, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to detect abnormal behavior or dangerous situations in the store in real time and immediately issue an alert to prevent trouble and secure necessary evidence data.

[0647] "Audio data" refers to data in which sounds present around the user are recorded in digital format.

[0648] "Speech recognition means" refers to the technology or device used to convert collected voice data into text.

[0649] "Natural language processing tools" refer to techniques and algorithms for analyzing text data and extracting grammatical and semantic information.

[0650] "Emotion recognition means" refers to technologies and algorithms for analyzing voice data and text data to recognize a user's emotional state.

[0651] "Means for issuing a warning when an abnormality is detected" refers to a system or function for notifying users or other parties of a warning in real time.

[0652] "Means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically recording audio and video when an abnormality occurs.

[0653] "Means of storing data in a cloud environment" refers to the technology and systems used to store collected data on a remote server via the Internet.

[0654] "Means for reporting to the police" refers to a system or function that automatically reports to the police in an emergency depending on the severity of the abnormality.

[0655] This invention provides a system that detects abnormal behavior or danger in real time during interactions between customers and staff, or between customers themselves, in a brick-and-mortar store, and responds appropriately. This system detects abnormalities by collecting and analyzing voice data and performing emotion recognition, issues a warning, starts recording or filming as necessary, stores the data in a cloud environment, and can even notify the police in some cases.

[0656] Program processing overview

[0657] The system uses the following main hardware and software:

[0658] Hardware:

[0659] Smartphone (microphone, camera)

[0660] Cloud Server

[0661] Smart device (if needed)

[0662] software:

[0663] Speech recognition engine (e.g., Google Cloud Speech-to-Text API, Amazon Transcribe)

[0664] Natural language processing engines (e.g., Google Cloud Natural Language API, spaCy)

[0665] Emotion recognition engine (e.g. IBM Watson Tone Analyzer, Microsoft Emotion API)

[0666] Real-time communication protocols (e.g. web sockets, MQTT)

[0667] The server first collects voice data from within the physical store using the smartphone's microphone. The collected voice data is sent to the cloud server in real time. The cloud server then converts the voice data into text using a voice recognition engine. Next, it uses natural language processing to analyze the text data and extract grammatical and semantic information. It also uses an emotion recognition engine to evaluate the user's emotional state from the text and voice data and detect abnormalities. If an abnormality is detected, the server issues a warning to the user and the other party in real time, starts audio and video recording as necessary, and saves the data in cloud storage. It can also automatically notify the police depending on the severity of the abnormality.

[0668] Specific examples

[0669] For example, consider the case where one afternoon in a brick-and-mortar store, Customer A yells at Staff B, "Why are you treating me like that?" The customer's voice is collected by the smartphone's microphone and sent to a cloud server. A speech recognition engine converts the speech into text, and natural language processing detects overbearing language. At the same time, an emotion recognition engine recognizes Customer A's anger. This triggers the system to issue a warning message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," and begin recording and filming. This data is then stored in cloud storage, and in some cases, the police may be notified.

[0670] Prompt Sentence Examples

[0671] Please explain your system for detecting abnormal speech and behavior or danger. This system is used in physical stores to prevent trouble between customers and staff, or between customers. It combines speech recognition, natural language processing, and emotion recognition to detect abnormalities and, if necessary, issue a warning or record audio or video. Please also provide detailed descriptions of the application you have created and the hardware and software you use.

[0672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0673] Step 1:

[0674] The device collects the user's surrounding sounds using a microphone, converts the collected audio data into a digital format, and applies a noise-canceling filter. The input is the surrounding audio, and the output is the noise-removed digital audio data.

[0675] Step 2:

[0676] The device encodes the noise-removed digital audio data and sends it to the cloud server using a secure communication protocol (HTTPS / WebSocket).The input is the noise-removed digital audio data, and the output is the encoded audio data sent to the cloud server.

[0677] Step 3:

[0678] The server decodes the received encoded voice data and converts it into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API). The input is the encoded voice data, and the output is the converted text data.

[0679] Step 4:

[0680] The server analyzes the text data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API, spaCy), extracts grammatical and semantic information, and detects anomalies. The input is the text data, and the output is the analyzed text data and the results of anomaly detection.

[0681] Step 5:

[0682] The server analyzes the analyzed text data using an emotion recognition engine (e.g., IBM Watson Tone Analyzer, Microsoft Emotion API) to evaluate the user's emotional state. The input is the analyzed text data, and the output is the evaluation result of the user's emotional state.

[0683] Step 6:

[0684] If an anomaly is detected, the server sends a real-time warning to the device. It generates a warning message and displays it on the device's display or issues an audio warning through the speaker. The input is the result of anomaly detection and emotion evaluation, and the output is a warning notification.

[0685] Step 7:

[0686] The terminal automatically starts recording audio and video when an abnormality is detected. The input is a warning notification, and the output is audio and video data.

[0687] Step 8:

[0688] The device uploads the collected audio and video data to cloud storage and temporarily stores it in local memory as needed. The input is the audio and video data, and the output is the data stored in cloud storage.

[0689] Step 9:

[0690] The server automatically generates and sends an emergency call to the police, including the user's location information and collected evidence data (audio and video). The input is the result of anomaly detection and emotion evaluation, as well as location information. The output is the emergency call sent to the police.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] [Third embodiment]

[0695] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0696] 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.

[0697] 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).

[0698] 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.

[0699] 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.

[0700] 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).

[0701] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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."

[0707] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the present invention will be described in detail.

[0708] System Overview

[0709] This system is primarily composed of "terminals" and "servers." "Terminals" are devices owned by users (smartphones, tablets, dedicated devices, etc.), and "servers" are computers for data processing installed in a cloud environment.

[0710] Program processing explanation

[0711] Audio input and preprocessing

[0712] The device uses a microphone to collect sounds around the user and processes the audio data in real time. Specifically, the device converts the audio signal into a digital format and performs noise reduction and filtering. The audio data is then sent to a server via Wi-Fi or mobile network.

[0713] Speech recognition and text conversion

[0714] The server processes the received voice data and converts it into text using a speech recognition engine. This speech recognition engine uses deep learning technology to convert voice signals into text data with high accuracy. The resulting text data then proceeds to the next processing stage within the server.

[0715] Natural Language Processing and Anomaly Detection

[0716] The server analyzes the text data and applies natural language processing (NLP) algorithms to detect anomalies. Specifically, it performs syntactic analysis, semantic analysis, and sentiment analysis to detect aggressive language or abusive behavior. If an anomaly is detected, a notification is sent to the user's device with details about the anomaly.

[0717] Sending a warning

[0718] If an abnormality is detected, the device will issue a warning message to the user and the other party. In the case of a voice warning, the warning message is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, the message is displayed on the screen.

[0719] Start recording and recording

[0720] When an abnormality is detected, the device will automatically start recording and video recording, the video camera and microphone will be immediately turned on, and the collected data will be saved in local memory and cloud storage in real time, which can be used for later analysis or as evidence.

[0721] Report to the police

[0722] If the abnormality is deemed serious, the server automatically contacts the police, providing the user's location, current situation, and evidence (audio and video) so that the police can respond quickly.

[0723] Specific examples

[0724] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server then converts it into text using voice recognition and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0725] summary

[0726] This invention is a system that uses speech recognition and natural language processing technology to detect abnormalities in real time and ensure the safety of users. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[0727] The processing flow will be explained below.

[0728] Step 1:

[0729] The device uses a microphone to collect audio signals from the user's surroundings. The device then converts the audio signals into a digital format (e.g., PCM format) and processes them in real time, performing noise reduction and filtering to improve sound quality.

[0730] Step 2:

[0731] The device encodes the collected audio data and sends it to a server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[0732] Step 3:

[0733] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then stored in a buffer for further processing.

[0734] Step 4:

[0735] The server analyzes the text data using natural language processing (NLP) algorithms. Specifically, it performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior (such as overbearing language or violent expressions). The analysis results are processed in real time.

[0736] Step 5:

[0737] If the server detects an anomaly, it notifies the device of that information. The notification includes detailed information about the anomaly (for example, whether it was "aggressive language") and a recommended next action (for example, issuing a warning or starting audio or video recording).

[0738] Step 6:

[0739] Based on the notification received by the device, a warning is issued to the user and the other party. For example, in the case of a voice warning, a message such as "This conversation is being recorded, please refrain from inappropriate behavior" is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, a message is displayed on the display.

[0740] Step 7:

[0741] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected audio and video data will be uploaded to cloud storage in real time, and also temporarily stored in local memory for later analysis or as evidence.

[0742] Step 8:

[0743] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video). The police can respond quickly based on this information.

[0744] Example 1

[0745] 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."

[0746] In modern society, to ensure user safety, it is necessary to quickly detect abnormal speech and behavior or danger, especially in public places or environments where danger is anticipated, and to take appropriate measures. However, existing systems have difficulty performing a series of processes in real time, such as collecting voice, analyzing, detecting abnormalities, notifying, recording and filming, and reporting to the police, and are therefore unable to adequately respond to ensure user safety. To solve this problem, a new system that combines highly accurate speech recognition and natural language processing technology is needed.

[0747] 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.

[0748] In this invention, the server includes means including a voice recognition engine for converting voice data into text, natural language processing algorithm means for analyzing the converted text and detecting abnormalities, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to monitor the audio environment around the user in real time, quickly detect abnormal behavior or danger, and take appropriate action.

[0749] A "terminal" is a device owned by a user (such as a smartphone, tablet, or dedicated device) that collects, preprocesses, and transmits voice data.

[0750] "Audio data" refers to data obtained by converting audio signals collected around the user into digital format.

[0751] "Noise reduction" is a process that removes unnecessary noise from audio data.

[0752] "Filtering" is data processing that emphasizes or removes specific frequencies or signal components.

[0753] A "network" is a communication method, such as Wi-Fi or mobile communications, for transmitting data between a terminal and a server.

[0754] A "server" is a computer installed in the cloud for data processing, which analyzes received voice data, detects abnormalities, and takes appropriate action.

[0755] A "speech recognition engine" is software or algorithms for converting voice data into text.

[0756] A "natural language processing algorithm" is a technology for analyzing text data and understanding its content, and is used to detect anomalies.

[0757] A "user interface" is a device that includes a display screen and an operation unit that allows a user to directly operate or check something.

[0758] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[0759] "Encryption" is the process of converting data using a specific algorithm in order to send and receive the data securely.

[0760] "Recording" is the act of recording audio data.

[0761] "Recording" is the act of recording video data.

[0762] "Reporting to the police" means contacting the police when an abnormality or emergency occurs and providing the situation and evidential data.

[0763] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. An embodiment of the present invention will be described in detail below.

[0764] This system is primarily composed of terminals and servers. Terminals are devices owned by users (smartphones, tablets, dedicated devices, etc.), and servers are computers for data processing installed in a cloud environment.

[0765] Audio input and preprocessing

[0766] The device is equipped with a microphone that collects sounds around the user. This audio signal is converted into a digital format. The device then performs noise reduction and filtering to obtain clear audio data. Active noise cancellation technology is used for noise reduction.

[0767] Sending audio data

[0768] The device sends the preprocessed audio data to a server in a cloud environment via a network (Wi-Fi or mobile network). The transmitted data is securely encrypted using SSL / TLS.

[0769] Speech recognition and text conversion

[0770] The server processes the received voice data using a deep learning-based speech recognition engine and converts it into text. This speech recognition engine uses a commonly available speech recognition API (e.g., Google Cloud Speech-to-Text API).

[0771] Natural Language Processing and Anomaly Detection

[0772] The server applies natural language processing (NLP) algorithms to the text data, performing syntactic analysis, semantic analysis, and sentiment analysis to detect anomalies. NLP algorithms use models for topic modeling and sentiment analysis (e.g., BERT, GPT-3).

[0773] Sending a warning

[0774] If an abnormality is detected, the device will issue a warning message. For voice warnings, the device's speech synthesis engine (e.g., Google Cloud Text-to-Speech) will generate the warning message and play it through the speaker. For text warnings, the message will be displayed on the device's display.

[0775] Start recording and recording

[0776] When an abnormality is detected, the device will automatically start recording and turn on the device's video camera and microphone, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time.

[0777] Report to the police

[0778] Depending on the severity of the abnormality, the server will automatically contact the police and provide the user's location information, current situation, and evidence data (audio and video), enabling a prompt response.

[0779] Specific examples

[0780] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0781] Prompt sentence for generative AI model

[0782] The system should generate a program that monitors the user's voice environment in real time and detects anomalies using speech recognition and natural language processing. Specific functions should include voice input and preprocessing, voice data transmission, speech recognition and text conversion, natural language processing and anomaly detection, issuing a warning, starting audio and video recording, and reporting to the police. Please also provide examples of specific technologies and frameworks.

[0783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0784] Step 1: Audio Input and Preprocessing

[0785] The device collects the audio around the user using a microphone. The input is the user's surrounding environmental sound. The collected audio signal is converted into a digital format (A / D conversion). The device's DSP (Digital Signal Processor) then processes the audio signal, performing noise reduction (active noise canceling technology) and filtering. The output is clean digital audio data.

[0786] Step 2: Sending audio data

[0787] The device sends pre-processed audio data to the server via the network (Wi-Fi or mobile network). The input is clean digital audio data. The transmitted data is encrypted by SSL / TLS. The output is the encrypted audio data reaching the server.

[0788] Step 3: Speech recognition and text conversion

[0789] The voice data received by the server is processed by a voice recognition engine (e.g., voice recognition API) using deep learning technology and converted into text data. The input is encrypted voice data. The server decrypts the voice data and inputs it into the voice recognition engine. The output is text data.

[0790] Step 4: Natural Language Processing and Anomaly Detection

[0791] The server applies natural language processing (NLP) algorithms to the text data. The input is text-converted audio data. The server performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior or coercive remarks. The output is a report of whether an anomaly exists and detailed information if one is found.

[0792] Step 5: Sending an alert

[0793] When the device receives a notification of an anomaly detection, it issues a warning message. The input is the anomaly detection notification. In the case of an audio warning, the device uses a speech synthesis engine (e.g., Google Cloud Text-to-Speech) to generate a warning message and plays it through the speaker. In the case of a text warning, the message is displayed on the device display. The output is a voice or text warning message.

[0794] Step 6: Start recording

[0795] When the device receives an anomaly detection notification, it automatically starts recording and audio. The input is the anomaly detection notification. The device turns on the video camera and microphone and saves the collected data in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time. The output is the recorded and audio data.

[0796] Step 7: Report to the police

[0797] The server assesses the severity of the anomaly and, if deemed serious, automatically contacts the police. The input is the analyzed anomaly data and related audio and video data. The server communicates with the police system through an API, providing the user's location, current situation, and evidence data. The output is a police report and the provided information.

[0798] (Application example 1)

[0799] 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."

[0800] Recent advances in voice recognition and surveillance technologies have created a demand for systems that can ensure user safety. However, existing systems often lack sufficient accuracy in anomaly detection, making it difficult to respond immediately in emergencies. Real-time notifications that take user location information into account and secure data storage in cloud storage are also required. Furthermore, there are few systems that integrate advanced voice data analysis using generative AI models with anomaly detection.

[0801] 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.

[0802] In this invention, the server includes means for collecting voice data, speech recognition means for converting the collected voice data into text, natural language processing means for analyzing the converted text data and detecting abnormalities, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for notifying the police depending on the severity of the abnormality, means for acquiring and notifying the user's location information when an abnormality occurs, means for saving data in cloud storage, means for analyzing and responding to the voice data using a generative AI model, and means for inputting prompt sentences to detect abnormalities. This enables highly accurate anomaly detection in real time and appropriate responses.

[0803] "Means for collecting audio data" refers to a device or method that collects audio around the user in real time through a microphone.

[0804] "Speech recognition means for converting collected voice data into text" refers to software or technology for converting collected voice data into digital form and converting it into text data with high accuracy.

[0805] "Natural language processing means for analyzing text data and detecting anomalies" refers to a natural language processing algorithm for analyzing text data and detecting anomalous patterns or content.

[0806] The "means for issuing a warning when an abnormality is detected" refers to a speech synthesis engine or a text message display method that issues a warning message to the user and the other party when an abnormality is detected.

[0807] The "means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically starting audio and video recording in response to a detected abnormality.

[0808] The "means for reporting to the police depending on the severity of the abnormality" is a communication means for reporting the user's location information and evidential data to the police when the abnormality is determined to be serious.

[0809] "Means for acquiring and notifying the user's location information when an abnormality occurs" refers to a mechanism for acquiring the user's location information in real time when an abnormality occurs and notifying relevant organizations and parties.

[0810] "Means for storing data in cloud storage" refers to an online storage service for safely storing collected audio and video data in a cloud environment.

[0811] "Means for analyzing voice data and responding using a generative AI model" refers to a technology that uses a generative AI model that employs deep learning technology to analyze voice data with high accuracy and generate an appropriate response.

[0812] "Means for detecting anomalies by inputting prompt sentences" refers to a method that uses text prompts that are input into a generative AI model to detect specific anomalies.

[0813] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the invention will be described.

[0814] System Configuration

[0815] This system is primarily composed of "terminals" and "servers." "Terminals" can be smartphones, tablets, or dedicated devices. "Servers" are computers for data processing installed in a cloud environment.

[0816] Hardware and Software Use Cases

[0817] Device: A smartphone with a built-in microphone, camera, and speaker.

[0818] Server: A cloud computing environment (e.g., Amazon Web Services or Google Cloud Platform).

[0819] Speech recognition engine: Google's Cloud Speech-to-Text.

[0820] Natural Language Processing (NLP) engine: Proprietary NLP models using SpaCy, NLTK, and TensorFlow.

[0821] Generative AI models: such as OpenAI's GPT-3.

[0822] Cloud storage: Amazon S3 or Google Cloud Storage.

[0823] Data Processing Overview

[0824] 1. Audio input and preprocessing: The device microphone collects the user's surrounding audio in real time, performs noise reduction, and then converts the collected audio data into a digital format, which is then sent to a server via Wi-Fi or mobile network.

[0825] 2. Speech recognition and text conversion: The server processes the received audio data and converts it to text using a speech recognition engine such as Google's Cloud Speech-to-Text. The text data then proceeds to the next processing stage.

[0826] 3. Natural Language Processing and Anomaly Detection: The server analyzes the text data and applies natural language processing algorithms using SpaCy and TensorFlow. Syntax analysis, semantic analysis, and sentiment analysis are performed to detect aggressive language and abusive behavior. If an anomaly is detected, a notification is sent to the user's device with detailed information.

[0827] 4. Sending a warning: If an abnormality is detected, the device will send a warning message to the user and the other party. For voice warnings, the device will use a speech synthesis engine to generate the warning message and play it through the speaker. For text warnings, the device will show the message on the display.

[0828] 5. Start recording: When an abnormality is detected, the device will automatically start recording. The video camera and microphone will be turned on immediately, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3 or Google Cloud Storage) in real time.

[0829] 6. Report to the police: If the abnormality is deemed serious, the server will automatically contact the police, providing the user's location, current situation, and evidence data (audio and video).

[0830] Specific examples

[0831] For example, if a user is walking in a park and someone suddenly calls out, "Help!", this audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the word "help." As an abnormality is detected, the device issues an audio warning to the other party saying, "An abnormality has been detected. A warning has been issued." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0832] Prompt Sentence Examples

[0833] "Detect audio of someone around you making coercive remarks."

[0834] Make sure the word "help" is included in your voice input.

[0835] "Detect text containing violent language and report the details."

[0836] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0837] Step 1:

[0838] Audio input and preprocessing

[0839] The device collects the user's surrounding sounds in real time using a microphone, converts the collected analog audio signals into a digital format, and performs noise reduction and filtering to obtain clean audio data, which is then transmitted to a server via Wi-Fi or a mobile network.

[0840] Input: Ambient analog audio signal

[0841] Output: Clean audio data in digital format

[0842] Step 2:

[0843] Speech recognition and text conversion

[0844] The server receives the voice data sent from the device and converts it into text using a speech recognition engine (such as Google's Cloud Speech-to-Text). This process utilizes a deep learning model to convert voice signals into text with high accuracy.

[0845] Input: Digital audio data

[0846] Output: Text data

[0847] Step 3:

[0848] Natural Language Processing and Anomaly Detection

[0849] The server receives the text data and analyzes it using a natural language processing (NLP) engine (for example, a model using SpaCy or TensorFlow). This analysis includes syntactic analysis, semantic analysis, and sentiment analysis. If an abnormal pattern is detected (for example, strong language or violent content), an anomaly is detected and an alert is generated.

[0850] Input: Text data

[0851] Output: Anomaly detection results (alert information)

[0852] Step 4:

[0853] Sending a warning

[0854] If an abnormality is detected, the device will issue a warning message to the user and other relevant parties. As a voice warning, the device generates a warning message using a speech synthesis engine and plays it through the speaker. As a text warning, the device displays the warning message on the screen.

[0855] Input: Anomaly detection result (alert information)

[0856] Output: Warning message (audio or text)

[0857] Step 5:

[0858] Start recording and recording

[0859] When an abnormality is detected, the device will automatically start recording and turn on the video camera and microphone, and the collected video and audio data will be saved in real time to cloud storage (e.g., Amazon S3 or Google Cloud Storage).

[0860] Input: Anomaly detection result (alert information)

[0861] Output: Audio and video data

[0862] Step 6:

[0863] Report to the police

[0864] If the abnormality is deemed serious, the server automatically notifies the police, providing them with the user's location, current situation, and collected audio and video data, allowing the police to respond quickly.

[0865] Input: Anomaly detection results (alert information) and user location information

[0866] Output: Police report (location and evidence data)

[0867] Examples of prompt statements

[0868] By feeding prompts into a generative AI model (e.g., GPT-3), the system can detect specific anomalous patterns. Here are some examples of prompts:

[0869] "Detect audio of someone around you making coercive remarks."

[0870] Make sure the word "help" is included in your voice input.

[0871] "Detect text containing violent language and report the details."

[0872] 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.

[0873] The present invention provides a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger, thereby ensuring the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[0874] System Overview

[0875] This system consists of a "terminal," a "server," and an "emotion engine." The "terminal" corresponds to a device owned by the user (smartphone, tablet, dedicated device, etc.), and the "server" is a computer for data processing installed in a cloud environment. The "emotion engine" has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[0876] Program processing explanation

[0877] Audio input and preprocessing

[0878] The device uses a microphone to collect audio from the user's surroundings, converts the audio signal into a digital format, and then uses noise reduction and filtering to make it clearer. The audio data is then encoded and sent to a server using a secure communication protocol.

[0879] Speech recognition and text conversion

[0880] The server decodes the received voice data and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice data into text data with high accuracy. The converted text data is then passed on to the next processing stage.

[0881] Natural Language Processing and Anomaly Detection

[0882] The server analyzes the text data using natural language processing (NLP) algorithms. It performs syntax analysis, semantic analysis, and sentiment analysis to detect coercive language and violent behavior. If an anomaly is detected based on the results of this analysis, detailed information is sent to the device.

[0883] Emotion recognition by emotion engine

[0884] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes voice parameters such as tone, pitch, and tempo to recognize emotions such as anger, sadness, and joy. This information is returned to the server and reflected in the anomaly detection process.

[0885] Sending a warning

[0886] If an abnormality is detected, the device will issue a warning to the user and the other party. For voice warnings, a speech synthesis engine is used to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior" and play it through the speaker. For text warnings, a message is displayed on the display. It is also possible to issue different warning messages depending on the user's emotional state using an emotion engine.

[0887] Start recording and recording

[0888] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time and temporarily stored in local memory for later analysis or as evidence.

[0889] Report to the police

[0890] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video), allowing the police to respond quickly.

[0891] Specific examples

[0892] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This speech is collected by the device and sent to the server. The server then converts it into text using a speech recognition engine, detecting the "overbearing language." At the same time, an emotion engine recognizes the user's anger. Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[0893] summary

[0894] This invention is a system that combines speech recognition and natural language processing technologies, and also introduces an emotion engine to detect abnormalities in real time and ensure user safety. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] The device uses a microphone to collect audio signals from the user's surroundings, converts them into a digital format, and performs real-time pre-amplification, noise reduction, and filtering to improve sound quality.

[0898] Step 2:

[0899] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[0900] Step 3:

[0901] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then immediately stored in a buffer for NLP analysis.

[0902] Step 4:

[0903] The server analyzes the text data using natural language processing (NLP) algorithms. Syntax analysis, semantic analysis, and sentiment analysis are performed in succession to detect abnormal behavior (such as aggressive language or violent expressions). This analysis involves analyzing the frequency of occurrence of specific keywords and phrases and context.

[0904] Step 5:

[0905] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine identifies the emotional state based on parameters such as tone, pitch, and tempo of the voice. The identified emotional state is sent to the server and reflected in the anomaly detection process.

[0906] Step 6:

[0907] If the server detects an anomaly based on the text and emotional state, it notifies the device with detailed information about the anomaly (e.g., "aggressive language" or "angry emotion") and a recommended next action (e.g., issuing a warning or starting audio or video recording).

[0908] Step 7:

[0909] Based on the notification received by the device, a warning is issued to the user and the other party. In the case of a voice warning, a voice synthesis engine is used to generate a message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," which is played through the speaker. If the emotion engine recognizes "anger," a warning with an even stronger tone is issued.

[0910] Step 8:

[0911] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time, while also being temporarily stored in local memory for later analysis or as evidence.

[0912] Step 9:

[0913] If the abnormality is deemed serious, the server automatically notifies the police. The report includes the user's location (GPS data), current situation, and evidence data (audio and video), allowing the police to respond quickly.

[0914] Example 2

[0915] 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."

[0916] Conventional technologies have difficulty monitoring the user's surroundings in real time and quickly detecting abnormal behavior or danger. They also lack the ability to understand the user's emotional state through emotion analysis and respond appropriately based on that information. Furthermore, they lack the ability to record audio or video after detecting an abnormality, making it difficult to quickly report the incident to the police.

[0917] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, means for converting the collected voice data into a digital format and performing preprocessing, means for encoding the preprocessed voice data and transmitting it to the server, means for converting the received voice data into text, means for analyzing the text data and detecting anomalies, means for analyzing emotions when anomalies are detected, means for issuing an alarm when an anomaly is detected, means for starting audio and video recording when an anomaly is detected, and means for reporting to the police depending on the severity of the anomaly. This enables real-time monitoring of the user's surrounding environment and rapid detection of abnormal speech and behavior or danger. Furthermore, emotion analysis can be used to understand the user's emotional state in detail and to take appropriate action based on that information. Furthermore, audio and video recording is automatically performed after an anomaly is detected, enabling rapid reporting to the police if necessary.

[0918] "Audio Data" means a digital representation of sound collected using a Device's microphone.

[0919] "Converting to digital form" refers to converting collected audio data into a digital signal that can be analyzed and processed.

[0920] "Preprocessing" refers to processing to improve the quality of collected audio data by performing noise reduction and filtering.

[0921] "Encoding" means converting audio data into a specific format so that it can be sent to a server via a secure communications protocol.

[0922] "Speech recognition means" refers to a system that analyzes voice data and converts the voice into text.

[0923] "Natural language processing means" refers to algorithms or programs that analyze text data and detect abnormal behavior or danger from its content.

[0924] "Means for analyzing emotions" refers to algorithms or programs that analyze the tone, pitch, tempo, etc. of a user's voice to determine the user's emotional state.

[0925] The "means for issuing a warning" is a system that issues a warning message to the user and the other party by voice or text when an abnormality is detected.

[0926] The "means for starting audio and video recording" refers to a system that automatically activates the device's microphone and camera to collect audio and video data when an abnormality is detected.

[0927] The "means for reporting to the police" is a system that automatically reports to the police depending on the severity of the abnormality and provides the user's location information and collected evidence data.

[0928] The present invention relates to a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger to ensure the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[0929] System Overview

[0930] This system consists of a terminal, a server, and an emotion engine. The terminal is a device held by the user (smartphone, tablet, dedicated device, etc.), and the server is a computer for data processing installed in a cloud environment. The emotion engine has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[0931] Hardware and software used

[0932] The hardware used includes devices such as smartphones and tablets. These devices also have built-in microphones and cameras. Servers are located in a cloud computing environment and perform data processing and analysis. The software used includes audio editing software (e.g., Audacity), speech recognition engines (e.g., Google Cloud Speech-to-Text API), natural language processing tools (e.g., Spacy, NLTK), emotion engines (e.g., IBM Watson Tone Analyzer), and speech synthesis engines (e.g., Amazon Polly).

[0933] Audio input and preprocessing

[0934] The device uses a microphone to collect audio around the user. For example, if the user has a smartphone, the smartphone's built-in microphone is used. Since the collected audio is difficult to use as is, it is converted into a digital format and noise reduction and filtering are performed. This process may involve the use of audio input libraries such as "PulseAudio" or "ALSA." The cleared audio data is then sent to the server using a secure communication protocol (e.g., TLS / SSL).

[0935] Speech recognition and text conversion

[0936] The server decodes the voice data sent from the device and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice into text data with high accuracy. For example, if a user says "Help!" to ask for help, the speech recognition engine generates the text "Help." This text data is passed to the next stage of natural language processing.

[0937] Natural Language Processing and Anomaly Detection

[0938] The server analyzes the text data using natural language processing algorithms. Tools such as Spacy and NLTK are used for analysis, and syntactic analysis, semantic analysis, and sentiment analysis are performed. For example, if the other person uses an overbearing phrase like "What are you looking at?", the server analyzes the text and detects the overbearing phrase. This information is used for anomaly detection, and detailed information about the detected anomaly is sent to the device.

[0939] Emotion recognition by emotion engine

[0940] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes parameters such as tone, pitch, and tempo of the voice to recognize emotions such as anger, sadness, and joy. For example, if the other person is angry, the emotion of anger is recognized. This information is sent back to the server and reflected in the anomaly detection process.

[0941] Sending a warning

[0942] If an abnormality is detected, the device will issue a warning to the user and the other party. A voice warning uses a speech synthesis engine to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior." This message is played through the device's speaker. A text warning displays a message on the device's display.

[0943] Start recording and recording

[0944] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on to collect audio and video. For example, the device's camera and microphone will be activated and the audio and video from the scene will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[0945] Report to the police

[0946] If the abnormality is deemed serious, the server automatically notifies the police. The user's location information is obtained from GPS data, and an emergency call is made including the current situation and evidence data (audio and video). This allows the police to respond quickly. For example, if the server determines that the user is in danger, it immediately notifies the police and provides the necessary evidence along with the GPS location information.

[0947] Prompt Sentence Examples

[0948] Below are examples of prompt sentences that input voice data into a generative AI model to perform tasks that meet the following conditions:

[0949] The user is being monitored in real time for their current audio environment. Enter this audio data and perform a task that meets the following criteria:

[0950] 1. Convert audio data to text

[0951] 2. Analyze the converted text using natural language processing to detect abnormal behavior and dangerous language

[0952] 3. Analyze emotional states using an emotion engine

[0953] 4. If an abnormality is detected, a warning message is generated and notified to the user and the other party.

[0954] 5. If necessary, start recording and save to cloud storage.

[0955] 6. Automatically notify the police if a serious abnormality is detected

[0956] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0957] Step 1:

[0958] The device collects audio around the user using a microphone. This collected audio data is input to the device in analog format. An audio input library such as "PulseAudio" or "ALSA" is then used to convert this analog audio data into digital format. The result of this conversion process is digital audio data output.

[0959] Step 2:

[0960] The device preprocesses the digital audio data. Specifically, it uses software such as Audacity or Waves Noise Reduction to reduce noise and perform filtering. This preprocessing improves the quality of the audio data, making it easier to analyze. After preprocessing, the audio data is output as clear audio.

[0961] Step 3:

[0962] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., TLS / SSL). This encoding process converts the audio data into a format that can be transferred efficiently and securely. The encoded audio data is then sent to the server.

[0963] Step 4:

[0964] The server decodes the encoded audio data it receives. The result of the decoding process is the original digital audio data. The reconstructed audio data is output.

[0965] Step 5:

[0966] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to convert the decoded voice data into text. This process uses deep learning technology to convert voice into text with high accuracy. For example, if the voice input is "Help!", the text data "Help" is output.

[0967] Step 6:

[0968] The server analyzes the text data using natural language processing algorithms (e.g., Spacy, NLTK). The analysis includes syntactic analysis, semantic analysis, and sentiment analysis. For example, if text data containing the overbearing phrase "What are you looking at!" is input, it will detect this as dangerous behavior. The analysis results will output information indicating that abnormal behavior has been detected.

[0969] Step 7:

[0970] The device uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from voice data in real time. The emotion engine analyzes parameters such as voice tone, pitch, and tempo to identify emotions such as anger, sadness, and joy. For example, if anger is detected, the emotion information for "anger" is output and sent to the server.

[0971] Step 8:

[0972] If the device detects an abnormality, it will issue a warning to the user and the other party. It uses a speech synthesis engine (e.g., Amazon Polly) to generate a voice warning message such as, "This conversation is being recorded. Please refrain from inappropriate behavior." The generated voice message is played from the device's speaker. At the same time, a text message is also displayed on the display.

[0973] Step 9:

[0974] If the device detects an abnormality, it will automatically start recording. At this time, the video camera and microphone will immediately turn on to collect audio and video from the scene. The data obtained through recording will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[0975] Step 10:

[0976] The server will then notify the police depending on the severity of the anomaly. The user's location is retrieved from GPS data, and an emergency call is sent, including audio and video evidence, allowing the police to respond quickly.

[0977] Through the above processing steps, this system is able to monitor the user's surrounding environment in real time, quickly and accurately detect abnormal behavior or danger, and take appropriate action.

[0978] (Application example 2)

[0979] 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."

[0980] In modern brick-and-mortar stores, troubles and dangerous situations can arise between customers and staff, or between customers themselves. If such situations escalate, they can not only disrupt store operations but also pose significant risks to both parties. Conventional surveillance cameras and alarm systems have difficulty detecting signs of trouble in advance and lack the means to respond immediately. Therefore, there is a need for a system that can detect abnormal behavior and heightened emotions in real time and take appropriate measures.

[0981] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, voice recognition means for converting the collected voice data into text, natural language processing means for analyzing the text data and detecting abnormalities, emotion recognition means for analyzing the text data and recognizing the user's emotional state, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for saving the collected voice data and video recording data in a cloud environment, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to detect abnormal behavior or dangerous situations in the store in real time and immediately issue an alert to prevent trouble and secure necessary evidence data.

[0982] "Audio data" refers to data in which sounds present around the user are recorded in digital format.

[0983] "Speech recognition means" refers to the technology or device used to convert collected voice data into text.

[0984] "Natural language processing tools" refer to techniques and algorithms for analyzing text data and extracting grammatical and semantic information.

[0985] "Emotion recognition means" refers to technologies and algorithms for analyzing voice data and text data to recognize a user's emotional state.

[0986] "Means for issuing a warning when an abnormality is detected" refers to a system or function for notifying users or other parties of a warning in real time.

[0987] "Means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically recording audio and video when an abnormality occurs.

[0988] "Means of storing data in a cloud environment" refers to the technology and systems used to store collected data on a remote server via the Internet.

[0989] "Means for reporting to the police" refers to a system or function that automatically reports to the police in an emergency depending on the severity of the abnormality.

[0990] This invention provides a system that detects abnormal behavior or danger in real time during interactions between customers and staff, or between customers themselves, in a brick-and-mortar store, and responds appropriately. This system detects abnormalities by collecting and analyzing voice data and performing emotion recognition, issues a warning, starts recording or filming as necessary, stores the data in a cloud environment, and can even notify the police in some cases.

[0991] Program processing overview

[0992] The system uses the following main hardware and software:

[0993] Hardware:

[0994] Smartphone (microphone, camera)

[0995] Cloud Server

[0996] Smart device (if needed)

[0997] software:

[0998] Speech recognition engine (e.g., Google Cloud Speech-to-Text API, Amazon Transcribe)

[0999] Natural language processing engines (e.g., Google Cloud Natural Language API, spaCy)

[1000] Emotion recognition engine (e.g. IBM Watson Tone Analyzer, Microsoft Emotion API)

[1001] Real-time communication protocols (e.g. web sockets, MQTT)

[1002] The server first collects voice data from within the physical store using the smartphone's microphone. The collected voice data is sent to the cloud server in real time. The cloud server then converts the voice data into text using a voice recognition engine. Next, it uses natural language processing to analyze the text data and extract grammatical and semantic information. It also uses an emotion recognition engine to evaluate the user's emotional state from the text and voice data and detect abnormalities. If an abnormality is detected, the server issues a warning to the user and the other party in real time, starts audio and video recording as necessary, and saves the data in cloud storage. It can also automatically notify the police depending on the severity of the abnormality.

[1003] Specific examples

[1004] For example, consider the case where one afternoon in a brick-and-mortar store, Customer A yells at Staff B, "Why are you treating me like that?" The customer's voice is collected by the smartphone's microphone and sent to a cloud server. A speech recognition engine converts the speech into text, and natural language processing detects overbearing language. At the same time, an emotion recognition engine recognizes Customer A's anger. This triggers the system to issue a warning message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," and begin recording and filming. This data is then stored in cloud storage, and in some cases, the police may be notified.

[1005] Prompt Sentence Examples

[1006] Please explain your system for detecting abnormal speech and behavior or danger. This system is used in physical stores to prevent trouble between customers and staff, or between customers. It combines speech recognition, natural language processing, and emotion recognition to detect abnormalities and, if necessary, issue a warning or record audio or video. Please also provide detailed descriptions of the application you have created and the hardware and software you use.

[1007] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1008] Step 1:

[1009] The device collects the user's surrounding sounds using a microphone, converts the collected audio data into a digital format, and applies a noise-canceling filter. The input is the surrounding audio, and the output is the noise-removed digital audio data.

[1010] Step 2:

[1011] The device encodes the noise-removed digital audio data and sends it to the cloud server using a secure communication protocol (HTTPS / WebSocket).The input is the noise-removed digital audio data, and the output is the encoded audio data sent to the cloud server.

[1012] Step 3:

[1013] The server decodes the received encoded voice data and converts it into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API). The input is the encoded voice data, and the output is the converted text data.

[1014] Step 4:

[1015] The server analyzes the text data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API, spaCy), extracts grammatical and semantic information, and detects anomalies. The input is the text data, and the output is the analyzed text data and the results of anomaly detection.

[1016] Step 5:

[1017] The server analyzes the analyzed text data using an emotion recognition engine (e.g., IBM Watson Tone Analyzer, Microsoft Emotion API) to evaluate the user's emotional state. The input is the analyzed text data, and the output is the evaluation result of the user's emotional state.

[1018] Step 6:

[1019] If an anomaly is detected, the server sends a real-time warning to the device. It generates a warning message and displays it on the device's display or issues an audio warning through the speaker. The input is the result of anomaly detection and emotion evaluation, and the output is a warning notification.

[1020] Step 7:

[1021] The terminal automatically starts recording audio and video when an abnormality is detected. The input is a warning notification, and the output is audio and video data.

[1022] Step 8:

[1023] The device uploads the collected audio and video data to cloud storage and temporarily stores it in local memory as needed. The input is the audio and video data, and the output is the data stored in cloud storage.

[1024] Step 9:

[1025] The server automatically generates and sends an emergency call to the police, including the user's location information and collected evidence data (audio and video). The input is the result of anomaly detection and emotion evaluation, as well as location information. The output is the emergency call sent to the police.

[1026] 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.

[1027] 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.

[1028] 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.

[1029] [Fourth embodiment]

[1030] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1031] 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.

[1032] 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).

[1033] 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.

[1034] 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.

[1035] 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).

[1036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1037] 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.

[1038] 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.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] 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."

[1043] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the present invention will be described in detail.

[1044] System Overview

[1045] This system is primarily composed of "terminals" and "servers." "Terminals" are devices owned by users (smartphones, tablets, dedicated devices, etc.), and "servers" are computers for data processing installed in a cloud environment.

[1046] Program processing explanation

[1047] Audio input and preprocessing

[1048] The device uses a microphone to collect sounds around the user and processes the audio data in real time. Specifically, the device converts the audio signal into a digital format and performs noise reduction and filtering. The audio data is then sent to a server via Wi-Fi or mobile network.

[1049] Speech recognition and text conversion

[1050] The server processes the received voice data and converts it into text using a speech recognition engine. This speech recognition engine uses deep learning technology to convert voice signals into text data with high accuracy. The resulting text data then proceeds to the next processing stage within the server.

[1051] Natural Language Processing and Anomaly Detection

[1052] The server analyzes the text data and applies natural language processing (NLP) algorithms to detect anomalies. Specifically, it performs syntactic analysis, semantic analysis, and sentiment analysis to detect aggressive language or abusive behavior. If an anomaly is detected, a notification is sent to the user's device with details about the anomaly.

[1053] Sending a warning

[1054] If an abnormality is detected, the device will issue a warning message to the user and the other party. In the case of a voice warning, the warning message is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, the message is displayed on the screen.

[1055] Start recording and recording

[1056] When an abnormality is detected, the device will automatically start recording and video recording, the video camera and microphone will be immediately turned on, and the collected data will be saved in local memory and cloud storage in real time, which can be used for later analysis or as evidence.

[1057] Report to the police

[1058] If the abnormality is deemed serious, the server automatically contacts the police, providing the user's location, current situation, and evidence (audio and video) so that the police can respond quickly.

[1059] Specific examples

[1060] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server then converts it into text using voice recognition and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[1061] summary

[1062] This invention is a system that uses speech recognition and natural language processing technology to detect abnormalities in real time and ensure the safety of users. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[1063] The processing flow will be explained below.

[1064] Step 1:

[1065] The device uses a microphone to collect audio signals from the user's surroundings. The device then converts the audio signals into a digital format (e.g., PCM format) and processes them in real time, performing noise reduction and filtering to improve sound quality.

[1066] Step 2:

[1067] The device encodes the collected audio data and sends it to a server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[1068] Step 3:

[1069] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then stored in a buffer for further processing.

[1070] Step 4:

[1071] The server analyzes the text data using natural language processing (NLP) algorithms. Specifically, it performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior (such as overbearing language or violent expressions). The analysis results are processed in real time.

[1072] Step 5:

[1073] If the server detects an anomaly, it notifies the device of that information. The notification includes detailed information about the anomaly (for example, whether it was "aggressive language") and a recommended next action (for example, issuing a warning or starting audio or video recording).

[1074] Step 6:

[1075] Based on the notification received by the device, a warning is issued to the user and the other party. For example, in the case of a voice warning, a message such as "This conversation is being recorded, please refrain from inappropriate behavior" is generated using a speech synthesis engine and played through the speaker. In the case of a text warning, a message is displayed on the display.

[1076] Step 7:

[1077] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected audio and video data will be uploaded to cloud storage in real time, and also temporarily stored in local memory for later analysis or as evidence.

[1078] Step 8:

[1079] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video). The police can respond quickly based on this information.

[1080] Example 1

[1081] 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."

[1082] In modern society, to ensure user safety, it is necessary to quickly detect abnormal speech and behavior or danger, especially in public places or environments where danger is anticipated, and to take appropriate measures. However, existing systems have difficulty performing a series of processes in real time, such as collecting voice, analyzing, detecting abnormalities, notifying, recording and filming, and reporting to the police, and are therefore unable to adequately respond to ensure user safety. To solve this problem, a new system that combines highly accurate speech recognition and natural language processing technology is needed.

[1083] 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.

[1084] In this invention, the server includes means including a voice recognition engine for converting voice data into text, natural language processing algorithm means for analyzing the converted text and detecting abnormalities, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to monitor the audio environment around the user in real time, quickly detect abnormal behavior or danger, and take appropriate action.

[1085] A "terminal" is a device owned by a user (such as a smartphone, tablet, or dedicated device) that collects, preprocesses, and transmits voice data.

[1086] "Audio data" refers to data obtained by converting audio signals collected around the user into digital format.

[1087] "Noise reduction" is a process that removes unnecessary noise from audio data.

[1088] "Filtering" is data processing that emphasizes or removes specific frequencies or signal components.

[1089] A "network" is a communication method, such as Wi-Fi or mobile communications, for transmitting data between a terminal and a server.

[1090] A "server" is a computer installed in the cloud for data processing, which analyzes received voice data, detects abnormalities, and takes appropriate action.

[1091] A "speech recognition engine" is software or algorithms for converting voice data into text.

[1092] A "natural language processing algorithm" is a technology for analyzing text data and understanding its content, and is used to detect anomalies.

[1093] A "user interface" is a device that includes a display screen and an operation unit that allows a user to directly operate or check something.

[1094] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[1095] "Encryption" is the process of converting data using a specific algorithm in order to send and receive the data securely.

[1096] "Recording" is the act of recording audio data.

[1097] "Recording" is the act of recording video data.

[1098] "Reporting to the police" means contacting the police when an abnormality or emergency occurs and providing the situation and evidential data.

[1099] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. An embodiment of the present invention will be described in detail below.

[1100] This system is primarily composed of terminals and servers. Terminals are devices owned by users (smartphones, tablets, dedicated devices, etc.), and servers are computers for data processing installed in a cloud environment.

[1101] Audio input and preprocessing

[1102] The device is equipped with a microphone that collects sounds around the user. This audio signal is converted into a digital format. The device then performs noise reduction and filtering to obtain clear audio data. Active noise cancellation technology is used for noise reduction.

[1103] Sending audio data

[1104] The device sends the preprocessed audio data to a server in a cloud environment via a network (Wi-Fi or mobile network). The transmitted data is securely encrypted using SSL / TLS.

[1105] Speech recognition and text conversion

[1106] The server processes the received voice data using a deep learning-based speech recognition engine and converts it into text. This speech recognition engine uses a commonly available speech recognition API (e.g., Google Cloud Speech-to-Text API).

[1107] Natural Language Processing and Anomaly Detection

[1108] The server applies natural language processing (NLP) algorithms to the text data, performing syntactic analysis, semantic analysis, and sentiment analysis to detect anomalies. NLP algorithms use models for topic modeling and sentiment analysis (e.g., BERT, GPT-3).

[1109] Sending a warning

[1110] If an abnormality is detected, the device will issue a warning message. For voice warnings, the device's speech synthesis engine (e.g., Google Cloud Text-to-Speech) will generate the warning message and play it through the speaker. For text warnings, the message will be displayed on the device's display.

[1111] Start recording and recording

[1112] When an abnormality is detected, the device will automatically start recording and turn on the device's video camera and microphone, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time.

[1113] Report to the police

[1114] Depending on the severity of the abnormality, the server will automatically contact the police and provide the user's location information, current situation, and evidence data (audio and video), enabling a prompt response.

[1115] Specific examples

[1116] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the "overbearing language." Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[1117] Prompt sentence for generative AI model

[1118] The system should generate a program that monitors the user's voice environment in real time and detects anomalies using speech recognition and natural language processing. Specific functions should include voice input and preprocessing, voice data transmission, speech recognition and text conversion, natural language processing and anomaly detection, issuing a warning, starting audio and video recording, and reporting to the police. Please also provide examples of specific technologies and frameworks.

[1119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1120] Step 1: Audio Input and Preprocessing

[1121] The device collects the audio around the user using a microphone. The input is the user's surrounding environmental sound. The collected audio signal is converted into a digital format (A / D conversion). The device's DSP (Digital Signal Processor) then processes the audio signal, performing noise reduction (active noise canceling technology) and filtering. The output is clean digital audio data.

[1122] Step 2: Sending audio data

[1123] The device sends pre-processed audio data to the server via the network (Wi-Fi or mobile network). The input is clean digital audio data. The transmitted data is encrypted by SSL / TLS. The output is the encrypted audio data reaching the server.

[1124] Step 3: Speech recognition and text conversion

[1125] The voice data received by the server is processed by a voice recognition engine (e.g., voice recognition API) using deep learning technology and converted into text data. The input is encrypted voice data. The server decrypts the voice data and inputs it into the voice recognition engine. The output is text data.

[1126] Step 4: Natural Language Processing and Anomaly Detection

[1127] The server applies natural language processing (NLP) algorithms to the text data. The input is text-converted audio data. The server performs syntax analysis, semantic analysis, and sentiment analysis to detect abnormal behavior or coercive remarks. The output is a report of whether an anomaly exists and detailed information if one is found.

[1128] Step 5: Sending an alert

[1129] When the device receives a notification of an anomaly detection, it issues a warning message. The input is the anomaly detection notification. In the case of an audio warning, the device uses a speech synthesis engine (e.g., Google Cloud Text-to-Speech) to generate a warning message and plays it through the speaker. In the case of a text warning, the message is displayed on the device display. The output is a voice or text warning message.

[1130] Step 6: Start recording

[1131] When the device receives an anomaly detection notification, it automatically starts recording and audio. The input is the anomaly detection notification. The device turns on the video camera and microphone and saves the collected data in local memory and cloud storage (e.g., Amazon S3, Google Cloud Storage) in real time. The output is the recorded and audio data.

[1132] Step 7: Report to the police

[1133] The server assesses the severity of the anomaly and, if deemed serious, automatically contacts the police. The input is the analyzed anomaly data and related audio and video data. The server communicates with the police system through an API, providing the user's location, current situation, and evidence data. The output is a police report and the provided information.

[1134] (Application example 1)

[1135] 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."

[1136] Recent advances in voice recognition and surveillance technologies have created a demand for systems that can ensure user safety. However, existing systems often lack sufficient accuracy in anomaly detection, making it difficult to respond immediately in emergencies. Real-time notifications that take user location information into account and secure data storage in cloud storage are also required. Furthermore, there are few systems that integrate advanced voice data analysis using generative AI models with anomaly detection.

[1137] 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.

[1138] In this invention, the server includes means for collecting voice data, speech recognition means for converting the collected voice data into text, natural language processing means for analyzing the converted text data and detecting abnormalities, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for notifying the police depending on the severity of the abnormality, means for acquiring and notifying the user's location information when an abnormality occurs, means for saving data in cloud storage, means for analyzing and responding to the voice data using a generative AI model, and means for inputting prompt sentences to detect abnormalities. This enables highly accurate anomaly detection in real time and appropriate responses.

[1139] "Means for collecting audio data" refers to a device or method that collects audio around the user in real time through a microphone.

[1140] "Speech recognition means for converting collected voice data into text" refers to software or technology for converting collected voice data into digital form and converting it into text data with high accuracy.

[1141] "Natural language processing means for analyzing text data and detecting anomalies" refers to a natural language processing algorithm for analyzing text data and detecting anomalous patterns or content.

[1142] The "means for issuing a warning when an abnormality is detected" refers to a speech synthesis engine or a text message display method that issues a warning message to the user and the other party when an abnormality is detected.

[1143] The "means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically starting audio and video recording in response to a detected abnormality.

[1144] The "means for reporting to the police depending on the severity of the abnormality" is a communication means for reporting the user's location information and evidential data to the police when the abnormality is determined to be serious.

[1145] "Means for acquiring and notifying the user's location information when an abnormality occurs" refers to a mechanism for acquiring the user's location information in real time when an abnormality occurs and notifying relevant organizations and parties.

[1146] "Means for storing data in cloud storage" refers to an online storage service for safely storing collected audio and video data in a cloud environment.

[1147] "Means for analyzing voice data and responding using a generative AI model" refers to a technology that uses a generative AI model that employs deep learning technology to analyze voice data with high accuracy and generate an appropriate response.

[1148] "Means for detecting anomalies by inputting prompt sentences" refers to a method that uses text prompts that are input into a generative AI model to detect specific anomalies.

[1149] The present invention provides a system for ensuring the safety of a user by monitoring the user's voice environment in real time and detecting abnormal speech and behavior or danger. Hereinafter, an embodiment of the invention will be described.

[1150] System Configuration

[1151] This system is primarily composed of "terminals" and "servers." "Terminals" can be smartphones, tablets, or dedicated devices. "Servers" are computers for data processing installed in a cloud environment.

[1152] Hardware and Software Use Cases

[1153] Device: A smartphone with a built-in microphone, camera, and speaker.

[1154] Server: A cloud computing environment (e.g., Amazon Web Services or Google Cloud Platform).

[1155] Speech recognition engine: Google's Cloud Speech-to-Text.

[1156] Natural Language Processing (NLP) engine: Proprietary NLP models using SpaCy, NLTK, and TensorFlow.

[1157] Generative AI models: such as OpenAI's GPT-3.

[1158] Cloud storage: Amazon S3 or Google Cloud Storage.

[1159] Data Processing Overview

[1160] 1. Audio input and preprocessing: The device microphone collects the user's surrounding audio in real time, performs noise reduction, and then converts the collected audio data into a digital format, which is then sent to a server via Wi-Fi or mobile network.

[1161] 2. Speech recognition and text conversion: The server processes the received audio data and converts it to text using a speech recognition engine such as Google's Cloud Speech-to-Text. The text data then proceeds to the next processing stage.

[1162] 3. Natural Language Processing and Anomaly Detection: The server analyzes the text data and applies natural language processing algorithms using SpaCy and TensorFlow. Syntax analysis, semantic analysis, and sentiment analysis are performed to detect aggressive language and abusive behavior. If an anomaly is detected, a notification is sent to the user's device with detailed information.

[1163] 4. Sending a warning: If an abnormality is detected, the device will send a warning message to the user and the other party. For voice warnings, the device will use a speech synthesis engine to generate the warning message and play it through the speaker. For text warnings, the device will show the message on the display.

[1164] 5. Start recording: When an abnormality is detected, the device will automatically start recording. The video camera and microphone will be turned on immediately, and the collected data will be saved in local memory and cloud storage (e.g., Amazon S3 or Google Cloud Storage) in real time.

[1165] 6. Report to the police: If the abnormality is deemed serious, the server will automatically contact the police, providing the user's location, current situation, and evidence data (audio and video).

[1166] Specific examples

[1167] For example, if a user is walking in a park and someone suddenly calls out, "Help!", this audio is collected by the device and sent to the server. The server uses voice recognition to convert it into text and detects the word "help." As an abnormality is detected, the device issues an audio warning to the other party saying, "An abnormality has been detected. A warning has been issued." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[1168] Prompt Sentence Examples

[1169] "Detect audio of someone around you making coercive remarks."

[1170] Make sure the word "help" is included in your voice input.

[1171] "Detect text containing violent language and report the details."

[1172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1173] Step 1:

[1174] Audio input and preprocessing

[1175] The device collects the user's surrounding sounds in real time using a microphone, converts the collected analog audio signals into a digital format, and performs noise reduction and filtering to obtain clean audio data, which is then transmitted to a server via Wi-Fi or a mobile network.

[1176] Input: Ambient analog audio signal

[1177] Output: Clean audio data in digital format

[1178] Step 2:

[1179] Speech recognition and text conversion

[1180] The server receives the voice data sent from the device and converts it into text using a speech recognition engine (such as Google's Cloud Speech-to-Text). This process utilizes a deep learning model to convert voice signals into text with high accuracy.

[1181] Input: Digital audio data

[1182] Output: Text data

[1183] Step 3:

[1184] Natural Language Processing and Anomaly Detection

[1185] The server receives the text data and analyzes it using a natural language processing (NLP) engine (for example, a model using SpaCy or TensorFlow). This analysis includes syntactic analysis, semantic analysis, and sentiment analysis. If an abnormal pattern is detected (for example, strong language or violent content), an anomaly is detected and an alert is generated.

[1186] Input: Text data

[1187] Output: Anomaly detection results (alert information)

[1188] Step 4:

[1189] Sending a warning

[1190] If an abnormality is detected, the device will issue a warning message to the user and other relevant parties. As a voice warning, the device generates a warning message using a speech synthesis engine and plays it through the speaker. As a text warning, the device displays the warning message on the screen.

[1191] Input: Anomaly detection result (alert information)

[1192] Output: Warning message (audio or text)

[1193] Step 5:

[1194] Start recording and recording

[1195] When an abnormality is detected, the device will automatically start recording and turn on the video camera and microphone, and the collected video and audio data will be saved in real time to cloud storage (e.g., Amazon S3 or Google Cloud Storage).

[1196] Input: Anomaly detection result (alert information)

[1197] Output: Audio and video data

[1198] Step 6:

[1199] Report to the police

[1200] If the abnormality is deemed serious, the server automatically notifies the police, providing them with the user's location, current situation, and collected audio and video data, allowing the police to respond quickly.

[1201] Input: Anomaly detection results (alert information) and user location information

[1202] Output: Police report (location and evidence data)

[1203] Examples of prompt statements

[1204] By feeding prompts into a generative AI model (e.g., GPT-3), the system can detect specific anomalous patterns. Here are some examples of prompts:

[1205] "Detect audio of someone around you making coercive remarks."

[1206] Make sure the word "help" is included in your voice input.

[1207] "Detect text containing violent language and report the details."

[1208] 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.

[1209] The present invention provides a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger, thereby ensuring the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[1210] System Overview

[1211] This system consists of a "terminal," a "server," and an "emotion engine." The "terminal" corresponds to a device owned by the user (smartphone, tablet, dedicated device, etc.), and the "server" is a computer for data processing installed in a cloud environment. The "emotion engine" has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[1212] Program processing explanation

[1213] Audio input and preprocessing

[1214] The device uses a microphone to collect audio from the user's surroundings, converts the audio signal into a digital format, and then uses noise reduction and filtering to make it clearer. The audio data is then encoded and sent to a server using a secure communication protocol.

[1215] Speech recognition and text conversion

[1216] The server decodes the received voice data and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice data into text data with high accuracy. The converted text data is then passed on to the next processing stage.

[1217] Natural Language Processing and Anomaly Detection

[1218] The server analyzes the text data using natural language processing (NLP) algorithms. It performs syntax analysis, semantic analysis, and sentiment analysis to detect coercive language and violent behavior. If an anomaly is detected based on the results of this analysis, detailed information is sent to the device.

[1219] Emotion recognition by emotion engine

[1220] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes voice parameters such as tone, pitch, and tempo to recognize emotions such as anger, sadness, and joy. This information is returned to the server and reflected in the anomaly detection process.

[1221] Sending a warning

[1222] If an abnormality is detected, the device will issue a warning to the user and the other party. For voice warnings, a speech synthesis engine is used to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior" and play it through the speaker. For text warnings, a message is displayed on the display. It is also possible to issue different warning messages depending on the user's emotional state using an emotion engine.

[1223] Start recording and recording

[1224] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time and temporarily stored in local memory for later analysis or as evidence.

[1225] Report to the police

[1226] If the abnormality is deemed serious, the server automatically notifies the police. At this time, the user's location information is obtained from GPS data, and an emergency call is made containing the current situation and evidence data (audio and video), allowing the police to respond quickly.

[1227] Specific examples

[1228] Suppose the user is a train station employee and is talking to another person. The other person suddenly says something overbearing like, "What are you looking at?" This speech is collected by the device and sent to the server. The server then converts it into text using a speech recognition engine, detecting the "overbearing language." At the same time, an emotion engine recognizes the user's anger. Having detected an abnormality, the device issues an audio warning to the other person saying, "This conversation is being recorded. Please refrain from inappropriate behavior." At the same time, audio and video recording begins, and the data is saved in cloud storage. If necessary, the server can send the user's location information and evidence data to the police, enabling a prompt response.

[1229] summary

[1230] This invention is a system that combines speech recognition and natural language processing technologies, and also introduces an emotion engine to detect abnormalities in real time and ensure user safety. This system allows users to quickly detect signs of trouble or crime and take appropriate measures.

[1231] The processing flow will be explained below.

[1232] Step 1:

[1233] The device uses a microphone to collect audio signals from the user's surroundings, converts them into a digital format, and performs real-time pre-amplification, noise reduction, and filtering to improve sound quality.

[1234] Step 2:

[1235] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., HTTPS). The data is divided into packets and transmitted efficiently over the network.

[1236] Step 3:

[1237] The server decodes the received voice data and passes it to a speech recognition engine, which uses a deep learning model to convert the voice signal into text data, which is then immediately stored in a buffer for NLP analysis.

[1238] Step 4:

[1239] The server analyzes the text data using natural language processing (NLP) algorithms. Syntax analysis, semantic analysis, and sentiment analysis are performed in succession to detect abnormal behavior (such as aggressive language or violent expressions). This analysis involves analyzing the frequency of occurrence of specific keywords and phrases and context.

[1240] Step 5:

[1241] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine identifies the emotional state based on parameters such as tone, pitch, and tempo of the voice. The identified emotional state is sent to the server and reflected in the anomaly detection process.

[1242] Step 6:

[1243] If the server detects an anomaly based on the text and emotional state, it notifies the device with detailed information about the anomaly (e.g., "aggressive language" or "angry emotion") and a recommended next action (e.g., issuing a warning or starting audio or video recording).

[1244] Step 7:

[1245] Based on the notification received by the device, a warning is issued to the user and the other party. In the case of a voice warning, a voice synthesis engine is used to generate a message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," which is played through the speaker. If the emotion engine recognizes "anger," a warning with an even stronger tone is issued.

[1246] Step 8:

[1247] The device will begin recording audio and video, the device's video camera and microphone will be immediately turned on, and the collected data will be uploaded to cloud storage in real time, while also being temporarily stored in local memory for later analysis or as evidence.

[1248] Step 9:

[1249] If the abnormality is deemed serious, the server automatically notifies the police. The report includes the user's location (GPS data), current situation, and evidence data (audio and video), allowing the police to respond quickly.

[1250] Example 2

[1251] 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."

[1252] Conventional technologies have difficulty monitoring the user's surroundings in real time and quickly detecting abnormal behavior or danger. They also lack the ability to understand the user's emotional state through emotion analysis and respond appropriately based on that information. Furthermore, they lack the ability to record audio or video after detecting an abnormality, making it difficult to quickly report the incident to the police.

[1253] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, means for converting the collected voice data into a digital format and performing preprocessing, means for encoding the preprocessed voice data and transmitting it to the server, means for converting the received voice data into text, means for analyzing the text data and detecting anomalies, means for analyzing emotions when anomalies are detected, means for issuing an alarm when an anomaly is detected, means for starting audio and video recording when an anomaly is detected, and means for reporting to the police depending on the severity of the anomaly. This enables real-time monitoring of the user's surrounding environment and rapid detection of abnormal speech and behavior or danger. Furthermore, emotion analysis can be used to understand the user's emotional state in detail and to take appropriate action based on that information. Furthermore, audio and video recording is automatically performed after an anomaly is detected, enabling rapid reporting to the police if necessary.

[1254] "Audio Data" means a digital representation of sound collected using a Device's microphone.

[1255] "Converting to digital form" refers to converting collected audio data into a digital signal that can be analyzed and processed.

[1256] "Preprocessing" refers to processing to improve the quality of collected audio data by performing noise reduction and filtering.

[1257] "Encoding" means converting audio data into a specific format so that it can be sent to a server via a secure communications protocol.

[1258] "Speech recognition means" refers to a system that analyzes voice data and converts the voice into text.

[1259] "Natural language processing means" refers to algorithms or programs that analyze text data and detect abnormal behavior or danger from its content.

[1260] "Means for analyzing emotions" refers to algorithms or programs that analyze the tone, pitch, tempo, etc. of a user's voice to determine the user's emotional state.

[1261] The "means for issuing a warning" is a system that issues a warning message to the user and the other party by voice or text when an abnormality is detected.

[1262] The "means for starting audio and video recording" refers to a system that automatically activates the device's microphone and camera to collect audio and video data when an abnormality is detected.

[1263] The "means for reporting to the police" is a system that automatically reports to the police depending on the severity of the abnormality and provides the user's location information and collected evidence data.

[1264] The present invention relates to a system that monitors a user's voice environment in real time and detects abnormal speech and behavior or danger to ensure the user's safety. Furthermore, by incorporating an emotion engine, it is possible to recognize the user's emotional state and take appropriate action based on that. Below, an embodiment of the present invention will be described in detail.

[1265] System Overview

[1266] This system consists of a terminal, a server, and an emotion engine. The terminal is a device held by the user (smartphone, tablet, dedicated device, etc.), and the server is a computer for data processing installed in a cloud environment. The emotion engine has software and algorithms for recognizing the emotional state of the user from their voice and behavior.

[1267] Hardware and software used

[1268] The hardware used includes devices such as smartphones and tablets. These devices also have built-in microphones and cameras. Servers are located in a cloud computing environment and perform data processing and analysis. The software used includes audio editing software (e.g., Audacity), speech recognition engines (e.g., Google Cloud Speech-to-Text API), natural language processing tools (e.g., Spacy, NLTK), emotion engines (e.g., IBM Watson Tone Analyzer), and speech synthesis engines (e.g., Amazon Polly).

[1269] Audio input and preprocessing

[1270] The device uses a microphone to collect audio around the user. For example, if the user has a smartphone, the smartphone's built-in microphone is used. Since the collected audio is difficult to use as is, it is converted into a digital format and noise reduction and filtering are performed. This process may involve the use of audio input libraries such as "PulseAudio" or "ALSA." The cleared audio data is then sent to the server using a secure communication protocol (e.g., TLS / SSL).

[1271] Speech recognition and text conversion

[1272] The server decodes the voice data sent from the device and converts it into text using a speech recognition engine. This engine uses deep learning technology to convert voice into text data with high accuracy. For example, if a user says "Help!" to ask for help, the speech recognition engine generates the text "Help." This text data is passed to the next stage of natural language processing.

[1273] Natural Language Processing and Anomaly Detection

[1274] The server analyzes the text data using natural language processing algorithms. Tools such as Spacy and NLTK are used for analysis, and syntactic analysis, semantic analysis, and sentiment analysis are performed. For example, if the other person uses an overbearing phrase like "What are you looking at?", the server analyzes the text and detects the overbearing phrase. This information is used for anomaly detection, and detailed information about the detected anomaly is sent to the device.

[1275] Emotion recognition by emotion engine

[1276] The device sends the collected voice data to the emotion engine, which analyzes the user's emotional state in real time. The emotion engine analyzes parameters such as tone, pitch, and tempo of the voice to recognize emotions such as anger, sadness, and joy. For example, if the other person is angry, the emotion of anger is recognized. This information is sent back to the server and reflected in the anomaly detection process.

[1277] Sending a warning

[1278] If an abnormality is detected, the device will issue a warning to the user and the other party. A voice warning uses a speech synthesis engine to generate a message such as "This conversation is being recorded. Please refrain from inappropriate behavior." This message is played through the device's speaker. A text warning displays a message on the device's display.

[1279] Start recording and recording

[1280] If an abnormality is detected, the device will automatically start recording and the video camera and microphone will be immediately turned on to collect audio and video. For example, the device's camera and microphone will be activated and the audio and video from the scene will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[1281] Report to the police

[1282] If the abnormality is deemed serious, the server automatically notifies the police. The user's location information is obtained from GPS data, and an emergency call is made including the current situation and evidence data (audio and video). This allows the police to respond quickly. For example, if the server determines that the user is in danger, it immediately notifies the police and provides the necessary evidence along with the GPS location information.

[1283] Prompt Sentence Examples

[1284] Below are examples of prompt sentences that input voice data into a generative AI model to perform tasks that meet the following conditions:

[1285] The user is being monitored in real time for their current audio environment. Enter this audio data and perform a task that meets the following criteria:

[1286] 1. Convert audio data to text

[1287] 2. Analyze the converted text using natural language processing to detect abnormal behavior and dangerous language

[1288] 3. Analyze emotional states using an emotion engine

[1289] 4. If an abnormality is detected, a warning message is generated and notified to the user and the other party.

[1290] 5. If necessary, start recording and save to cloud storage.

[1291] 6. Automatically notify the police if a serious abnormality is detected

[1292] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1293] Step 1:

[1294] The device collects audio around the user using a microphone. This collected audio data is input to the device in analog format. An audio input library such as "PulseAudio" or "ALSA" is then used to convert this analog audio data into digital format. The result of this conversion process is digital audio data output.

[1295] Step 2:

[1296] The device preprocesses the digital audio data. Specifically, it uses software such as Audacity or Waves Noise Reduction to reduce noise and perform filtering. This preprocessing improves the quality of the audio data, making it easier to analyze. After preprocessing, the audio data is output as clear audio.

[1297] Step 3:

[1298] The device encodes the pre-processed audio data and sends it to the server using a secure communication protocol (e.g., TLS / SSL). This encoding process converts the audio data into a format that can be transferred efficiently and securely. The encoded audio data is then sent to the server.

[1299] Step 4:

[1300] The server decodes the encoded audio data it receives. The result of the decoding process is the original digital audio data. The reconstructed audio data is output.

[1301] Step 5:

[1302] The server uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to convert the decoded voice data into text. This process uses deep learning technology to convert voice into text with high accuracy. For example, if the voice input is "Help!", the text data "Help" is output.

[1303] Step 6:

[1304] The server analyzes the text data using natural language processing algorithms (e.g., Spacy, NLTK). The analysis includes syntactic analysis, semantic analysis, and sentiment analysis. For example, if text data containing the overbearing phrase "What are you looking at!" is input, it will detect this as dangerous behavior. The analysis results will output information indicating that abnormal behavior has been detected.

[1305] Step 7:

[1306] The device uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from voice data in real time. The emotion engine analyzes parameters such as voice tone, pitch, and tempo to identify emotions such as anger, sadness, and joy. For example, if anger is detected, the emotion information for "anger" is output and sent to the server.

[1307] Step 8:

[1308] If the device detects an abnormality, it will issue a warning to the user and the other party. It uses a speech synthesis engine (e.g., Amazon Polly) to generate a voice warning message such as, "This conversation is being recorded. Please refrain from inappropriate behavior." The generated voice message is played from the device's speaker. At the same time, a text message is also displayed on the display.

[1309] Step 9:

[1310] If the device detects an abnormality, it will automatically start recording. At this time, the video camera and microphone will immediately turn on to collect audio and video from the scene. The data obtained through recording will be uploaded to cloud storage in real time. This data may also be temporarily stored in local memory.

[1311] Step 10:

[1312] The server will then notify the police depending on the severity of the anomaly. The user's location is retrieved from GPS data, and an emergency call is sent, including audio and video evidence, allowing the police to respond quickly.

[1313] Through the above processing steps, this system is able to monitor the user's surrounding environment in real time, quickly and accurately detect abnormal behavior or danger, and take appropriate action.

[1314] (Application example 2)

[1315] 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."

[1316] In modern brick-and-mortar stores, troubles and dangerous situations can arise between customers and staff, or between customers themselves. If such situations escalate, they can not only disrupt store operations but also pose significant risks to both parties. Conventional surveillance cameras and alarm systems have difficulty detecting signs of trouble in advance and lack the means to respond immediately. Therefore, there is a need for a system that can detect abnormal behavior and heightened emotions in real time and take appropriate measures.

[1317] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting voice data, voice recognition means for converting the collected voice data into text, natural language processing means for analyzing the text data and detecting abnormalities, emotion recognition means for analyzing the text data and recognizing the user's emotional state, means for issuing an alert when an abnormality is detected, means for starting audio and video recording when an abnormality is detected, means for saving the collected voice data and video recording data in a cloud environment, and means for reporting to the police depending on the severity of the abnormality. This makes it possible to detect abnormal behavior or dangerous situations in the store in real time and immediately issue an alert to prevent trouble and secure necessary evidence data.

[1318] "Audio data" refers to data in which sounds present around the user are recorded in digital format.

[1319] "Speech recognition means" refers to the technology or device used to convert collected voice data into text.

[1320] "Natural language processing tools" refer to techniques and algorithms for analyzing text data and extracting grammatical and semantic information.

[1321] "Emotion recognition means" refers to technologies and algorithms for analyzing voice data and text data to recognize a user's emotional state.

[1322] "Means for issuing a warning when an abnormality is detected" refers to a system or function for notifying users or other parties of a warning in real time.

[1323] "Means for starting audio and video recording when an abnormality is detected" refers to a function or device for automatically recording audio and video when an abnormality occurs.

[1324] "Means of storing data in a cloud environment" refers to the technology and systems used to store collected data on a remote server via the Internet.

[1325] "Means for reporting to the police" refers to a system or function that automatically reports to the police in an emergency depending on the severity of the abnormality.

[1326] This invention provides a system that detects abnormal behavior or danger in real time during interactions between customers and staff, or between customers themselves, in a brick-and-mortar store, and responds appropriately. This system detects abnormalities by collecting and analyzing voice data and performing emotion recognition, issues a warning, starts recording or filming as necessary, stores the data in a cloud environment, and can even notify the police in some cases.

[1327] Program processing overview

[1328] The system uses the following main hardware and software:

[1329] Hardware:

[1330] Smartphone (microphone, camera)

[1331] Cloud Server

[1332] Smart device (if needed)

[1333] software:

[1334] Speech recognition engine (e.g., Google Cloud Speech-to-Text API, Amazon Transcribe)

[1335] Natural language processing engines (e.g., Google Cloud Natural Language API, spaCy)

[1336] Emotion recognition engine (e.g. IBM Watson Tone Analyzer, Microsoft Emotion API)

[1337] Real-time communication protocols (e.g. web sockets, MQTT)

[1338] The server first collects voice data from within the physical store using the smartphone's microphone. The collected voice data is sent to the cloud server in real time. The cloud server then converts the voice data into text using a voice recognition engine. Next, it uses natural language processing to analyze the text data and extract grammatical and semantic information. It also uses an emotion recognition engine to evaluate the user's emotional state from the text and voice data and detect abnormalities. If an abnormality is detected, the server issues a warning to the user and the other party in real time, starts audio and video recording as necessary, and saves the data in cloud storage. It can also automatically notify the police depending on the severity of the abnormality.

[1339] Specific examples

[1340] For example, consider the case where one afternoon in a brick-and-mortar store, Customer A yells at Staff B, "Why are you treating me like that?" The customer's voice is collected by the smartphone's microphone and sent to a cloud server. A speech recognition engine converts the speech into text, and natural language processing detects overbearing language. At the same time, an emotion recognition engine recognizes Customer A's anger. This triggers the system to issue a warning message saying, "This conversation is being recorded. Please refrain from inappropriate behavior," and begin recording and filming. This data is then stored in cloud storage, and in some cases, the police may be notified.

[1341] Prompt Sentence Examples

[1342] Please explain your system for detecting abnormal speech and behavior or danger. This system is used in physical stores to prevent trouble between customers and staff, or between customers. It combines speech recognition, natural language processing, and emotion recognition to detect abnormalities and, if necessary, issue a warning or record audio or video. Please also provide detailed descriptions of the application you have created and the hardware and software you use.

[1343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1344] Step 1:

[1345] The device collects the user's surrounding sounds using a microphone, converts the collected audio data into a digital format, and applies a noise-canceling filter. The input is the surrounding audio, and the output is the noise-removed digital audio data.

[1346] Step 2:

[1347] The device encodes the noise-removed digital audio data and sends it to the cloud server using a secure communication protocol (HTTPS / WebSocket).The input is the noise-removed digital audio data, and the output is the encoded audio data sent to the cloud server.

[1348] Step 3:

[1349] The server decodes the received encoded voice data and converts it into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API). The input is the encoded voice data, and the output is the converted text data.

[1350] Step 4:

[1351] The server analyzes the text data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API, spaCy), extracts grammatical and semantic information, and detects anomalies. The input is the text data, and the output is the analyzed text data and the results of anomaly detection.

[1352] Step 5:

[1353] The server analyzes the analyzed text data using an emotion recognition engine (e.g., IBM Watson Tone Analyzer, Microsoft Emotion API) to evaluate the user's emotional state. The input is the analyzed text data, and the output is the evaluation result of the user's emotional state.

[1354] Step 6:

[1355] If an anomaly is detected, the server sends a real-time warning to the device. It generates a warning message and displays it on the device's display or issues an audio warning through the speaker. The input is the result of anomaly detection and emotion evaluation, and the output is a warning notification.

[1356] Step 7:

[1357] The terminal automatically starts recording audio and video when an abnormality is detected. The input is a warning notification, and the output is audio and video data.

[1358] Step 8:

[1359] The device uploads the collected audio and video data to cloud storage and temporarily stores it in local memory as needed. The input is the audio and video data, and the output is the data stored in cloud storage.

[1360] Step 9:

[1361] The server automatically generates and sends an emergency call to the police, including the user's location information and collected evidence data (audio and video). The input is the result of anomaly detection and emotion evaluation, as well as location information. The output is the emergency call sent to the police.

[1362] 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.

[1363] 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.

[1364] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] 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).

[1369] 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.

[1370] 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."

[1371] 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.

[1372] 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).

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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.

[1383] The following is further disclosed regarding the above embodiment.

[1384] (Claim 1)

[1385] means for collecting audio data;

[1386] a speech recognition means for converting the collected speech data into text;

[1387] natural language processing means for analyzing the text data and detecting anomalies;

[1388] means for issuing an alert when an anomaly is detected;

[1389] means for initiating audio and video recording when an abnormality is detected;

[1390] A means of reporting the abnormality to the police depending on its severity;

[1391] A system including:

[1392] (Claim 2)

[1393] 10. The system of claim 1, wherein sentiment analysis is performed when detecting anomalies.

[1394] (Claim 3)

[1395] 10. The system of claim 1, wherein the audio data and the video data are stored in cloud storage.

[1396] "Example 1"

[1397] (Claim 1)

[1398] a terminal for collecting voice data;

[1399] means for converting the collected audio data into digital form and for performing noise reduction and filtering;

[1400] means for transmitting the preprocessed audio data to a server via a network;

[1401] a server including a speech recognition engine for converting voice data into text;

[1402] a natural language processing algorithm means for analyzing the textual data and detecting anomalies;

[1403] a terminal including a user interface for issuing an alert when an anomaly is detected;

[1404] means for initiating audio and video recording when an abnormality is detected;

[1405] A means of reporting the abnormality to the police depending on its severity;

[1406] A system including:

[1407] (Claim 2)

[1408] 10. The system of claim 1, wherein sentiment analysis is performed when detecting anomalies.

[1409] (Claim 3)

[1410] 10. The system of claim 1, wherein the audio data and the video data are stored in cloud storage.

[1411] "Application Example 1"

[1412] (Claim 1)

[1413] means for collecting audio data;

[1414] a speech recognition means for converting the collected speech data into text;

[1415] natural language processing means for analyzing the text data and detecting anomalies;

[1416] means for issuing an alert when an anomaly is detected;

[1417] means for initiating audio and video recording when an abnormality is detected;

[1418] A means of reporting the abnormality to the police depending on its severity;

[1419] A means for acquiring and notifying the user of location information when an abnormality occurs;

[1420] A means for storing data in cloud storage;

[1421] a means for analyzing and responding to voice data using a generative AI model;

[1422] a means for inputting a prompt statement to detect an anomaly;

[1423] A system including:

[1424] (Claim 2)

[1425] 10. The system of claim 1, wherein sentiment analysis is performed when detecting anomalies.

[1426] (Claim 3)

[1427] 10. The system of claim 1, wherein the audio data and the video data are stored in cloud storage.

[1428] "Example 2: Combining Emotion Engines"

[1429] (Claim 1)

[1430] means for collecting audio data;

[1431] means for converting the collected audio data into a digital format and pre-processing the data;

[1432] means for encoding the pre-processed audio data and transmitting it to a server;

[1433] a speech recognition means for converting received speech data into text;

[1434] natural language processing means for analyzing the text data and detecting anomalies;

[1435] a means for analyzing emotions when detecting anomalies; and

[1436] means for issuing an alert when an anomaly is detected;

[1437] means for initiating audio and video recording when an abnormality is detected;

[1438] A means of reporting the abnormality to the police depending on its severity;

[1439] A system including:

[1440] (Claim 2)

[1441] 10. The system of claim 1, wherein an appropriate warning message is generated and sent based on the results of sentiment analysis of the text and voice data.

[1442] (Claim 3)

[1443] 10. The system of claim 1, wherein the audio data and the video data are stored in cloud storage.

[1444] "Application example 2 when combining emotion engines"

[1445] (Claim 1)

[1446] means for collecting audio data;

[1447] a speech recognition means for converting the collected speech data into text;

[1448] natural language processing means for analyzing the text data and detecting anomalies;

[1449] emotion recognition means for analyzing the text data and recognizing the user's emotional state;

[1450] means for issuing an alert when an anomaly is detected;

[1451] means for initiating audio and video recording when an abnormality is detected;

[1452] A means for storing the collected audio data and video recording data in a cloud environment;

[1453] A means of reporting the abnormality to the police depending on its severity;

[1454] A system including:

[1455] (Claim 2)

[1456] 2. The system according to claim 1, wherein if an abnormality is detected, a warning is issued to the user and the other party in real time.

[1457] (Claim 3)

[1458] The system according to claim 1, further comprising means for storing audio data and recorded data in cloud storage. [Explanation of symbols]

[1459] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting audio data; a speech recognition means for converting the collected speech data into text; natural language processing means for analyzing the text data and detecting anomalies; means for issuing an alert when an anomaly is detected; means for initiating audio and video recording when an abnormality is detected; A means of reporting the abnormality to the police depending on its severity; A system including:

2. The system of claim 1 , further comprising: a sentiment analysis for detecting anomalies.

3. The system of claim 1 , wherein the audio data and the video data are stored in cloud storage.

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