System

Abstract

A system is provided.SOLUTION: A system including data acquisition means for acquiring data from a low-spec terminal, data transmission means for transmitting the data to a server, data analysis means for analyzing the data received by the server with generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for storing the generated dictionary data in a database and transmitting the dictionary data from the server to the terminal for use in the next data transmission, and encryption means for securely encrypting and decrypting the data and the dictionary data and performing communication.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] With the rapid miniaturization and widespread use of IoT devices in recent years, it is expected that as many as 29 billion devices will be in operation by 2030. However, the large amount of data generated by these devices will strain the capacity of communication routes, increasing the burden on power, channels, and human resources. Furthermore, advanced data compression is required to efficiently and securely transmit data from low-spec IoT devices. However, conventional data compression methods using generative AI require a long time for compression processing on the device side, making them unsuitable for low-spec devices. Therefore, improving the efficiency of communication routes and achieving data compression on low-spec devices are key challenges. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: A data acquisition means is provided to acquire data from a low-spec terminal, and a data transmission means is provided to transmit the acquired data to a server. The server side is provided with a data analysis means that analyzes the received data using generative AI and generates dictionary data to be used for compression. The generated dictionary data is stored in a database and transmitted from the server to the terminal for use the next time data is transmitted. Furthermore, an encryption means is provided to securely encrypt and decrypt the transmission and reception of data and dictionary data. In this way, advanced data compression using generative AI is performed on the server side even on low-spec terminals, achieving efficient data transmission while reducing communication capacity.

[0006] A "low-spec terminal" is a device with limited computing resources (CPU, memory, storage, etc.), and primarily refers to IoT devices and sensors.

[0007] "Data acquisition means" refers to a means that allows a terminal to collect data from the external environment, built-in sensors, etc.

[0008] "Data transmission means" refers to a communication module or protocol for transmitting data acquired by a terminal to a server.

[0009] A "server" is a computer system that processes and stores received data and performs tasks such as generating models and distributing dictionary data.

[0010] "Generative artificial intelligence" refers to AI technology that has the ability to analyze data and generate new models and dictionaries.

[0011] "Data analysis means" refers to means that has the function of analyzing received data using generative artificial intelligence and extracting its features.

[0012] "Dictionary data" is information that systematizes patterns and rules used for data compression, and is data that can be used for the next data compression.

[0013] The "dictionary data management means" is a means having the function of saving the generated dictionary data, managing it appropriately, and transmitting it to the terminal at the next communication.

[0014] The "encryption means" is a means having the function of encrypting and decrypting data and dictionary data in order to transmit and receive them securely.

[0015] A "database" is an information management system that systematically manages stored information and allows quick access when needed. [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] System Configuration

[0038] The system of the present invention is primarily composed of a low-spec terminal, a server, and a user interface. The low-spec terminal acquires data and transmits it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely.

[0039] Program processing overview

[0040] Terminal side processing

[0041] The device first acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[0042] Specific examples

[0043] The temperature sensor measures the room temperature every minute and stores this data in a buffer. New dictionary data is sent from the server, and the received dictionary data is decrypted. The acquired room temperature data is compressed using the decrypted dictionary data, and the compressed data is encrypted using RSA encryption. The encrypted data is then sent to the server.

[0044] Server-side processing

[0045] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts the data's characteristics and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is sent. The new dictionary data is also encrypted and sent to the device the next time communication occurs.

[0046] Specific examples

[0047] The server receives the data sent from the temperature sensor and stores it in storage. The received data is decrypted using AES encryption to restore the original room temperature data. This data is then passed to a generative AI system, which extracts periodic patterns and fluctuation characteristics. A new compression dictionary is generated based on the extracted characteristics and saved in the database as version 1.0. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0048] User operation and monitoring

[0049] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings. Furthermore, if any problems occur with data transmission or compression, users can be notified through the interface.

[0050] Specific examples

[0051] The user uses a smartphone app to monitor the data transmission status of the room temperature sensor in real time. The user can change the data acquisition frequency from 10 minutes to 5 minutes from the app's settings screen. If a communication error occurs, the user receives a notification from the app and retries.

[0052] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals, and performs advanced data compression and analysis on the server side, achieving effective communication.

[0053] The processing flow will be explained below.

[0054] Program processing steps

[0055] Terminal side processing

[0056] Step 1:

[0057] The device receives data from built-in sensors and external devices, such as current sensor readings and environmental data.

[0058] Step 2:

[0059] The acquired data is temporarily stored in a buffer. For example, room temperature data is acquired and temporarily stored in the device's memory.

[0060] Step 3:

[0061] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[0062] Step 4:

[0063] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[0064] Step 5:

[0065] The received dictionary data is used to compress the acquired data in the buffer. This compression can be performed even on low-spec devices by using a lightweight algorithm.

[0066] Step 6:

[0067] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[0068] Step 7:

[0069] The encrypted compressed data is sent to a server via the Internet or a dedicated communication protocol.

[0070] Server-side processing

[0071] Step 1:

[0072] The server receives the encrypted data sent from the device and temporarily stores it in storage.

[0073] Step 2:

[0074] The received data is decrypted to restore the original compressed data, for example, using RSA encryption to decrypt the data.

[0075] Step 3:

[0076] The decoded data is passed to a generative AI for data analysis, which extracts data features and generates dictionary data for use in the next data compression.

[0077] Step 4:

[0078] The generated dictionary data is saved in a database. This save is also version-controlled and registered as the latest dictionary data.

[0079] Step 5:

[0080] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent, for example, by encrypting it using AES encryption.

[0081] Step 6:

[0082] The new dictionary data is sent to the terminal at the next communication timing, and this data is used in the next compression process.

[0083] User operation and monitoring

[0084] Step 1:

[0085] Through the interface, users can check the data sent from the device to the server and the compression status in real time. For example, they can view the room temperature data history on a smartphone app.

[0086] Step 2:

[0087] Users use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings, for example, changing the data retrieval frequency from 10 minutes to 5 minutes.

[0088] Step 3:

[0089] If a communication error or compression failure occurs, the user is notified through the interface, for example, the app receives an error notification and can choose to retry.

[0090] Example 1

[0091] 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."

[0092] In conventional data collection systems, the challenge was to efficiently transmit data from low-spec devices to a server while keeping communication capacity low. Furthermore, the security of the transmitted data was not adequately ensured, resulting in a lack of confidentiality. This could lead to inefficiencies in the data collection and analysis process and higher costs.

[0093] 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.

[0094] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, data compression means for receiving the dictionary data at the terminal and compressing the acquired data, data compression means for compressing data acquired by the terminal using the decrypted dictionary data, data encryption means for encrypting the compressed data and transmitting it to the server, data recovery means for decrypting the received data at the server and generating the original data, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This enables fast and efficient data transmission from the low-spec terminal and secure data communication while reducing communication capacity.

[0095] A "low-spec device" is an electronic device that has limited processing power but is equipped with sensors and data collection functions to acquire and transmit data.

[0096] "Data acquisition means" is a function that collects data from sensors and external devices and temporarily stores it in a buffer inside the terminal.

[0097] The "data transmission means" is a communication function for transmitting data collected by the terminal to the server.

[0098] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for data compression.

[0099] The "dictionary data management means" is a management function for storing the generated dictionary data in a database and transmitting it to the terminal the next time data is transmitted.

[0100] The "data compression means" is a function that compresses data acquired by the terminal using dictionary data.

[0101] The "data encryption means" is a function that encrypts the compressed data in a secure manner and transmits it to the server.

[0102] The "data restoration means" is a function that decrypts data received by the server and restores the original data.

[0103] The "encryption means" is a function for securely encrypting and decrypting data and dictionary data for communication.

[0104] "Generative AI" is an AI technology that analyzes the characteristics of data and generates new data compression dictionaries.

[0105] The system of the present invention achieves efficient and secure data collection and transmission using low-spec terminals, a server, and a user interface. This system uses the following hardware and software.

[0106] Terminal side processing

[0107] The device acquires data from built-in sensors such as a temperature sensor and external devices. For example, the temperature sensor measures the room temperature every minute and temporarily stores this data in a buffer within the device. The device then waits for new dictionary data to be sent from the server and decrypts the received dictionary data using RSA encryption. The decrypted dictionary data is used to compress the acquired temperature data, and the compressed data is then encrypted using the same RSA encryption. The encrypted data is then sent from the device to the server.

[0108] Server-side processing

[0109] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted using AES encryption to restore the original data. The restored data is then analyzed using generative artificial intelligence to extract data characteristics. New dictionary data is generated based on these characteristics and stored in the database. When the next communication is scheduled, this dictionary data is encrypted using RSA encryption and sent back to the device.

[0110] User operation and monitoring

[0111] Users can check the data being sent from their device to the server and its compression status in real time through an interface, such as a smartphone app. Users can also change data retrieval frequency and encryption settings from the app's settings screen. In addition, if a communication error occurs, users will receive a notification from the app and can retry.

[0112] Through each of the above stages, this system efficiently and securely transmits data from low-spec devices, and performs advanced data compression and analysis on the server side to achieve effective communication.

[0113] Specific examples

[0114] The temperature sensor measures the room temperature every minute and stores the data in a buffer. New dictionary data is sent from the server, and the device receives and decrypts the dictionary data. The acquired room temperature data is compressed using the dictionary data and encrypted with RSA encryption. The encrypted data is then sent to the server.

[0115] The server receives the data and temporarily stores it in storage. It decrypts it using AES encryption to restore the original data. It then uses generative artificial intelligence to extract features from the data, generates a new compressed dictionary, and stores it in the database. The dictionary data is then encrypted at the next communication time and sent to the device.

[0116] The user monitors the data transmission status of the room temperature sensor in real time using a smartphone app. The user can change the data acquisition frequency from the app's settings screen, and if a communication error occurs, they will receive a notification and try again.

[0117] Prompt Sentence Examples

[0118] Design a system with the following data processing flow:

[0119] 1. The device acquires data from the sensor and stores it in a buffer.

[0120] 2. The device receives the new dictionary data from the server and decrypts it.

[0121] 3. The data acquired by the terminal is compressed using dictionary data and encrypted with RSA.

[0122] 4. The device sends the encrypted data to the server.

[0123] 5. The server stores the received data in storage and decrypts it using AES encryption.

[0124] 6. The server passes the original data to the generation AI, which extracts features and generates new dictionary data.

[0125] 7. The generated dictionary data is encrypted and sent to the device during the next communication.

[0126] 8. The user monitors the data transmission and compression status through the interface and changes the settings as necessary.

[0127] Please design a specific system based on this flow.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] The flow of this system's program processing

[0130] Terminal side processing

[0131] Step 1:

[0132] The terminal acquires data from sensors and external devices. Specifically, the temperature sensor measures the room temperature every minute and stores this data in a buffer. The input is an analog signal from the temperature sensor, and the output is digital data stored in the buffer. For example, data for a room temperature of 25°C is acquired and stored in the buffer.

[0133] Step 2:

[0134] The terminal waits for new dictionary data to be sent from the server. When the dictionary data arrives, it is decrypted using RSA encryption. The input is the encrypted dictionary data sent from the server, and the output is the decrypted dictionary data. Specifically, let XYZ be the dictionary data decrypted by RSA encryption.

[0135] Step 3:

[0136] The terminal compresses the acquired data using the received dictionary data. The input is the room temperature data in the buffer and the decoded dictionary data XYZ, and the output is the compressed data. Specifically, the room temperature data of 25°C is compressed using the dictionary data XYZ to generate the compressed data ABCDE.

[0137] Step 4:

[0138] The terminal encrypts the compressed data using RSA encryption. The input is compressed data ABCDE, and the output is encrypted data FGHIJ. The compressed data ABCDE is encrypted using RSA encryption to generate data FGHIJ.

[0139] Step 5:

[0140] The terminal sends encrypted data to the server. The input is the encrypted data FGHIJ, and the output is the data to be sent to the server. The terminal sends data by specifying the IP address and port number.

[0141] Server-side processing

[0142] Step 6:

[0143] The server receives the data sent from the terminal and temporarily stores it in storage. The input is the encrypted data FGHIJ sent from the terminal, and the output is the encrypted data stored in storage. The server temporarily stores the data and stores it securely.

[0144] Step 7:

[0145] The server decrypts the received data and restores the original data. The input is the encrypted data FGHIJ stored in storage, and the output is the restored room temperature data of 25°C. The data FGHIJ is decrypted using AES encryption, and the original room temperature data of 25°C is restored.

[0146] Step 8:

[0147] The server analyzes the restored data using generative artificial intelligence and extracts data features. The input is room temperature data of 25°C, and the output is the extracted data features. The generative artificial intelligence analyzes periodic patterns and fluctuation features to generate feature data.

[0148] Step 9:

[0149] The server generates new dictionary data based on the features and saves it in the database. The input is the extracted data features, and the output is the generated new dictionary data XYZ+1. The server creates a new compression dictionary and saves it in the database.

[0150] Step 10:

[0151] The server encrypts the new dictionary data and sends it to the terminal at the next communication timing. The input is the generated dictionary data XYZ+1, and the output is the encrypted dictionary data. The dictionary data XYZ+1 is encrypted using RSA encryption and sent to the terminal at the next communication timing.

[0152] User operation and monitoring

[0153] Step 11:

[0154] Through the interface, the user can check the data sent from the device to the server and its compression status in real time. The input is the data transmission status from the server, and the output is the information displayed on the user's interface screen. The user monitors the data transmission status of the room temperature sensor using a smartphone app.

[0155] Step 12:

[0156] The user uses the interface to change the data capture frequency and encryption settings. The input is the setting change instruction entered by the user into the app, and the output is the changed data capture frequency and encryption settings. For example, the user changes the data capture frequency from 10 minutes to 5 minutes.

[0157] Step 13:

[0158] If a communication error occurs, the user is notified through the interface. The input is the information about the communication error, and the output is a notification displayed on the user's interface. When the user receives the notification, they can press the retry button to try to resend the data.

[0159] (Application example 1)

[0160] 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."

[0161] In communication systems that use low-spec terminals, transmitting data efficiently and securely is a challenge. Surveillance systems also require video data to be compressed and encrypted and transmitted to a server in real time. However, performing such processing on low-spec terminals is technically difficult, so there is a need to improve the efficiency of data transmission while ensuring security.

[0162] 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.

[0163] In this invention, the server includes a data acquisition means for acquiring data from a low-spec terminal, a data transmission means for transmitting data to the server, a data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, a dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, an encryption means for securely encrypting and decrypting the data and dictionary data for communication, and a monitoring device means for a monitoring device to acquire video data, compress and encrypt it, and transmit it to the server. This enables efficient and secure data transmission even from low-spec terminals, and also enables real-time video data transmission and anomaly detection in a monitoring system.

[0164] A "low-spec device" is a device that has limited processing power and memory compared to typical high-performance devices.

[0165] "Data acquisition means" refers to methods and devices for collecting information from devices such as various sensors and cameras.

[0166] "Data transmission means" refers to a method or apparatus for transferring acquired data to a server or other device.

[0167] A "server" is a computer system that provides services to client devices over a network.

[0168] "Generative AI" is an AI technology that has the ability to analyze data and generate new data.

[0169] A "data analysis means" is a method or device for processing received data and extracting useful information.

[0170] "Dictionary data" is a database for efficiently handling specific patterns and expressions in data compression and data management.

[0171] The "dictionary data management means" refers to a method or device for storing the generated dictionary data and using it as needed.

[0172] A "cryptographic means" is a method or device for encrypting and decrypting data for secure communication.

[0173] A "surveillance device" is a device such as a camera or sensor used to monitor a specific area.

[0174] A "compression means" is a method or device for compressing data to reduce the volume of the data.

[0175] An "encryption means" is a method or device for encrypting data to protect it from third parties.

[0176] A "decryption means" is a method or device for restoring encrypted data to its original form.

[0177] "Adaptively repeating the transmission and reception of dictionary data with the data transmission means" refers to a process of efficiently transmitting and receiving data according to the network state and the properties of the data.

[0178] "Monitoring devices acquire video data in real time, compress, encrypt, and transmit it, and then analyze the data to detect abnormalities" refers to the process of instantly processing video data acquired by devices such as surveillance cameras to check for safety and abnormalities.

[0179] A system for implementing the present invention comprises a low-spec terminal, a server, and a monitoring device.

[0180] Hardware and software used

[0181] The present invention uses the following hardware and software.

[0182] Hardware: Low-spec devices (e.g., IoT devices and simple sensors), servers, and surveillance devices (e.g., surveillance cameras).

[0183] Software: Python, OpenCV, RSA, PyCryptodome, Requests library.

[0184] Program processing overview

[0185] Terminal side processing

[0186] The device first acquires data from built-in sensors and monitoring devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[0187] Processing on the monitoring device

[0188] The surveillance device (surveillance camera) captures video data in real time. This video data is temporarily stored in a buffer. The stored video data is compressed and encrypted in the same way as on low-spec devices. Dictionary data sent from the server is used for compression, and AES and RSA are used for encryption. The encrypted video data is then sent to the server.

[0189] Server-side processing

[0190] The server receives data sent from the terminals and monitoring devices and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts features from the data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is transmitted. The new dictionary data is also encrypted and sent to the terminals and monitoring devices at the next communication timing.

[0191] Specific examples

[0192] For example, suppose a surveillance camera monitors an office entrance in real time and sends the video data to a server. This data is temporarily stored in a buffer and compressed using dictionary data sent from the server. The compressed data is encrypted with AES encryption, and the AES key is encrypted with RSA encryption. The encrypted data is sent to the server, where it is decrypted and analyzed. New dictionary data generated based on the results of this analysis is used for the next communication.

[0193] Prompt Sentence Examples

[0194] "How can I compress and encrypt surveillance camera data more efficiently?"

[0195] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals and monitoring devices, and performs advanced data compression and analysis on the server side, achieving effective communication.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The terminal acquires data from sensors and monitoring devices.

[0199] Input: Raw data from sensors and surveillance cameras (e.g., temperature data and video data).

[0200] Specific operation: The device reads data from the built-in sensors and camera and temporarily stores it in a buffer.

[0201] Output: Temporarily stored raw data.

[0202] Step 2:

[0203] The terminal receives the dictionary data from the server and decrypts it.

[0204] Input: Encrypted dictionary data sent from the server.

[0205] Specific operation: The terminal waits for dictionary data sent from the server, and when it receives it, it decrypts the dictionary data using RSA encryption.

[0206] Output: Decoded dictionary data.

[0207] Step 3:

[0208] The raw data acquired by the terminal is compressed using the decrypted dictionary data.

[0209] Input: Raw data, decoded dictionary data.

[0210] Specific operation: The terminal refers to the dictionary data and applies a compression algorithm to the raw data to reduce the data size.

[0211] Output: Compressed data.

[0212] Step 4:

[0213] The terminal encrypts the compressed data using AES encryption and sends it to the server.

[0214] Input: Compressed data, AES key.

[0215] Specific operation: The terminal encrypts the compressed data using the AES algorithm, and the AES key is re-encrypted with RSA before being sent to the server.

[0216] Output: The encrypted data and the encrypted AES key.

[0217] Step 5:

[0218] The server receives the encrypted data and stores it temporarily in storage.

[0219] Input: Encrypted data, Encrypted AES key.

[0220] Specific operation: The server receives the data sent from the terminal and stores it in storage.

[0221] Output: The stored encrypted data.

[0222] Step 6:

[0223] The server decrypts the received data and analyzes the restored data using generative artificial intelligence.

[0224] Input: Stored encrypted data, encrypted AES key.

[0225] How it works: The server decrypts the encrypted AES key with RSA, then uses that AES key to decrypt the stored data, and then passes the recovered data to a generative AI for analysis.

[0226] Output: Analysis results and feature extraction data.

[0227] Step 7:

[0228] The server generates new dictionary data based on the analysis results and stores it in the database.

[0229] Input: Analysis results, feature extraction data.

[0230] Specific operation: The server creates new dictionary data based on the analysis results of the generative artificial intelligence, and stores the dictionary data in a database under version control.

[0231] Output: The generated dictionary data.

[0232] Step 8:

[0233] The server encrypts the new dictionary data and sends it to the device the next time it communicates.

[0234] Input: The generated dictionary data.

[0235] Specific operation: The server encrypts the new dictionary data with AES, encrypts the AES key with RSA, and then sends it to the terminal during the next data communication.

[0236] Output: Encrypted dictionary data.

[0237] In this way, the system of the present invention ensures efficient and secure transmission of data through processing steps, and also leverages generative artificial intelligence to improve data compression efficiency and enable real-time analysis of video data from surveillance devices.

[0238] 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.

[0239] System Configuration

[0240] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[0241] Program processing overview

[0242] Terminal side processing

[0243] The device acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. In addition, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The recognized emotion data is also stored in a buffer. The device then waits for new dictionary data sent from the server and decrypts it when it receives it. The acquired data and emotion data are compressed and encrypted using the received dictionary data. The encrypted data is then sent to the server.

[0244] Specific examples

[0245] The temperature sensor measures the room temperature and stores this data in a buffer. At the same time, the emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. The device then receives new dictionary data from the server and decrypts it. The temperature and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[0246] Server-side processing

[0247] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is passed to the generative AI for analysis. The AI ​​extracts the characteristics of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used for the next data transmission. The dictionary data is then encrypted and sent to the device at the next communication timing.

[0248] Specific examples

[0249] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and saved as version 1.0 in the database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0250] User operation and monitoring

[0251] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, users will be notified through the interface if any problems occur with data transmission or compression.

[0252] Specific examples

[0253] Users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine in real time. They can change the emotion recognition settings of the emotion engine from the app's settings screen, changing the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them and they can take appropriate measures.

[0254] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, achieving effective communication and emotion recognition.

[0255] The processing flow will be explained below.

[0256] Program processing steps

[0257] Terminal side processing

[0258] Step 1:

[0259] The device collects data from built-in sensors, such as a temperature sensor that measures the current room temperature in real time.

[0260] Step 2:

[0261] The device activates an emotion engine and analyzes the user's facial expressions and voice data to recognize their emotions. This emotion data is labeled as "happy" or "sad."

[0262] Step 3:

[0263] The acquired data and emotion data are temporarily stored in a buffer. For example, room temperature data and emotion data such as "happy" are stored.

[0264] Step 4:

[0265] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[0266] Step 5:

[0267] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[0268] Step 6:

[0269] The received dictionary data is used to compress the acquired data and emotion data in the buffer. For example, the dictionary data is used to compress the data using the LZMA algorithm.

[0270] Step 7:

[0271] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[0272] Step 8:

[0273] The encrypted compressed data is sent to the server using either HTTP over the Internet or a dedicated protocol.

[0274] Server-side processing

[0275] Step 1:

[0276] The server waits for the encrypted data sent from the device and receives it when it arrives. The received data is temporarily stored in storage.

[0277] Step 2:

[0278] The received encrypted data is decrypted, for example, using RSA encryption.

[0279] Step 3:

[0280] The decoded data is passed to a generative AI for analysis, which extracts features from the data and emotion data and generates dictionary data for use in the next compression.

[0281] Step 4:

[0282] The generated dictionary data is saved in the database. This save also performs version control and is used as the latest dictionary data.

[0283] Step 5:

[0284] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent. For example, encryption is performed using AES encryption.

[0285] Step 6:

[0286] At the next communication timing, new dictionary data is sent to the terminal, and the dictionary data is used for the next data compression.

[0287] User operation and monitoring

[0288] Step 1:

[0289] Through the interface, users can check the data sent from their device to the server and its compression status in real time, for example by viewing the data transmission history on a smartphone app.

[0290] Step 2:

[0291] The user uses the interface to change basic settings such as compression method, data capture frequency, encryption settings, emotion recognition settings for the emotion engine, etc. For example, setting the sensitivity of the emotion engine.

[0292] Step 3:

[0293] If a communication error or compression failure occurs, the user is notified through the interface, for example, by receiving an error message via a push notification in the app.

[0294] This series of processing steps enables data containing emotional data to be sent efficiently and securely even on low-spec devices. The server analyzes the data, generates dictionary data that will be useful for the next data compression, and distributes it to the device. Users can check the data status in real time and make necessary setting changes to optimize system performance.

[0295] Example 2

[0296] 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."

[0297] In conventional low-spec devices, it has been difficult to efficiently and securely acquire and transmit data due to limited resources. Furthermore, even in systems that acquire user emotion data in real time and analyze it on the server side, many issues remain, such as improving data compression efficiency and reducing communication capacity. The present invention aims to solve these issues by providing a system that achieves effective communication and emotion recognition by efficiently and securely transmitting data and emotion data acquired from low-spec devices and performing advanced data analysis and compression on the server side.

[0298] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0299] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, emotion data acquisition means for recognizing user emotions using an emotion engine and acquiring the data, data transmission means for transmitting data to the server, data analysis means for analyzing data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, compression means for compressing the data and emotion data acquired by the terminal using the dictionary data received from the server, encryption means for securely encrypting and decrypting the data and dictionary data for communication, and user interface means for allowing the user to monitor the operating status of the terminal and server in real time and change settings. This enables simultaneous acquisition of data and emotion data from the low-spec terminal, realizes effective data analysis and compression on the server side, and enables highly efficient data transmission while reducing communication capacity.

[0300] A "low-spec terminal" is a computing device that has limited computing power and memory, but is equipped with sensors and basic input means.

[0301] "Data acquisition means" is a function that allows a terminal to collect data from sensors and external devices and temporarily store it in a buffer.

[0302] The "emotion data acquisition means" is a function that collects emotion data in real time using an emotion engine that recognizes the user's emotions.

[0303] The "data transmission means" is a communication function for transmitting data acquired by the terminal to the server.

[0304] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for future data compression.

[0305] The "dictionary data management means" is a function that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[0306] "Compression means" is a function that efficiently compresses the data and emotion data acquired by the terminal.

[0307] The "encryption means" is a function that securely encrypts data and dictionary data and decrypts received data.

[0308] The "user interface means" is an interface that allows the user to monitor the operating status of the terminal and server in real time and change various settings.

[0309] "Generative AI" is an AI technology that analyzes incoming data, extracts its features, and uses them for future data compression and analysis.

[0310] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[0311] The device acquires data from built-in sensors and external devices. For example, a DHT11 temperature sensor can be used. This data is temporarily stored in a buffer. The device also has an emotion engine that recognizes the user's emotions in real time. The emotion engine can use the OpenCV library. The recognized emotion data is also stored in the buffer.

[0312] Next, the device waits for new dictionary data to be sent from the server. Upon receiving it, it decrypts the RSA encryption using the OpenSSL library. The acquired data and emotion data are compressed using the received dictionary data. The LZ77 algorithm can be used for compression. The data is then RSA encrypted again using the OpenSSL library. The encrypted data is then sent to the server.

[0313] The server receives data sent from the device using the HTTPS protocol and temporarily stores it in storage. The storage used here could be, for example, an AWS (registered trademark) S3 bucket. The server then decrypts the received data and restores the original data. This decryption also uses the OpenSSL library. The restored data is passed to a generative AI system. This AI system can use a custom model based on TENSORFLOW (registered trademark). The AI ​​extracts features from the data and emotion data, and generates dictionary data to be used for the next data compression. The generated dictionary data is stored in a PostgreSQL database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0314] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. This interface uses a React.js-based web application. Users can also use the interface to change basic settings such as the compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, if any problems occur with data transmission or compression, users will be notified via Firebase Cloud Messaging.

[0315] For example, a temperature sensor measures the room temperature and stores this data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and generates emotion data, such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives new dictionary data from the server and decrypts it. The temperature data and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[0316] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and stored in a database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0317] An example prompt might be:

[0318] Please update the information as follows to generate the latest dictionary data:

[0319] Acquired temperature data: 22.5℃

[0320] Recognized emotion data: Happy

[0321] Acquisition frequency: Every 5 minutes

[0322]

[0323] Adjust the settings for receiving the latest dictionary data from the server, compressing and encrypting the data before sending it:

[0324] RSA encryption key: 2048 bits

[0325] Compression algorithm: LZ77

[0326] Communication protocol: HTTPS

[0327] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Step 1: Get the data

[0330] The device acquires data from built-in sensors (e.g., temperature sensor DHT11) and external devices.

[0331] Input: Room temperature data measured by a temperature sensor

[0332] Data processing: Temperature data is saved in a buffer

[0333] Output: Buffered room temperature data

[0334] Specific operation: The temperature sensor measures the room temperature as 22.5°C and stores the data in the terminal's buffer.

[0335] Step 2: Recognizing Emotional Data

[0336] The device uses an emotion engine (e.g., OpenCV library) to recognize the user's emotions in real time.

[0337] Input: User's facial expression data

[0338] Data Computing: Recognizing Emotions from Facial Expressions Using OpenCV Library

[0339] Output: Recognized emotion data

[0340] Specific operation: The emotion engine analyzes the user's facial expression, generates emotion data of "happy," and stores this in a buffer.

[0341] Step 3: Receiving and Decoding Dictionary Data

[0342] The terminal waits for new dictionary data to be sent from the server.

[0343] Input: Encrypted dictionary data sent from the server

[0344] Data Calculation: Decrypting dictionary data using the OpenSSL library

[0345] Output: Decoded dictionary data

[0346] Specific operation: The terminal receives the dictionary data using the HTTPS protocol, decrypts the RSA encryption, and loads the dictionary data into memory.

[0347] Step 4: Compress the data

[0348] The terminal compresses the acquired data and emotion data using the received dictionary data.

[0349] Input: Room temperature data and emotion data stored in the buffer, decoded dictionary data

[0350] Data operation: Compress data using the LZ77 algorithm

[0351] Output: Compressed data

[0352] Specific operation: Temperature data and emotion data are efficiently compressed using the LZ77 algorithm to generate compressed data.

[0353] Step 5: Encrypt the data

[0354] The terminal encrypts the compressed data.

[0355] Input: Compressed data

[0356] Data Computation: RSA encryption using the OpenSSL library

[0357] Output: Encrypted data

[0358] Specific operation: RSA encrypts the compressed data to generate encrypted data.

[0359] Step 6: Sending data

[0360] The terminal transmits the encrypted data to the server.

[0361] Input: Encrypted data

[0362] Data processing: Send data using HTTPS protocol

[0363] Output: Data sent to the server

[0364] What it does: Encrypts data and sends it to the server using the HTTPS protocol.

[0365] Step 7: Receiving Data

[0366] The server receives the data sent from the terminal and temporarily stores it in storage.

[0367] Input: Encrypted data sent from the device

[0368] Data processing: Save to storage

[0369] Output: Encrypted data stored in storage

[0370] Specific operation: The server saves the received data to an AWS S3 bucket using the HTTPS protocol.

[0371] Step 8: Decrypt and recover data

[0372] The server decrypts the received data and restores the original data.

[0373] Input: Encrypted and stored data

[0374] Data Computing: Decrypting RSA Encryption Using the OpenSSL Library

[0375] Output: Recovered room temperature data and emotion data

[0376] Specific operation: Decrypt the RSA encryption and restore the original temperature data (note: 22.5°C) and emotion data (e.g., "happy").

[0377] Step 9: Analyze the data and generate dictionary data

[0378] The server extracts features from the data and emotional data passed to the generative AI and generates new dictionary data.

[0379] Input: Recovered room temperature data and emotion data

[0380] Data Computation: Extracting Periodic Patterns and Emotional Fluctuations with TensorFlow

[0381] Output: New compression dictionary

[0382] How it works: The TensorFlow model analyzes periodic patterns and emotional fluctuations to generate new dictionary data.

[0383] Step 10: Preparing to save and send dictionary data

[0384] The server stores the generated dictionary data in a database, encrypts it, and prepares to send it to the terminal at the next communication timing.

[0385] Input: New dictionary data

[0386] Data processing: Dictionary data is stored in a database and encrypted.

[0387] Output: Encrypted dictionary data

[0388] What it does: It saves the new dictionary data in a PostgreSQL database and encrypts it using the OpenSSL library.

[0389] Step 11: Monitor users and change settings

[0390] The user monitors the operating status of the terminal and server through the interface and changes the settings as necessary.

[0391] Input: Real-time terminal and server activity

[0392] Data Calculation: Changing various settings

[0393] Output: Changed settings

[0394] What it does: Use a React.js-based web application to change data collection frequency, emotion recognition settings, and receive notification of communication errors.

[0395] (Application example 2)

[0396] 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."

[0397] Conventional systems that collect data from low-spec devices and analyze it on a server have had problems with data compression efficiency and communication costs. Furthermore, to improve the user experience, it is necessary to collect and appropriately analyze user emotion data in real time, but current systems are also insufficient in this regard. To solve these issues, a data compression and analysis system incorporating emotion recognition technology is needed.

[0398] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0399] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, emotion recognition means for recognizing the user's emotions in real time, data compression means for combining the acquired emotion data and sensor data, compressing the data and transmitting it to the server, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This makes it possible to effectively collect emotion data while keeping communication costs down during data compression and transmission.

[0400] A "low-spec terminal" is an information processing device with limited resources such as computing power and memory capacity.

[0401] "Data acquisition means" refers to a function or device for collecting data from low-spec terminals.

[0402] "Data transmission means" refers to a function or device for transmitting collected data to a server.

[0403] "Data analysis means" refers to a function or device that uses generative artificial intelligence to analyze data received on the server side and create dictionary data to be used for compression.

[0404] The "dictionary data management means" is a function or device that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[0405] "Emotion recognition means" refers to a function or device for detecting and analyzing a user's emotions in real time.

[0406] "Data compression means" refers to a function or device that combines acquired emotion data and sensor data, compresses it efficiently, and transmits it to the server.

[0407] "Encryption means" refers to a function or device for securely encrypting and decrypting data and dictionary data for communication.

[0408] "Generative AI" is an AI that has the ability to analyze collected data and extract specific patterns and characteristics.

[0409] System Configuration

[0410] The system of the present invention includes a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires sensor data and emotion data and transmits it to the server. The system includes the following main components:

[0411] Low-spec device: Equipped with sensors and emotion engines to collect data.

[0412] Server: Analyzes the received data and generates dictionary data to be used for compression.

[0413] User interface: The user can check the data transmission status and emotion data, and change settings.

[0414] Emotion engine: Recognizes user emotions in real time and provides them as data.

[0415] Program processing overview

[0416] Terminal side processing

[0417] The device acquires data from its built-in sensors and emotion engine and temporarily stores it in a buffer. The acquired data receives dictionary data sent from the server, decrypts it, and then compresses it. The compressed data is encrypted using RSA encryption and sent to the server.

[0418] For example, a temperature sensor measures the room temperature and stores the data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives and decrypts new dictionary data sent from the server. The temperature and emotion data are efficiently compressed, then encrypted using RSA encryption and sent to the server.

[0419] Server-side processing

[0420] The server receives the data sent from the device and temporarily stores it in storage. It then decrypts the received data and restores the original data. The restored data is passed to a generative AI for analysis. The AI ​​extracts features of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and sent to the device the next time it communicates.

[0421] As a concrete example, the server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This is then passed to the generative AI, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated and stored in the database. This dictionary data is then sent to the device in time for the next communication.

[0422] User operation and monitoring

[0423] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings. Furthermore, if a problem occurs, users will receive notifications through the interface.

[0424] As a concrete example, users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine. They can change the emotion recognition settings of the emotion engine from the app's settings screen and change the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them so that they can take appropriate measures.

[0425] Prompt sentence for generative AI model

[0426] As a concrete example, the following prompt can be used, using emotion data and sensor information from a generative AI model:

[0427] "The room temperature data is 26 degrees and the user is smiling. What would you suggest in this case?"

[0428] "The user has a sad expression and the room temperature is 18 degrees. What promotion would work?"

[0429] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[0430] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0431] Step 1:

[0432] The device collects sensor data.

[0433] Input: Data from temperature sensor, camera, and microphone.

[0434] Specific operation: The temperature sensor measures the room temperature, the camera captures the user's facial expressions, and the microphone captures voice data.

[0435] Output: Sensor data and raw image and audio data.

[0436] Step 2:

[0437] The device collects emotional data.

[0438] Input: Raw image and audio data.

[0439] Specific operation: The emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[0440] Output: Emotion data (e.g., the user's emotional state, such as "happy" or "sad").

[0441] Step 3:

[0442] The device stores sensor data and emotion data in a buffer.

[0443] Input: Sensor data and emotion data.

[0444] Specific operation: The acquired sensor data and emotion data are temporarily stored in a buffer in memory.

[0445] Output: The data stored in the buffer.

[0446] Step 4:

[0447] The terminal receives the dictionary data from the server and decrypts it.

[0448] Input: Encrypted dictionary data sent from the server.

[0449] Specific operation: The device receives the encrypted dictionary data and decrypts it using the decryption key.

[0450] Output: Decoded dictionary data.

[0451] Step 5:

[0452] The device compresses sensor data and emotion data.

[0453] Input: Buffered data and decoded dictionary data.

[0454] Specific operation: Sensor data and emotion data are compressed using dictionary data.

[0455] Output: Compressed data.

[0456] Step 6:

[0457] The terminal encrypts the compressed data and sends it to the server.

[0458] Input: Compressed data.

[0459] Specific operation: The compressed data is encrypted using the RSA encryption method and sent to the server.

[0460] Output: The encrypted data sent to the server.

[0461] Step 7:

[0462] The server receives the data and stores it temporarily.

[0463] Input: Encrypted data sent from the terminal.

[0464] Specific operation: The server receives the data and temporarily stores it in storage.

[0465] Output: Encrypted data in storage.

[0466] Step 8:

[0467] The server decrypts and recovers the encrypted data.

[0468] Input: Encrypted data stored in storage.

[0469] Specific operation: The data is decrypted using the decryption key to restore the original sensor data and emotion data.

[0470] Output: The recovered data.

[0471] Step 9:

[0472] The server passes the restored data to a generative artificial intelligence for analysis.

[0473] Input: Recovered data.

[0474] How it works: Data is passed to a generative AI to extract cyclical patterns and emotional fluctuations.

[0475] Output: Analysis results and feature data.

[0476] Step 10:

[0477] The server generates new dictionary data and stores it in the database.

[0478] Input: Analysis results and feature data.

[0479] Specific operation: The server generates new dictionary data based on this data to be used for the next data compression and stores it in the database.

[0480] Output: The new dictionary data stored in the database.

[0481] Step 11:

[0482] The server encrypts the new dictionary data and sends it to the device during the next communication.

[0483] Input: New dictionary data.

[0484] Specific operation: The new dictionary data is encrypted and sent to the device the next time communication occurs.

[0485] Output: Encrypted dictionary data.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] [Second embodiment]

[0490] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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).

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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."

[0502] System Configuration

[0503] The system of the present invention is primarily composed of a low-spec terminal, a server, and a user interface. The low-spec terminal acquires data and transmits it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely.

[0504] Program processing overview

[0505] Terminal side processing

[0506] The device first acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[0507] Specific examples

[0508] The temperature sensor measures the room temperature every minute and stores this data in a buffer. New dictionary data is sent from the server, and the received dictionary data is decrypted. The acquired room temperature data is compressed using the decrypted dictionary data, and the compressed data is encrypted using RSA encryption. The encrypted data is then sent to the server.

[0509] Server-side processing

[0510] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts the data's characteristics and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is sent. The new dictionary data is also encrypted and sent to the device the next time communication occurs.

[0511] Specific examples

[0512] The server receives the data sent from the temperature sensor and stores it in storage. The received data is decrypted using AES encryption to restore the original room temperature data. This data is then passed to a generative AI system, which extracts periodic patterns and fluctuation characteristics. A new compression dictionary is generated based on the extracted characteristics and saved in the database as version 1.0. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0513] User operation and monitoring

[0514] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings. Furthermore, if any problems occur with data transmission or compression, users can be notified through the interface.

[0515] Specific examples

[0516] The user uses a smartphone app to monitor the data transmission status of the room temperature sensor in real time. The user can change the data acquisition frequency from 10 minutes to 5 minutes from the app's settings screen. If a communication error occurs, the user receives a notification from the app and retries.

[0517] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals, and performs advanced data compression and analysis on the server side, achieving effective communication.

[0518] The processing flow will be explained below.

[0519] Program processing steps

[0520] Terminal side processing

[0521] Step 1:

[0522] The device receives data from built-in sensors and external devices, such as current sensor readings and environmental data.

[0523] Step 2:

[0524] The acquired data is temporarily stored in a buffer. For example, room temperature data is acquired and temporarily stored in the device's memory.

[0525] Step 3:

[0526] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[0527] Step 4:

[0528] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[0529] Step 5:

[0530] The received dictionary data is used to compress the acquired data in the buffer. This compression can be performed even on low-spec devices by using a lightweight algorithm.

[0531] Step 6:

[0532] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[0533] Step 7:

[0534] The encrypted compressed data is sent to a server via the Internet or a dedicated communication protocol.

[0535] Server-side processing

[0536] Step 1:

[0537] The server receives the encrypted data sent from the device and temporarily stores it in storage.

[0538] Step 2:

[0539] The received data is decrypted to restore the original compressed data, for example, using RSA encryption to decrypt the data.

[0540] Step 3:

[0541] The decoded data is passed to a generative AI for data analysis, which extracts data features and generates dictionary data for use in the next data compression.

[0542] Step 4:

[0543] The generated dictionary data is saved in a database. This save is also version-controlled and registered as the latest dictionary data.

[0544] Step 5:

[0545] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent, for example, by encrypting it using AES encryption.

[0546] Step 6:

[0547] The new dictionary data is sent to the terminal at the next communication timing, and this data is used in the next compression process.

[0548] User operation and monitoring

[0549] Step 1:

[0550] Through the interface, users can check the data sent from the device to the server and the compression status in real time. For example, they can view the room temperature data history on a smartphone app.

[0551] Step 2:

[0552] Users use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings, for example, changing the data retrieval frequency from 10 minutes to 5 minutes.

[0553] Step 3:

[0554] If a communication error or compression failure occurs, the user is notified through the interface, for example, the app receives an error notification and can choose to retry.

[0555] Example 1

[0556] 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."

[0557] In conventional data collection systems, the challenge was to efficiently transmit data from low-spec devices to a server while keeping communication capacity low. Furthermore, the security of the transmitted data was not adequately ensured, resulting in a lack of confidentiality. This could lead to inefficiencies in the data collection and analysis process and higher costs.

[0558] 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.

[0559] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, data compression means for receiving the dictionary data at the terminal and compressing the acquired data, data compression means for compressing data acquired by the terminal using the decrypted dictionary data, data encryption means for encrypting the compressed data and transmitting it to the server, data recovery means for decrypting the received data at the server and generating the original data, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This enables fast and efficient data transmission from the low-spec terminal and secure data communication while reducing communication capacity.

[0560] A "low-spec device" is an electronic device that has limited processing power but is equipped with sensors and data collection functions to acquire and transmit data.

[0561] "Data acquisition means" is a function that collects data from sensors and external devices and temporarily stores it in a buffer inside the terminal.

[0562] The "data transmission means" is a communication function for transmitting data collected by the terminal to the server.

[0563] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for data compression.

[0564] The "dictionary data management means" is a management function for storing the generated dictionary data in a database and transmitting it to the terminal the next time data is transmitted.

[0565] The "data compression means" is a function that compresses data acquired by the terminal using dictionary data.

[0566] The "data encryption means" is a function that encrypts the compressed data in a secure manner and transmits it to the server.

[0567] The "data restoration means" is a function that decrypts data received by the server and restores the original data.

[0568] The "encryption means" is a function for securely encrypting and decrypting data and dictionary data for communication.

[0569] "Generative AI" is an AI technology that analyzes the characteristics of data and generates new data compression dictionaries.

[0570] The system of the present invention achieves efficient and secure data collection and transmission using low-spec terminals, a server, and a user interface. This system uses the following hardware and software.

[0571] Terminal side processing

[0572] The device acquires data from built-in sensors such as a temperature sensor and external devices. For example, the temperature sensor measures the room temperature every minute and temporarily stores this data in a buffer within the device. The device then waits for new dictionary data to be sent from the server and decrypts the received dictionary data using RSA encryption. The decrypted dictionary data is used to compress the acquired temperature data, and the compressed data is then encrypted using the same RSA encryption. The encrypted data is then sent from the device to the server.

[0573] Server-side processing

[0574] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted using AES encryption to restore the original data. The restored data is then analyzed using generative artificial intelligence to extract data characteristics. New dictionary data is generated based on these characteristics and stored in the database. When the next communication is scheduled, this dictionary data is encrypted using RSA encryption and sent back to the device.

[0575] User operation and monitoring

[0576] Users can check the data being sent from their device to the server and its compression status in real time through an interface, such as a smartphone app. Users can also change data retrieval frequency and encryption settings from the app's settings screen. In addition, if a communication error occurs, users will receive a notification from the app and can retry.

[0577] Through each of the above stages, this system efficiently and securely transmits data from low-spec devices, and performs advanced data compression and analysis on the server side to achieve effective communication.

[0578] Specific examples

[0579] The temperature sensor measures the room temperature every minute and stores the data in a buffer. New dictionary data is sent from the server, and the device receives and decrypts the dictionary data. The acquired room temperature data is compressed using the dictionary data and encrypted with RSA encryption. The encrypted data is then sent to the server.

[0580] The server receives the data and temporarily stores it in storage. It decrypts it using AES encryption to restore the original data. It then uses generative artificial intelligence to extract features from the data, generates a new compressed dictionary, and stores it in the database. The dictionary data is then encrypted at the next communication time and sent to the device.

[0581] The user monitors the data transmission status of the room temperature sensor in real time using a smartphone app. The user can change the data acquisition frequency from the app's settings screen, and if a communication error occurs, they will receive a notification and try again.

[0582] Prompt Sentence Examples

[0583] Design a system with the following data processing flow:

[0584] 1. The device acquires data from the sensor and stores it in a buffer.

[0585] 2. The device receives the new dictionary data from the server and decrypts it.

[0586] 3. The data acquired by the terminal is compressed using dictionary data and encrypted with RSA.

[0587] 4. The device sends the encrypted data to the server.

[0588] 5. The server stores the received data in storage and decrypts it using AES encryption.

[0589] 6. The server passes the original data to the generation AI, which extracts features and generates new dictionary data.

[0590] 7. The generated dictionary data is encrypted and sent to the device during the next communication.

[0591] 8. The user monitors the data transmission and compression status through the interface and changes the settings as necessary.

[0592] Please design a specific system based on this flow.

[0593] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0594] The flow of this system's program processing

[0595] Terminal side processing

[0596] Step 1:

[0597] The terminal acquires data from sensors and external devices. Specifically, the temperature sensor measures the room temperature every minute and stores this data in a buffer. The input is an analog signal from the temperature sensor, and the output is digital data stored in the buffer. For example, data for a room temperature of 25°C is acquired and stored in the buffer.

[0598] Step 2:

[0599] The terminal waits for new dictionary data to be sent from the server. When the dictionary data arrives, it is decrypted using RSA encryption. The input is the encrypted dictionary data sent from the server, and the output is the decrypted dictionary data. Specifically, let XYZ be the dictionary data decrypted by RSA encryption.

[0600] Step 3:

[0601] The terminal compresses the acquired data using the received dictionary data. The input is the room temperature data in the buffer and the decoded dictionary data XYZ, and the output is the compressed data. Specifically, the room temperature data of 25°C is compressed using the dictionary data XYZ to generate the compressed data ABCDE.

[0602] Step 4:

[0603] The terminal encrypts the compressed data using RSA encryption. The input is compressed data ABCDE, and the output is encrypted data FGHIJ. The compressed data ABCDE is encrypted using RSA encryption to generate data FGHIJ.

[0604] Step 5:

[0605] The terminal sends encrypted data to the server. The input is the encrypted data FGHIJ, and the output is the data to be sent to the server. The terminal sends data by specifying the IP address and port number.

[0606] Server-side processing

[0607] Step 6:

[0608] The server receives the data sent from the terminal and temporarily stores it in storage. The input is the encrypted data FGHIJ sent from the terminal, and the output is the encrypted data stored in storage. The server temporarily stores the data and stores it securely.

[0609] Step 7:

[0610] The server decrypts the received data and restores the original data. The input is the encrypted data FGHIJ stored in storage, and the output is the restored room temperature data of 25°C. The data FGHIJ is decrypted using AES encryption, and the original room temperature data of 25°C is restored.

[0611] Step 8:

[0612] The server analyzes the restored data using generative artificial intelligence and extracts data features. The input is room temperature data of 25°C, and the output is the extracted data features. The generative artificial intelligence analyzes periodic patterns and fluctuation features to generate feature data.

[0613] Step 9:

[0614] The server generates new dictionary data based on the features and saves it in the database. The input is the extracted data features, and the output is the generated new dictionary data XYZ+1. The server creates a new compression dictionary and saves it in the database.

[0615] Step 10:

[0616] The server encrypts the new dictionary data and sends it to the terminal at the next communication timing. The input is the generated dictionary data XYZ+1, and the output is the encrypted dictionary data. The dictionary data XYZ+1 is encrypted using RSA encryption and sent to the terminal at the next communication timing.

[0617] User operation and monitoring

[0618] Step 11:

[0619] Through the interface, the user can check the data sent from the device to the server and its compression status in real time. The input is the data transmission status from the server, and the output is the information displayed on the user's interface screen. The user monitors the data transmission status of the room temperature sensor using a smartphone app.

[0620] Step 12:

[0621] The user uses the interface to change the data capture frequency and encryption settings. The input is the setting change instruction entered by the user into the app, and the output is the changed data capture frequency and encryption settings. For example, the user changes the data capture frequency from 10 minutes to 5 minutes.

[0622] Step 13:

[0623] If a communication error occurs, the user is notified through the interface. The input is the information about the communication error, and the output is a notification displayed on the user's interface. When the user receives the notification, they can press the retry button to try to resend the data.

[0624] (Application example 1)

[0625] 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."

[0626] In communication systems that use low-spec terminals, transmitting data efficiently and securely is a challenge. Surveillance systems also require video data to be compressed and encrypted and transmitted to a server in real time. However, performing such processing on low-spec terminals is technically difficult, so there is a need to improve the efficiency of data transmission while ensuring security.

[0627] 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.

[0628] In this invention, the server includes a data acquisition means for acquiring data from a low-spec terminal, a data transmission means for transmitting data to the server, a data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, a dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, an encryption means for securely encrypting and decrypting the data and dictionary data for communication, and a monitoring device means for a monitoring device to acquire video data, compress and encrypt it, and transmit it to the server. This enables efficient and secure data transmission even from low-spec terminals, and also enables real-time video data transmission and anomaly detection in a monitoring system.

[0629] A "low-spec device" is a device that has limited processing power and memory compared to typical high-performance devices.

[0630] "Data acquisition means" refers to methods and devices for collecting information from devices such as various sensors and cameras.

[0631] "Data transmission means" refers to a method or apparatus for transferring acquired data to a server or other device.

[0632] A "server" is a computer system that provides services to client devices over a network.

[0633] "Generative AI" is an AI technology that has the ability to analyze data and generate new data.

[0634] A "data analysis means" is a method or device for processing received data and extracting useful information.

[0635] "Dictionary data" is a database for efficiently handling specific patterns and expressions in data compression and data management.

[0636] The "dictionary data management means" refers to a method or device for storing the generated dictionary data and using it as needed.

[0637] A "cryptographic means" is a method or device for encrypting and decrypting data for secure communication.

[0638] A "surveillance device" is a device such as a camera or sensor used to monitor a specific area.

[0639] A "compression means" is a method or device for compressing data to reduce the volume of the data.

[0640] An "encryption means" is a method or device for encrypting data to protect it from third parties.

[0641] A "decryption means" is a method or device for restoring encrypted data to its original form.

[0642] "Adaptively repeating the transmission and reception of dictionary data with the data transmission means" refers to a process of efficiently transmitting and receiving data according to the network state and the properties of the data.

[0643] "Monitoring devices acquire video data in real time, compress, encrypt, and transmit it, and then analyze the data to detect abnormalities" refers to the process of instantly processing video data acquired by devices such as surveillance cameras to check for safety and abnormalities.

[0644] A system for implementing the present invention comprises a low-spec terminal, a server, and a monitoring device.

[0645] Hardware and software used

[0646] The present invention uses the following hardware and software.

[0647] Hardware: Low-spec devices (e.g., IoT devices and simple sensors), servers, and surveillance devices (e.g., surveillance cameras).

[0648] Software: Python, OpenCV, RSA, PyCryptodome, Requests library.

[0649] Program processing overview

[0650] Terminal side processing

[0651] The device first acquires data from built-in sensors and monitoring devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[0652] Processing on the monitoring device

[0653] The surveillance device (surveillance camera) captures video data in real time. This video data is temporarily stored in a buffer. The stored video data is compressed and encrypted in the same way as on low-spec devices. Dictionary data sent from the server is used for compression, and AES and RSA are used for encryption. The encrypted video data is then sent to the server.

[0654] Server-side processing

[0655] The server receives data sent from the terminals and monitoring devices and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts features from the data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is transmitted. The new dictionary data is also encrypted and sent to the terminals and monitoring devices at the next communication timing.

[0656] Specific examples

[0657] For example, suppose a surveillance camera monitors an office entrance in real time and sends the video data to a server. This data is temporarily stored in a buffer and compressed using dictionary data sent from the server. The compressed data is encrypted with AES encryption, and the AES key is encrypted with RSA encryption. The encrypted data is sent to the server, where it is decrypted and analyzed. New dictionary data generated based on the results of this analysis is used for the next communication.

[0658] Prompt Sentence Examples

[0659] "How can I compress and encrypt surveillance camera data more efficiently?"

[0660] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals and monitoring devices, and performs advanced data compression and analysis on the server side, achieving effective communication.

[0661] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0662] Step 1:

[0663] The terminal acquires data from sensors and monitoring devices.

[0664] Input: Raw data from sensors and surveillance cameras (e.g., temperature data and video data).

[0665] Specific operation: The device reads data from the built-in sensors and camera and temporarily stores it in a buffer.

[0666] Output: Temporarily stored raw data.

[0667] Step 2:

[0668] The terminal receives the dictionary data from the server and decrypts it.

[0669] Input: Encrypted dictionary data sent from the server.

[0670] Specific operation: The terminal waits for dictionary data sent from the server, and when it receives it, it decrypts the dictionary data using RSA encryption.

[0671] Output: Decoded dictionary data.

[0672] Step 3:

[0673] The raw data acquired by the terminal is compressed using the decrypted dictionary data.

[0674] Input: Raw data, decoded dictionary data.

[0675] Specific operation: The terminal refers to the dictionary data and applies a compression algorithm to the raw data to reduce the data size.

[0676] Output: Compressed data.

[0677] Step 4:

[0678] The terminal encrypts the compressed data using AES encryption and sends it to the server.

[0679] Input: Compressed data, AES key.

[0680] Specific operation: The terminal encrypts the compressed data using the AES algorithm, and the AES key is re-encrypted with RSA before being sent to the server.

[0681] Output: The encrypted data and the encrypted AES key.

[0682] Step 5:

[0683] The server receives the encrypted data and stores it temporarily in storage.

[0684] Input: Encrypted data, Encrypted AES key.

[0685] Specific operation: The server receives the data sent from the terminal and stores it in storage.

[0686] Output: The stored encrypted data.

[0687] Step 6:

[0688] The server decrypts the received data and analyzes the restored data using generative artificial intelligence.

[0689] Input: Stored encrypted data, encrypted AES key.

[0690] How it works: The server decrypts the encrypted AES key with RSA, then uses that AES key to decrypt the stored data, and then passes the recovered data to a generative AI for analysis.

[0691] Output: Analysis results and feature extraction data.

[0692] Step 7:

[0693] The server generates new dictionary data based on the analysis results and stores it in the database.

[0694] Input: Analysis results, feature extraction data.

[0695] Specific operation: The server creates new dictionary data based on the analysis results of the generative artificial intelligence, and stores the dictionary data in a database under version control.

[0696] Output: The generated dictionary data.

[0697] Step 8:

[0698] The server encrypts the new dictionary data and sends it to the device the next time it communicates.

[0699] Input: The generated dictionary data.

[0700] Specific operation: The server encrypts the new dictionary data with AES, encrypts the AES key with RSA, and then sends it to the terminal during the next data communication.

[0701] Output: Encrypted dictionary data.

[0702] In this way, the system of the present invention ensures efficient and secure transmission of data through processing steps, and also leverages generative artificial intelligence to improve data compression efficiency and enable real-time analysis of video data from surveillance devices.

[0703] 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.

[0704] System Configuration

[0705] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[0706] Program processing overview

[0707] Terminal side processing

[0708] The device acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. In addition, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The recognized emotion data is also stored in a buffer. The device then waits for new dictionary data sent from the server and decrypts it when it receives it. The acquired data and emotion data are compressed and encrypted using the received dictionary data. The encrypted data is then sent to the server.

[0709] Specific examples

[0710] The temperature sensor measures the room temperature and stores this data in a buffer. At the same time, the emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. The device then receives new dictionary data from the server and decrypts it. The temperature and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[0711] Server-side processing

[0712] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is passed to the generative AI for analysis. The AI ​​extracts the characteristics of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used for the next data transmission. The dictionary data is then encrypted and sent to the device at the next communication timing.

[0713] Specific examples

[0714] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and saved as version 1.0 in the database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0715] User operation and monitoring

[0716] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, users will be notified through the interface if any problems occur with data transmission or compression.

[0717] Specific examples

[0718] Users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine in real time. They can change the emotion recognition settings of the emotion engine from the app's settings screen, changing the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them and they can take appropriate measures.

[0719] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, achieving effective communication and emotion recognition.

[0720] The processing flow will be explained below.

[0721] Program processing steps

[0722] Terminal side processing

[0723] Step 1:

[0724] The device collects data from built-in sensors, such as a temperature sensor that measures the current room temperature in real time.

[0725] Step 2:

[0726] The device activates an emotion engine and analyzes the user's facial expressions and voice data to recognize their emotions. This emotion data is labeled as "happy" or "sad."

[0727] Step 3:

[0728] The acquired data and emotion data are temporarily stored in a buffer. For example, room temperature data and emotion data such as "happy" are stored.

[0729] Step 4:

[0730] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[0731] Step 5:

[0732] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[0733] Step 6:

[0734] The received dictionary data is used to compress the acquired data and emotion data in the buffer. For example, the dictionary data is used to compress the data using the LZMA algorithm.

[0735] Step 7:

[0736] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[0737] Step 8:

[0738] The encrypted compressed data is sent to the server using either HTTP over the Internet or a dedicated protocol.

[0739] Server-side processing

[0740] Step 1:

[0741] The server waits for the encrypted data sent from the device and receives it when it arrives. The received data is temporarily stored in storage.

[0742] Step 2:

[0743] The received encrypted data is decrypted, for example, using RSA encryption.

[0744] Step 3:

[0745] The decoded data is passed to a generative AI for analysis, which extracts features from the data and emotion data and generates dictionary data for use in the next compression.

[0746] Step 4:

[0747] The generated dictionary data is saved in the database. This save also performs version control and is used as the latest dictionary data.

[0748] Step 5:

[0749] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent. For example, encryption is performed using AES encryption.

[0750] Step 6:

[0751] At the next communication timing, new dictionary data is sent to the terminal, and the dictionary data is used for the next data compression.

[0752] User operation and monitoring

[0753] Step 1:

[0754] Through the interface, users can check the data sent from their device to the server and its compression status in real time, for example by viewing the data transmission history on a smartphone app.

[0755] Step 2:

[0756] The user uses the interface to change basic settings such as compression method, data capture frequency, encryption settings, emotion recognition settings for the emotion engine, etc. For example, setting the sensitivity of the emotion engine.

[0757] Step 3:

[0758] If a communication error or compression failure occurs, the user is notified through the interface, for example, by receiving an error message via a push notification in the app.

[0759] This series of processing steps enables data containing emotional data to be sent efficiently and securely even on low-spec devices. The server analyzes the data, generates dictionary data that will be useful for the next data compression, and distributes it to the device. Users can check the data status in real time and make necessary setting changes to optimize system performance.

[0760] Example 2

[0761] 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."

[0762] In conventional low-spec devices, it has been difficult to efficiently and securely acquire and transmit data due to limited resources. Furthermore, even in systems that acquire user emotion data in real time and analyze it on the server side, many issues remain, such as improving data compression efficiency and reducing communication capacity. The present invention aims to solve these issues by providing a system that achieves effective communication and emotion recognition by efficiently and securely transmitting data and emotion data acquired from low-spec devices and performing advanced data analysis and compression on the server side.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0764] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, emotion data acquisition means for recognizing user emotions using an emotion engine and acquiring the data, data transmission means for transmitting data to the server, data analysis means for analyzing data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, compression means for compressing the data and emotion data acquired by the terminal using the dictionary data received from the server, encryption means for securely encrypting and decrypting the data and dictionary data for communication, and user interface means for allowing the user to monitor the operating status of the terminal and server in real time and change settings. This enables simultaneous acquisition of data and emotion data from the low-spec terminal, realizes effective data analysis and compression on the server side, and enables highly efficient data transmission while reducing communication capacity.

[0765] A "low-spec terminal" is a computing device that has limited computing power and memory, but is equipped with sensors and basic input means.

[0766] "Data acquisition means" is a function that allows a terminal to collect data from sensors and external devices and temporarily store it in a buffer.

[0767] The "emotion data acquisition means" is a function that collects emotion data in real time using an emotion engine that recognizes the user's emotions.

[0768] The "data transmission means" is a communication function for transmitting data acquired by the terminal to the server.

[0769] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for future data compression.

[0770] The "dictionary data management means" is a function that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[0771] "Compression means" is a function that efficiently compresses the data and emotion data acquired by the terminal.

[0772] The "encryption means" is a function that securely encrypts data and dictionary data and decrypts received data.

[0773] The "user interface means" is an interface that allows the user to monitor the operating status of the terminal and server in real time and change various settings.

[0774] "Generative AI" is an AI technology that analyzes incoming data, extracts its features, and uses them for future data compression and analysis.

[0775] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[0776] The device acquires data from built-in sensors and external devices. For example, a DHT11 temperature sensor can be used. This data is temporarily stored in a buffer. The device also has an emotion engine that recognizes the user's emotions in real time. The emotion engine can use the OpenCV library. The recognized emotion data is also stored in the buffer.

[0777] Next, the device waits for new dictionary data to be sent from the server. Upon receiving it, it decrypts the RSA encryption using the OpenSSL library. The acquired data and emotion data are compressed using the received dictionary data. The LZ77 algorithm can be used for compression. The data is then RSA encrypted again using the OpenSSL library. The encrypted data is then sent to the server.

[0778] The server receives data sent from the device using the HTTPS protocol and temporarily stores it in storage. The storage used here could be an AWS S3 bucket, for example. The server then decrypts the received data and restores the original data. This decryption also uses the OpenSSL library. The restored data is passed to a generative AI system, which can use a custom TensorFlow-based model. The AI ​​extracts features from the data and emotion data and generates dictionary data to be used for the next data compression. The generated dictionary data is stored in a PostgreSQL database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0779] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. This interface uses a React.js-based web application. Users can also use the interface to change basic settings such as the compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, if any problems occur with data transmission or compression, users will be notified via Firebase Cloud Messaging.

[0780] For example, a temperature sensor measures the room temperature and stores this data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and generates emotion data, such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives new dictionary data from the server and decrypts it. The temperature data and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[0781] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and stored in a database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0782] An example prompt might be:

[0783] Please update the information as follows to generate the latest dictionary data:

[0784] Acquired temperature data: 22.5℃

[0785] Recognized emotion data: Happy

[0786] Acquisition frequency: Every 5 minutes

[0787]

[0788] Adjust the settings for receiving the latest dictionary data from the server, compressing and encrypting the data before sending it:

[0789] RSA encryption key: 2048 bits

[0790] Compression algorithm: LZ77

[0791] Communication protocol: HTTPS

[0792] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[0793] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0794] Step 1: Get the data

[0795] The device acquires data from built-in sensors (e.g., temperature sensor DHT11) and external devices.

[0796] Input: Room temperature data measured by a temperature sensor

[0797] Data processing: Temperature data is saved in a buffer

[0798] Output: Buffered room temperature data

[0799] Specific operation: The temperature sensor measures the room temperature as 22.5°C and stores the data in the terminal's buffer.

[0800] Step 2: Recognizing Emotional Data

[0801] The device uses an emotion engine (e.g., OpenCV library) to recognize the user's emotions in real time.

[0802] Input: User's facial expression data

[0803] Data Computing: Recognizing Emotions from Facial Expressions Using OpenCV Library

[0804] Output: Recognized emotion data

[0805] Specific operation: The emotion engine analyzes the user's facial expression, generates emotion data of "happy," and stores this in a buffer.

[0806] Step 3: Receiving and Decoding Dictionary Data

[0807] The terminal waits for new dictionary data to be sent from the server.

[0808] Input: Encrypted dictionary data sent from the server

[0809] Data Calculation: Decrypting dictionary data using the OpenSSL library

[0810] Output: Decoded dictionary data

[0811] Specific operation: The terminal receives the dictionary data using the HTTPS protocol, decrypts the RSA encryption, and loads the dictionary data into memory.

[0812] Step 4: Compress the data

[0813] The terminal compresses the acquired data and emotion data using the received dictionary data.

[0814] Input: Room temperature data and emotion data stored in the buffer, decoded dictionary data

[0815] Data operation: Compress data using the LZ77 algorithm

[0816] Output: Compressed data

[0817] Specific operation: Temperature data and emotion data are efficiently compressed using the LZ77 algorithm to generate compressed data.

[0818] Step 5: Encrypt the data

[0819] The terminal encrypts the compressed data.

[0820] Input: Compressed data

[0821] Data Computation: RSA encryption using the OpenSSL library

[0822] Output: Encrypted data

[0823] Specific operation: RSA encrypts the compressed data to generate encrypted data.

[0824] Step 6: Sending data

[0825] The terminal transmits the encrypted data to the server.

[0826] Input: Encrypted data

[0827] Data processing: Send data using HTTPS protocol

[0828] Output: Data sent to the server

[0829] What it does: Encrypts data and sends it to the server using the HTTPS protocol.

[0830] Step 7: Receiving Data

[0831] The server receives the data sent from the terminal and temporarily stores it in storage.

[0832] Input: Encrypted data sent from the device

[0833] Data processing: Save to storage

[0834] Output: Encrypted data stored in storage

[0835] Specific operation: The server saves the received data to an AWS S3 bucket using the HTTPS protocol.

[0836] Step 8: Decrypt and recover data

[0837] The server decrypts the received data and restores the original data.

[0838] Input: Encrypted and stored data

[0839] Data Computing: Decrypting RSA Encryption Using the OpenSSL Library

[0840] Output: Recovered room temperature data and emotion data

[0841] Specific operation: Decrypt the RSA encryption and restore the original temperature data (note: 22.5°C) and emotion data (e.g., "happy").

[0842] Step 9: Analyze the data and generate dictionary data

[0843] The server extracts features from the data and emotional data passed to the generative AI and generates new dictionary data.

[0844] Input: Recovered room temperature data and emotion data

[0845] Data Computation: Extracting Periodic Patterns and Emotional Fluctuations with TensorFlow

[0846] Output: New compression dictionary

[0847] How it works: The TensorFlow model analyzes periodic patterns and emotional fluctuations to generate new dictionary data.

[0848] Step 10: Preparing to save and send dictionary data

[0849] The server stores the generated dictionary data in a database, encrypts it, and prepares to send it to the terminal at the next communication timing.

[0850] Input: New dictionary data

[0851] Data processing: Dictionary data is stored in a database and encrypted.

[0852] Output: Encrypted dictionary data

[0853] What it does: It saves the new dictionary data in a PostgreSQL database and encrypts it using the OpenSSL library.

[0854] Step 11: Monitor users and change settings

[0855] The user monitors the operating status of the terminal and server through the interface and changes the settings as necessary.

[0856] Input: Real-time terminal and server activity

[0857] Data Calculation: Changing various settings

[0858] Output: Changed settings

[0859] What it does: Use a React.js-based web application to change data collection frequency, emotion recognition settings, and receive notification of communication errors.

[0860] (Application example 2)

[0861] 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."

[0862] Conventional systems that collect data from low-spec devices and analyze it on a server have had problems with data compression efficiency and communication costs. Furthermore, to improve the user experience, it is necessary to collect and appropriately analyze user emotion data in real time, but current systems are also insufficient in this regard. To solve these issues, a data compression and analysis system incorporating emotion recognition technology is needed.

[0863] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0864] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, emotion recognition means for recognizing the user's emotions in real time, data compression means for combining the acquired emotion data and sensor data, compressing the data and transmitting it to the server, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This makes it possible to effectively collect emotion data while keeping communication costs down during data compression and transmission.

[0865] A "low-spec terminal" is an information processing device with limited resources such as computing power and memory capacity.

[0866] "Data acquisition means" refers to a function or device for collecting data from low-spec terminals.

[0867] "Data transmission means" refers to a function or device for transmitting collected data to a server.

[0868] "Data analysis means" refers to a function or device that uses generative artificial intelligence to analyze data received on the server side and create dictionary data to be used for compression.

[0869] The "dictionary data management means" is a function or device that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[0870] "Emotion recognition means" refers to a function or device for detecting and analyzing a user's emotions in real time.

[0871] "Data compression means" refers to a function or device that combines acquired emotion data and sensor data, compresses it efficiently, and transmits it to the server.

[0872] "Encryption means" refers to a function or device for securely encrypting and decrypting data and dictionary data for communication.

[0873] "Generative AI" is an AI that has the ability to analyze collected data and extract specific patterns and characteristics.

[0874] System Configuration

[0875] The system of the present invention includes a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires sensor data and emotion data and transmits it to the server. The system includes the following main components:

[0876] Low-spec device: Equipped with sensors and emotion engines to collect data.

[0877] Server: Analyzes the received data and generates dictionary data to be used for compression.

[0878] User interface: The user can check the data transmission status and emotion data, and change settings.

[0879] Emotion engine: Recognizes user emotions in real time and provides them as data.

[0880] Program processing overview

[0881] Terminal side processing

[0882] The device acquires data from its built-in sensors and emotion engine and temporarily stores it in a buffer. The acquired data receives dictionary data sent from the server, decrypts it, and then compresses it. The compressed data is encrypted using RSA encryption and sent to the server.

[0883] For example, a temperature sensor measures the room temperature and stores the data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives and decrypts new dictionary data sent from the server. The temperature and emotion data are efficiently compressed, then encrypted using RSA encryption and sent to the server.

[0884] Server-side processing

[0885] The server receives the data sent from the device and temporarily stores it in storage. It then decrypts the received data and restores the original data. The restored data is passed to a generative AI for analysis. The AI ​​extracts features of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and sent to the device the next time it communicates.

[0886] As a concrete example, the server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This is then passed to the generative AI, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated and stored in the database. This dictionary data is then sent to the device in time for the next communication.

[0887] User operation and monitoring

[0888] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings. Furthermore, if a problem occurs, users will receive notifications through the interface.

[0889] As a concrete example, users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine. They can change the emotion recognition settings of the emotion engine from the app's settings screen and change the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them so that they can take appropriate measures.

[0890] Prompt sentence for generative AI model

[0891] As a concrete example, the following prompt can be used, using emotion data and sensor information from a generative AI model:

[0892] "The room temperature data is 26 degrees and the user is smiling. What would you suggest in this case?"

[0893] "The user has a sad expression and the room temperature is 18 degrees. What promotion would work?"

[0894] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[0895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0896] Step 1:

[0897] The device collects sensor data.

[0898] Input: Data from temperature sensor, camera, and microphone.

[0899] Specific operation: The temperature sensor measures the room temperature, the camera captures the user's facial expressions, and the microphone captures voice data.

[0900] Output: Sensor data and raw image and audio data.

[0901] Step 2:

[0902] The device collects emotional data.

[0903] Input: Raw image and audio data.

[0904] Specific operation: The emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[0905] Output: Emotion data (e.g., the user's emotional state, such as "happy" or "sad").

[0906] Step 3:

[0907] The device stores sensor data and emotion data in a buffer.

[0908] Input: Sensor data and emotion data.

[0909] Specific operation: The acquired sensor data and emotion data are temporarily stored in a buffer in memory.

[0910] Output: The data stored in the buffer.

[0911] Step 4:

[0912] The terminal receives the dictionary data from the server and decrypts it.

[0913] Input: Encrypted dictionary data sent from the server.

[0914] Specific operation: The device receives the encrypted dictionary data and decrypts it using the decryption key.

[0915] Output: Decoded dictionary data.

[0916] Step 5:

[0917] The device compresses sensor data and emotion data.

[0918] Input: Buffered data and decoded dictionary data.

[0919] Specific operation: Sensor data and emotion data are compressed using dictionary data.

[0920] Output: Compressed data.

[0921] Step 6:

[0922] The terminal encrypts the compressed data and sends it to the server.

[0923] Input: Compressed data.

[0924] Specific operation: The compressed data is encrypted using the RSA encryption method and sent to the server.

[0925] Output: The encrypted data sent to the server.

[0926] Step 7:

[0927] The server receives the data and stores it temporarily.

[0928] Input: Encrypted data sent from the terminal.

[0929] Specific operation: The server receives the data and temporarily stores it in storage.

[0930] Output: Encrypted data in storage.

[0931] Step 8:

[0932] The server decrypts and recovers the encrypted data.

[0933] Input: Encrypted data stored in storage.

[0934] Specific operation: The data is decrypted using the decryption key to restore the original sensor data and emotion data.

[0935] Output: The recovered data.

[0936] Step 9:

[0937] The server passes the restored data to a generative artificial intelligence for analysis.

[0938] Input: Recovered data.

[0939] How it works: Data is passed to a generative AI to extract cyclical patterns and emotional fluctuations.

[0940] Output: Analysis results and feature data.

[0941] Step 10:

[0942] The server generates new dictionary data and stores it in the database.

[0943] Input: Analysis results and feature data.

[0944] Specific operation: The server generates new dictionary data based on this data to be used for the next data compression and stores it in the database.

[0945] Output: The new dictionary data stored in the database.

[0946] Step 11:

[0947] The server encrypts the new dictionary data and sends it to the device during the next communication.

[0948] Input: New dictionary data.

[0949] Specific operation: The new dictionary data is encrypted and sent to the device the next time communication occurs.

[0950] Output: Encrypted dictionary data.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] [Third embodiment]

[0955] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0956] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0957] 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).

[0958] 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.

[0959] 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.

[0960] 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).

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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."

[0967] System Configuration

[0968] The system of the present invention is primarily composed of a low-spec terminal, a server, and a user interface. The low-spec terminal acquires data and transmits it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely.

[0969] Program processing overview

[0970] Terminal side processing

[0971] The device first acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[0972] Specific examples

[0973] The temperature sensor measures the room temperature every minute and stores this data in a buffer. New dictionary data is sent from the server, and the received dictionary data is decrypted. The acquired room temperature data is compressed using the decrypted dictionary data, and the compressed data is encrypted using RSA encryption. The encrypted data is then sent to the server.

[0974] Server-side processing

[0975] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts the data's characteristics and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is sent. The new dictionary data is also encrypted and sent to the device the next time communication occurs.

[0976] Specific examples

[0977] The server receives the data sent from the temperature sensor and stores it in storage. The received data is decrypted using AES encryption to restore the original room temperature data. This data is then passed to a generative AI system, which extracts periodic patterns and fluctuation characteristics. A new compression dictionary is generated based on the extracted characteristics and saved in the database as version 1.0. This dictionary data is then encrypted in time for the next communication and sent to the device.

[0978] User operation and monitoring

[0979] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings. Furthermore, if any problems occur with data transmission or compression, users can be notified through the interface.

[0980] Specific examples

[0981] The user uses a smartphone app to monitor the data transmission status of the room temperature sensor in real time. The user can change the data acquisition frequency from 10 minutes to 5 minutes from the app's settings screen. If a communication error occurs, the user receives a notification from the app and retries.

[0982] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals, and performs advanced data compression and analysis on the server side, achieving effective communication.

[0983] The processing flow will be explained below.

[0984] Program processing steps

[0985] Terminal side processing

[0986] Step 1:

[0987] The device receives data from built-in sensors and external devices, such as current sensor readings and environmental data.

[0988] Step 2:

[0989] The acquired data is temporarily stored in a buffer. For example, room temperature data is acquired and temporarily stored in the device's memory.

[0990] Step 3:

[0991] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[0992] Step 4:

[0993] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[0994] Step 5:

[0995] The received dictionary data is used to compress the acquired data in the buffer. This compression can be performed even on low-spec devices by using a lightweight algorithm.

[0996] Step 6:

[0997] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[0998] Step 7:

[0999] The encrypted compressed data is sent to a server via the Internet or a dedicated communication protocol.

[1000] Server-side processing

[1001] Step 1:

[1002] The server receives the encrypted data sent from the device and temporarily stores it in storage.

[1003] Step 2:

[1004] The received data is decrypted to restore the original compressed data, for example, using RSA encryption to decrypt the data.

[1005] Step 3:

[1006] The decoded data is passed to a generative AI for data analysis, which extracts data features and generates dictionary data for use in the next data compression.

[1007] Step 4:

[1008] The generated dictionary data is saved in a database. This save is also version-controlled and registered as the latest dictionary data.

[1009] Step 5:

[1010] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent, for example, by encrypting it using AES encryption.

[1011] Step 6:

[1012] The new dictionary data is sent to the terminal at the next communication timing, and this data is used in the next compression process.

[1013] User operation and monitoring

[1014] Step 1:

[1015] Through the interface, users can check the data sent from the device to the server and the compression status in real time. For example, they can view the room temperature data history on a smartphone app.

[1016] Step 2:

[1017] Users use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings, for example, changing the data retrieval frequency from 10 minutes to 5 minutes.

[1018] Step 3:

[1019] If a communication error or compression failure occurs, the user is notified through the interface, for example, the app receives an error notification and can choose to retry.

[1020] Example 1

[1021] 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."

[1022] In conventional data collection systems, the challenge was to efficiently transmit data from low-spec devices to a server while keeping communication capacity low. Furthermore, the security of the transmitted data was not adequately ensured, resulting in a lack of confidentiality. This could lead to inefficiencies in the data collection and analysis process and higher costs.

[1023] 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.

[1024] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, data compression means for receiving the dictionary data at the terminal and compressing the acquired data, data compression means for compressing data acquired by the terminal using the decrypted dictionary data, data encryption means for encrypting the compressed data and transmitting it to the server, data recovery means for decrypting the received data at the server and generating the original data, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This enables fast and efficient data transmission from the low-spec terminal and secure data communication while reducing communication capacity.

[1025] A "low-spec device" is an electronic device that has limited processing power but is equipped with sensors and data collection functions to acquire and transmit data.

[1026] "Data acquisition means" is a function that collects data from sensors and external devices and temporarily stores it in a buffer inside the terminal.

[1027] The "data transmission means" is a communication function for transmitting data collected by the terminal to the server.

[1028] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for data compression.

[1029] The "dictionary data management means" is a management function for storing the generated dictionary data in a database and transmitting it to the terminal the next time data is transmitted.

[1030] The "data compression means" is a function that compresses data acquired by the terminal using dictionary data.

[1031] The "data encryption means" is a function that encrypts the compressed data in a secure manner and transmits it to the server.

[1032] The "data restoration means" is a function that decrypts data received by the server and restores the original data.

[1033] The "encryption means" is a function for securely encrypting and decrypting data and dictionary data for communication.

[1034] "Generative AI" is an AI technology that analyzes the characteristics of data and generates new data compression dictionaries.

[1035] The system of the present invention achieves efficient and secure data collection and transmission using low-spec terminals, a server, and a user interface. This system uses the following hardware and software.

[1036] Terminal side processing

[1037] The device acquires data from built-in sensors such as a temperature sensor and external devices. For example, the temperature sensor measures the room temperature every minute and temporarily stores this data in a buffer within the device. The device then waits for new dictionary data to be sent from the server and decrypts the received dictionary data using RSA encryption. The decrypted dictionary data is used to compress the acquired temperature data, and the compressed data is then encrypted using the same RSA encryption. The encrypted data is then sent from the device to the server.

[1038] Server-side processing

[1039] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted using AES encryption to restore the original data. The restored data is then analyzed using generative artificial intelligence to extract data characteristics. New dictionary data is generated based on these characteristics and stored in the database. When the next communication is scheduled, this dictionary data is encrypted using RSA encryption and sent back to the device.

[1040] User operation and monitoring

[1041] Users can check the data being sent from their device to the server and its compression status in real time through an interface, such as a smartphone app. Users can also change data retrieval frequency and encryption settings from the app's settings screen. In addition, if a communication error occurs, users will receive a notification from the app and can retry.

[1042] Through each of the above stages, this system efficiently and securely transmits data from low-spec devices, and performs advanced data compression and analysis on the server side to achieve effective communication.

[1043] Specific examples

[1044] The temperature sensor measures the room temperature every minute and stores the data in a buffer. New dictionary data is sent from the server, and the device receives and decrypts the dictionary data. The acquired room temperature data is compressed using the dictionary data and encrypted with RSA encryption. The encrypted data is then sent to the server.

[1045] The server receives the data and temporarily stores it in storage. It decrypts it using AES encryption to restore the original data. It then uses generative artificial intelligence to extract features from the data, generates a new compressed dictionary, and stores it in the database. The dictionary data is then encrypted at the next communication time and sent to the device.

[1046] The user monitors the data transmission status of the room temperature sensor in real time using a smartphone app. The user can change the data acquisition frequency from the app's settings screen, and if a communication error occurs, they will receive a notification and try again.

[1047] Prompt Sentence Examples

[1048] Design a system with the following data processing flow:

[1049] 1. The device acquires data from the sensor and stores it in a buffer.

[1050] 2. The device receives the new dictionary data from the server and decrypts it.

[1051] 3. The data acquired by the terminal is compressed using dictionary data and encrypted with RSA.

[1052] 4. The device sends the encrypted data to the server.

[1053] 5. The server stores the received data in storage and decrypts it using AES encryption.

[1054] 6. The server passes the original data to the generation AI, which extracts features and generates new dictionary data.

[1055] 7. The generated dictionary data is encrypted and sent to the device during the next communication.

[1056] 8. The user monitors the data transmission and compression status through the interface and changes the settings as necessary.

[1057] Please design a specific system based on this flow.

[1058] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1059] The flow of this system's program processing

[1060] Terminal side processing

[1061] Step 1:

[1062] The terminal acquires data from sensors and external devices. Specifically, the temperature sensor measures the room temperature every minute and stores this data in a buffer. The input is an analog signal from the temperature sensor, and the output is digital data stored in the buffer. For example, data for a room temperature of 25°C is acquired and stored in the buffer.

[1063] Step 2:

[1064] The terminal waits for new dictionary data to be sent from the server. When the dictionary data arrives, it is decrypted using RSA encryption. The input is the encrypted dictionary data sent from the server, and the output is the decrypted dictionary data. Specifically, let XYZ be the dictionary data decrypted by RSA encryption.

[1065] Step 3:

[1066] The terminal compresses the acquired data using the received dictionary data. The input is the room temperature data in the buffer and the decoded dictionary data XYZ, and the output is the compressed data. Specifically, the room temperature data of 25°C is compressed using the dictionary data XYZ to generate the compressed data ABCDE.

[1067] Step 4:

[1068] The terminal encrypts the compressed data using RSA encryption. The input is compressed data ABCDE, and the output is encrypted data FGHIJ. The compressed data ABCDE is encrypted using RSA encryption to generate data FGHIJ.

[1069] Step 5:

[1070] The terminal sends encrypted data to the server. The input is the encrypted data FGHIJ, and the output is the data to be sent to the server. The terminal sends data by specifying the IP address and port number.

[1071] Server-side processing

[1072] Step 6:

[1073] The server receives the data sent from the terminal and temporarily stores it in storage. The input is the encrypted data FGHIJ sent from the terminal, and the output is the encrypted data stored in storage. The server temporarily stores the data and stores it securely.

[1074] Step 7:

[1075] The server decrypts the received data and restores the original data. The input is the encrypted data FGHIJ stored in storage, and the output is the restored room temperature data of 25°C. The data FGHIJ is decrypted using AES encryption, and the original room temperature data of 25°C is restored.

[1076] Step 8:

[1077] The server analyzes the restored data using generative artificial intelligence and extracts data features. The input is room temperature data of 25°C, and the output is the extracted data features. The generative artificial intelligence analyzes periodic patterns and fluctuation features to generate feature data.

[1078] Step 9:

[1079] The server generates new dictionary data based on the features and saves it in the database. The input is the extracted data features, and the output is the generated new dictionary data XYZ+1. The server creates a new compression dictionary and saves it in the database.

[1080] Step 10:

[1081] The server encrypts the new dictionary data and sends it to the terminal at the next communication timing. The input is the generated dictionary data XYZ+1, and the output is the encrypted dictionary data. The dictionary data XYZ+1 is encrypted using RSA encryption and sent to the terminal at the next communication timing.

[1082] User operation and monitoring

[1083] Step 11:

[1084] Through the interface, the user can check the data sent from the device to the server and its compression status in real time. The input is the data transmission status from the server, and the output is the information displayed on the user's interface screen. The user monitors the data transmission status of the room temperature sensor using a smartphone app.

[1085] Step 12:

[1086] The user uses the interface to change the data capture frequency and encryption settings. The input is the setting change instruction entered by the user into the app, and the output is the changed data capture frequency and encryption settings. For example, the user changes the data capture frequency from 10 minutes to 5 minutes.

[1087] Step 13:

[1088] If a communication error occurs, the user is notified through the interface. The input is the information about the communication error, and the output is a notification displayed on the user's interface. When the user receives the notification, they can press the retry button to try to resend the data.

[1089] (Application example 1)

[1090] 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."

[1091] In communication systems that use low-spec terminals, transmitting data efficiently and securely is a challenge. Surveillance systems also require video data to be compressed and encrypted and transmitted to a server in real time. However, performing such processing on low-spec terminals is technically difficult, so there is a need to improve the efficiency of data transmission while ensuring security.

[1092] 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.

[1093] In this invention, the server includes a data acquisition means for acquiring data from a low-spec terminal, a data transmission means for transmitting data to the server, a data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, a dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, an encryption means for securely encrypting and decrypting the data and dictionary data for communication, and a monitoring device means for a monitoring device to acquire video data, compress and encrypt it, and transmit it to the server. This enables efficient and secure data transmission even from low-spec terminals, and also enables real-time video data transmission and anomaly detection in a monitoring system.

[1094] A "low-spec device" is a device that has limited processing power and memory compared to typical high-performance devices.

[1095] "Data acquisition means" refers to methods and devices for collecting information from devices such as various sensors and cameras.

[1096] "Data transmission means" refers to a method or apparatus for transferring acquired data to a server or other device.

[1097] A "server" is a computer system that provides services to client devices over a network.

[1098] "Generative AI" is an AI technology that has the ability to analyze data and generate new data.

[1099] A "data analysis means" is a method or device for processing received data and extracting useful information.

[1100] "Dictionary data" is a database for efficiently handling specific patterns and expressions in data compression and data management.

[1101] The "dictionary data management means" refers to a method or device for storing the generated dictionary data and using it as needed.

[1102] A "cryptographic means" is a method or device for encrypting and decrypting data for secure communication.

[1103] A "surveillance device" is a device such as a camera or sensor used to monitor a specific area.

[1104] A "compression means" is a method or device for compressing data to reduce the volume of the data.

[1105] An "encryption means" is a method or device for encrypting data to protect it from third parties.

[1106] A "decryption means" is a method or device for restoring encrypted data to its original form.

[1107] "Adaptively repeating the transmission and reception of dictionary data with the data transmission means" refers to a process of efficiently transmitting and receiving data according to the network state and the properties of the data.

[1108] "Monitoring devices acquire video data in real time, compress, encrypt, and transmit it, and then analyze the data to detect abnormalities" refers to the process of instantly processing video data acquired by devices such as surveillance cameras to check for safety and abnormalities.

[1109] A system for implementing the present invention comprises a low-spec terminal, a server, and a monitoring device.

[1110] Hardware and software used

[1111] The present invention uses the following hardware and software.

[1112] Hardware: Low-spec devices (e.g., IoT devices and simple sensors), servers, and surveillance devices (e.g., surveillance cameras).

[1113] Software: Python, OpenCV, RSA, PyCryptodome, Requests library.

[1114] Program processing overview

[1115] Terminal side processing

[1116] The device first acquires data from built-in sensors and monitoring devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[1117] Processing on the monitoring device

[1118] The surveillance device (surveillance camera) captures video data in real time. This video data is temporarily stored in a buffer. The stored video data is compressed and encrypted in the same way as on low-spec devices. Dictionary data sent from the server is used for compression, and AES and RSA are used for encryption. The encrypted video data is then sent to the server.

[1119] Server-side processing

[1120] The server receives data sent from the terminals and monitoring devices and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts features from the data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is transmitted. The new dictionary data is also encrypted and sent to the terminals and monitoring devices at the next communication timing.

[1121] Specific examples

[1122] For example, suppose a surveillance camera monitors an office entrance in real time and sends the video data to a server. This data is temporarily stored in a buffer and compressed using dictionary data sent from the server. The compressed data is encrypted with AES encryption, and the AES key is encrypted with RSA encryption. The encrypted data is sent to the server, where it is decrypted and analyzed. New dictionary data generated based on the results of this analysis is used for the next communication.

[1123] Prompt Sentence Examples

[1124] "How can I compress and encrypt surveillance camera data more efficiently?"

[1125] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals and monitoring devices, and performs advanced data compression and analysis on the server side, achieving effective communication.

[1126] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1127] Step 1:

[1128] The terminal acquires data from sensors and monitoring devices.

[1129] Input: Raw data from sensors and surveillance cameras (e.g., temperature data and video data).

[1130] Specific operation: The device reads data from the built-in sensors and camera and temporarily stores it in a buffer.

[1131] Output: Temporarily stored raw data.

[1132] Step 2:

[1133] The terminal receives the dictionary data from the server and decrypts it.

[1134] Input: Encrypted dictionary data sent from the server.

[1135] Specific operation: The terminal waits for dictionary data sent from the server, and when it receives it, it decrypts the dictionary data using RSA encryption.

[1136] Output: Decoded dictionary data.

[1137] Step 3:

[1138] The raw data acquired by the terminal is compressed using the decrypted dictionary data.

[1139] Input: Raw data, decoded dictionary data.

[1140] Specific operation: The terminal refers to the dictionary data and applies a compression algorithm to the raw data to reduce the data size.

[1141] Output: Compressed data.

[1142] Step 4:

[1143] The terminal encrypts the compressed data using AES encryption and sends it to the server.

[1144] Input: Compressed data, AES key.

[1145] Specific operation: The terminal encrypts the compressed data using the AES algorithm, and the AES key is re-encrypted with RSA before being sent to the server.

[1146] Output: The encrypted data and the encrypted AES key.

[1147] Step 5:

[1148] The server receives the encrypted data and stores it temporarily in storage.

[1149] Input: Encrypted data, Encrypted AES key.

[1150] Specific operation: The server receives the data sent from the terminal and stores it in storage.

[1151] Output: The stored encrypted data.

[1152] Step 6:

[1153] The server decrypts the received data and analyzes the restored data using generative artificial intelligence.

[1154] Input: Stored encrypted data, encrypted AES key.

[1155] How it works: The server decrypts the encrypted AES key with RSA, then uses that AES key to decrypt the stored data, and then passes the recovered data to a generative AI for analysis.

[1156] Output: Analysis results and feature extraction data.

[1157] Step 7:

[1158] The server generates new dictionary data based on the analysis results and stores it in the database.

[1159] Input: Analysis results, feature extraction data.

[1160] Specific operation: The server creates new dictionary data based on the analysis results of the generative artificial intelligence, and stores the dictionary data in a database under version control.

[1161] Output: The generated dictionary data.

[1162] Step 8:

[1163] The server encrypts the new dictionary data and sends it to the device the next time it communicates.

[1164] Input: The generated dictionary data.

[1165] Specific operation: The server encrypts the new dictionary data with AES, encrypts the AES key with RSA, and then sends it to the terminal during the next data communication.

[1166] Output: Encrypted dictionary data.

[1167] In this way, the system of the present invention ensures efficient and secure transmission of data through processing steps, and also leverages generative artificial intelligence to improve data compression efficiency and enable real-time analysis of video data from surveillance devices.

[1168] 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.

[1169] System Configuration

[1170] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[1171] Program processing overview

[1172] Terminal side processing

[1173] The device acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. In addition, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The recognized emotion data is also stored in a buffer. The device then waits for new dictionary data sent from the server and decrypts it when it receives it. The acquired data and emotion data are compressed and encrypted using the received dictionary data. The encrypted data is then sent to the server.

[1174] Specific examples

[1175] The temperature sensor measures the room temperature and stores this data in a buffer. At the same time, the emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. The device then receives new dictionary data from the server and decrypts it. The temperature and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[1176] Server-side processing

[1177] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is passed to the generative AI for analysis. The AI ​​extracts the characteristics of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used for the next data transmission. The dictionary data is then encrypted and sent to the device at the next communication timing.

[1178] Specific examples

[1179] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and saved as version 1.0 in the database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[1180] User operation and monitoring

[1181] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, users will be notified through the interface if any problems occur with data transmission or compression.

[1182] Specific examples

[1183] Users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine in real time. They can change the emotion recognition settings of the emotion engine from the app's settings screen, changing the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them and they can take appropriate measures.

[1184] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, achieving effective communication and emotion recognition.

[1185] The processing flow will be explained below.

[1186] Program processing steps

[1187] Terminal side processing

[1188] Step 1:

[1189] The device collects data from built-in sensors, such as a temperature sensor that measures the current room temperature in real time.

[1190] Step 2:

[1191] The device activates an emotion engine and analyzes the user's facial expressions and voice data to recognize their emotions. This emotion data is labeled as "happy" or "sad."

[1192] Step 3:

[1193] The acquired data and emotion data are temporarily stored in a buffer. For example, room temperature data and emotion data such as "happy" are stored.

[1194] Step 4:

[1195] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[1196] Step 5:

[1197] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[1198] Step 6:

[1199] The received dictionary data is used to compress the acquired data and emotion data in the buffer. For example, the dictionary data is used to compress the data using the LZMA algorithm.

[1200] Step 7:

[1201] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[1202] Step 8:

[1203] The encrypted compressed data is sent to the server using either HTTP over the Internet or a dedicated protocol.

[1204] Server-side processing

[1205] Step 1:

[1206] The server waits for the encrypted data sent from the device and receives it when it arrives. The received data is temporarily stored in storage.

[1207] Step 2:

[1208] The received encrypted data is decrypted, for example, using RSA encryption.

[1209] Step 3:

[1210] The decoded data is passed to a generative AI for analysis, which extracts features from the data and emotion data and generates dictionary data for use in the next compression.

[1211] Step 4:

[1212] The generated dictionary data is saved in the database. This save also performs version control and is used as the latest dictionary data.

[1213] Step 5:

[1214] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent. For example, encryption is performed using AES encryption.

[1215] Step 6:

[1216] At the next communication timing, new dictionary data is sent to the terminal, and the dictionary data is used for the next data compression.

[1217] User operation and monitoring

[1218] Step 1:

[1219] Through the interface, users can check the data sent from their device to the server and its compression status in real time, for example by viewing the data transmission history on a smartphone app.

[1220] Step 2:

[1221] The user uses the interface to change basic settings such as compression method, data capture frequency, encryption settings, emotion recognition settings for the emotion engine, etc. For example, setting the sensitivity of the emotion engine.

[1222] Step 3:

[1223] If a communication error or compression failure occurs, the user is notified through the interface, for example, by receiving an error message via a push notification in the app.

[1224] This series of processing steps enables data containing emotional data to be sent efficiently and securely even on low-spec devices. The server analyzes the data, generates dictionary data that will be useful for the next data compression, and distributes it to the device. Users can check the data status in real time and make necessary setting changes to optimize system performance.

[1225] Example 2

[1226] 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."

[1227] In conventional low-spec devices, it has been difficult to efficiently and securely acquire and transmit data due to limited resources. Furthermore, even in systems that acquire user emotion data in real time and analyze it on the server side, many issues remain, such as improving data compression efficiency and reducing communication capacity. The present invention aims to solve these issues by providing a system that achieves effective communication and emotion recognition by efficiently and securely transmitting data and emotion data acquired from low-spec devices and performing advanced data analysis and compression on the server side.

[1228] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1229] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, emotion data acquisition means for recognizing user emotions using an emotion engine and acquiring the data, data transmission means for transmitting data to the server, data analysis means for analyzing data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, compression means for compressing the data and emotion data acquired by the terminal using the dictionary data received from the server, encryption means for securely encrypting and decrypting the data and dictionary data for communication, and user interface means for allowing the user to monitor the operating status of the terminal and server in real time and change settings. This enables simultaneous acquisition of data and emotion data from the low-spec terminal, realizes effective data analysis and compression on the server side, and enables highly efficient data transmission while reducing communication capacity.

[1230] A "low-spec terminal" is a computing device that has limited computing power and memory, but is equipped with sensors and basic input means.

[1231] "Data acquisition means" is a function that allows a terminal to collect data from sensors and external devices and temporarily store it in a buffer.

[1232] The "emotion data acquisition means" is a function that collects emotion data in real time using an emotion engine that recognizes the user's emotions.

[1233] The "data transmission means" is a communication function for transmitting data acquired by the terminal to the server.

[1234] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for future data compression.

[1235] The "dictionary data management means" is a function that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[1236] "Compression means" is a function that efficiently compresses the data and emotion data acquired by the terminal.

[1237] The "encryption means" is a function that securely encrypts data and dictionary data and decrypts received data.

[1238] The "user interface means" is an interface that allows the user to monitor the operating status of the terminal and server in real time and change various settings.

[1239] "Generative AI" is an AI technology that analyzes incoming data, extracts its features, and uses them for future data compression and analysis.

[1240] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[1241] The device acquires data from built-in sensors and external devices. For example, a DHT11 temperature sensor can be used. This data is temporarily stored in a buffer. The device also has an emotion engine that recognizes the user's emotions in real time. The emotion engine can use the OpenCV library. The recognized emotion data is also stored in the buffer.

[1242] Next, the device waits for new dictionary data to be sent from the server. Upon receiving it, it decrypts the RSA encryption using the OpenSSL library. The acquired data and emotion data are compressed using the received dictionary data. The LZ77 algorithm can be used for compression. The data is then RSA encrypted again using the OpenSSL library. The encrypted data is then sent to the server.

[1243] The server receives data sent from the device using the HTTPS protocol and temporarily stores it in storage. The storage used here could be an AWS S3 bucket, for example. The server then decrypts the received data and restores the original data. This decryption also uses the OpenSSL library. The restored data is passed to a generative AI system, which can use a custom TensorFlow-based model. The AI ​​extracts features from the data and emotion data and generates dictionary data to be used for the next data compression. The generated dictionary data is stored in a PostgreSQL database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[1244] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. This interface uses a React.js-based web application. Users can also use the interface to change basic settings such as the compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, if any problems occur with data transmission or compression, users will be notified via Firebase Cloud Messaging.

[1245] For example, a temperature sensor measures the room temperature and stores this data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and generates emotion data, such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives new dictionary data from the server and decrypts it. The temperature data and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[1246] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and stored in a database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[1247] An example prompt might be:

[1248] Please update the information as follows to generate the latest dictionary data:

[1249] Acquired temperature data: 22.5℃

[1250] Recognized emotion data: Happy

[1251] Acquisition frequency: Every 5 minutes

[1252]

[1253] Adjust the settings for receiving the latest dictionary data from the server, compressing and encrypting the data before sending it:

[1254] RSA encryption key: 2048 bits

[1255] Compression algorithm: LZ77

[1256] Communication protocol: HTTPS

[1257] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[1258] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1259] Step 1: Get the data

[1260] The device acquires data from built-in sensors (e.g., temperature sensor DHT11) and external devices.

[1261] Input: Room temperature data measured by a temperature sensor

[1262] Data processing: Temperature data is saved in a buffer

[1263] Output: Buffered room temperature data

[1264] Specific operation: The temperature sensor measures the room temperature as 22.5°C and stores the data in the terminal's buffer.

[1265] Step 2: Recognizing Emotional Data

[1266] The device uses an emotion engine (e.g., OpenCV library) to recognize the user's emotions in real time.

[1267] Input: User's facial expression data

[1268] Data Computing: Recognizing Emotions from Facial Expressions Using OpenCV Library

[1269] Output: Recognized emotion data

[1270] Specific operation: The emotion engine analyzes the user's facial expression, generates emotion data of "happy," and stores this in a buffer.

[1271] Step 3: Receiving and Decoding Dictionary Data

[1272] The terminal waits for new dictionary data to be sent from the server.

[1273] Input: Encrypted dictionary data sent from the server

[1274] Data Calculation: Decrypting dictionary data using the OpenSSL library

[1275] Output: Decoded dictionary data

[1276] Specific operation: The terminal receives the dictionary data using the HTTPS protocol, decrypts the RSA encryption, and loads the dictionary data into memory.

[1277] Step 4: Compress the data

[1278] The terminal compresses the acquired data and emotion data using the received dictionary data.

[1279] Input: Room temperature data and emotion data stored in the buffer, decoded dictionary data

[1280] Data operation: Compress data using the LZ77 algorithm

[1281] Output: Compressed data

[1282] Specific operation: Temperature data and emotion data are efficiently compressed using the LZ77 algorithm to generate compressed data.

[1283] Step 5: Encrypt the data

[1284] The terminal encrypts the compressed data.

[1285] Input: Compressed data

[1286] Data Computation: RSA encryption using the OpenSSL library

[1287] Output: Encrypted data

[1288] Specific operation: RSA encrypts the compressed data to generate encrypted data.

[1289] Step 6: Sending data

[1290] The terminal transmits the encrypted data to the server.

[1291] Input: Encrypted data

[1292] Data processing: Send data using HTTPS protocol

[1293] Output: Data sent to the server

[1294] What it does: Encrypts data and sends it to the server using the HTTPS protocol.

[1295] Step 7: Receiving Data

[1296] The server receives the data sent from the terminal and temporarily stores it in storage.

[1297] Input: Encrypted data sent from the device

[1298] Data processing: Save to storage

[1299] Output: Encrypted data stored in storage

[1300] Specific operation: The server saves the received data to an AWS S3 bucket using the HTTPS protocol.

[1301] Step 8: Decrypt and recover data

[1302] The server decrypts the received data and restores the original data.

[1303] Input: Encrypted and stored data

[1304] Data Computing: Decrypting RSA Encryption Using the OpenSSL Library

[1305] Output: Recovered room temperature data and emotion data

[1306] Specific operation: Decrypt the RSA encryption and restore the original temperature data (note: 22.5°C) and emotion data (e.g., "happy").

[1307] Step 9: Analyze the data and generate dictionary data

[1308] The server extracts features from the data and emotional data passed to the generative AI and generates new dictionary data.

[1309] Input: Recovered room temperature data and emotion data

[1310] Data Computation: Extracting Periodic Patterns and Emotional Fluctuations with TensorFlow

[1311] Output: New compression dictionary

[1312] How it works: The TensorFlow model analyzes periodic patterns and emotional fluctuations to generate new dictionary data.

[1313] Step 10: Preparing to save and send dictionary data

[1314] The server stores the generated dictionary data in a database, encrypts it, and prepares to send it to the terminal at the next communication timing.

[1315] Input: New dictionary data

[1316] Data processing: Dictionary data is stored in a database and encrypted.

[1317] Output: Encrypted dictionary data

[1318] What it does: It saves the new dictionary data in a PostgreSQL database and encrypts it using the OpenSSL library.

[1319] Step 11: Monitor users and change settings

[1320] The user monitors the operating status of the terminal and server through the interface and changes the settings as necessary.

[1321] Input: Real-time terminal and server activity

[1322] Data Calculation: Changing various settings

[1323] Output: Changed settings

[1324] What it does: Use a React.js-based web application to change data collection frequency, emotion recognition settings, and receive notification of communication errors.

[1325] (Application example 2)

[1326] 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."

[1327] Conventional systems that collect data from low-spec devices and analyze it on a server have had problems with data compression efficiency and communication costs. Furthermore, to improve the user experience, it is necessary to collect and appropriately analyze user emotion data in real time, but current systems are also insufficient in this regard. To solve these issues, a data compression and analysis system incorporating emotion recognition technology is needed.

[1328] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1329] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, emotion recognition means for recognizing the user's emotions in real time, data compression means for combining the acquired emotion data and sensor data, compressing the data and transmitting it to the server, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This makes it possible to effectively collect emotion data while keeping communication costs down during data compression and transmission.

[1330] A "low-spec terminal" is an information processing device with limited resources such as computing power and memory capacity.

[1331] "Data acquisition means" refers to a function or device for collecting data from low-spec terminals.

[1332] "Data transmission means" refers to a function or device for transmitting collected data to a server.

[1333] "Data analysis means" refers to a function or device that uses generative artificial intelligence to analyze data received on the server side and create dictionary data to be used for compression.

[1334] The "dictionary data management means" is a function or device that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[1335] "Emotion recognition means" refers to a function or device for detecting and analyzing a user's emotions in real time.

[1336] "Data compression means" refers to a function or device that combines acquired emotion data and sensor data, compresses it efficiently, and transmits it to the server.

[1337] "Encryption means" refers to a function or device for securely encrypting and decrypting data and dictionary data for communication.

[1338] "Generative AI" is an AI that has the ability to analyze collected data and extract specific patterns and characteristics.

[1339] System Configuration

[1340] The system of the present invention includes a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires sensor data and emotion data and transmits it to the server. The system includes the following main components:

[1341] Low-spec device: Equipped with sensors and emotion engines to collect data.

[1342] Server: Analyzes the received data and generates dictionary data to be used for compression.

[1343] User interface: The user can check the data transmission status and emotion data, and change settings.

[1344] Emotion engine: Recognizes user emotions in real time and provides them as data.

[1345] Program processing overview

[1346] Terminal side processing

[1347] The device acquires data from its built-in sensors and emotion engine and temporarily stores it in a buffer. The acquired data receives dictionary data sent from the server, decrypts it, and then compresses it. The compressed data is encrypted using RSA encryption and sent to the server.

[1348] For example, a temperature sensor measures the room temperature and stores the data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives and decrypts new dictionary data sent from the server. The temperature and emotion data are efficiently compressed, then encrypted using RSA encryption and sent to the server.

[1349] Server-side processing

[1350] The server receives the data sent from the device and temporarily stores it in storage. It then decrypts the received data and restores the original data. The restored data is passed to a generative AI for analysis. The AI ​​extracts features of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and sent to the device the next time it communicates.

[1351] As a concrete example, the server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This is then passed to the generative AI, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated and stored in the database. This dictionary data is then sent to the device in time for the next communication.

[1352] User operation and monitoring

[1353] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings. Furthermore, if a problem occurs, users will receive notifications through the interface.

[1354] As a concrete example, users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine. They can change the emotion recognition settings of the emotion engine from the app's settings screen and change the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them so that they can take appropriate measures.

[1355] Prompt sentence for generative AI model

[1356] As a concrete example, the following prompt can be used, using emotion data and sensor information from a generative AI model:

[1357] "The room temperature data is 26 degrees and the user is smiling. What would you suggest in this case?"

[1358] "The user has a sad expression and the room temperature is 18 degrees. What promotion would work?"

[1359] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[1360] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1361] Step 1:

[1362] The device collects sensor data.

[1363] Input: Data from temperature sensor, camera, and microphone.

[1364] Specific operation: The temperature sensor measures the room temperature, the camera captures the user's facial expressions, and the microphone captures voice data.

[1365] Output: Sensor data and raw image and audio data.

[1366] Step 2:

[1367] The device collects emotional data.

[1368] Input: Raw image and audio data.

[1369] Specific operation: The emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[1370] Output: Emotion data (e.g., the user's emotional state, such as "happy" or "sad").

[1371] Step 3:

[1372] The device stores sensor data and emotion data in a buffer.

[1373] Input: Sensor data and emotion data.

[1374] Specific operation: The acquired sensor data and emotion data are temporarily stored in a buffer in memory.

[1375] Output: The data stored in the buffer.

[1376] Step 4:

[1377] The terminal receives the dictionary data from the server and decrypts it.

[1378] Input: Encrypted dictionary data sent from the server.

[1379] Specific operation: The device receives the encrypted dictionary data and decrypts it using the decryption key.

[1380] Output: Decoded dictionary data.

[1381] Step 5:

[1382] The device compresses sensor data and emotion data.

[1383] Input: Buffered data and decoded dictionary data.

[1384] Specific operation: Sensor data and emotion data are compressed using dictionary data.

[1385] Output: Compressed data.

[1386] Step 6:

[1387] The terminal encrypts the compressed data and sends it to the server.

[1388] Input: Compressed data.

[1389] Specific operation: The compressed data is encrypted using the RSA encryption method and sent to the server.

[1390] Output: The encrypted data sent to the server.

[1391] Step 7:

[1392] The server receives the data and stores it temporarily.

[1393] Input: Encrypted data sent from the terminal.

[1394] Specific operation: The server receives the data and temporarily stores it in storage.

[1395] Output: Encrypted data in storage.

[1396] Step 8:

[1397] The server decrypts and recovers the encrypted data.

[1398] Input: Encrypted data stored in storage.

[1399] Specific operation: The data is decrypted using the decryption key to restore the original sensor data and emotion data.

[1400] Output: The recovered data.

[1401] Step 9:

[1402] The server passes the restored data to a generative artificial intelligence for analysis.

[1403] Input: Recovered data.

[1404] How it works: Data is passed to a generative AI to extract cyclical patterns and emotional fluctuations.

[1405] Output: Analysis results and feature data.

[1406] Step 10:

[1407] The server generates new dictionary data and stores it in the database.

[1408] Input: Analysis results and feature data.

[1409] Specific operation: The server generates new dictionary data based on this data to be used for the next data compression and stores it in the database.

[1410] Output: The new dictionary data stored in the database.

[1411] Step 11:

[1412] The server encrypts the new dictionary data and sends it to the device during the next communication.

[1413] Input: New dictionary data.

[1414] Specific operation: The new dictionary data is encrypted and sent to the device the next time communication occurs.

[1415] Output: Encrypted dictionary data.

[1416] 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.

[1417] 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.

[1418] 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.

[1419] [Fourth embodiment]

[1420] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1421] 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.

[1422] 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).

[1423] 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.

[1424] 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.

[1425] 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).

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] 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."

[1433] System Configuration

[1434] The system of the present invention is primarily composed of a low-spec terminal, a server, and a user interface. The low-spec terminal acquires data and transmits it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely.

[1435] Program processing overview

[1436] Terminal side processing

[1437] The device first acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[1438] Specific examples

[1439] The temperature sensor measures the room temperature every minute and stores this data in a buffer. New dictionary data is sent from the server, and the received dictionary data is decrypted. The acquired room temperature data is compressed using the decrypted dictionary data, and the compressed data is encrypted using RSA encryption. The encrypted data is then sent to the server.

[1440] Server-side processing

[1441] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts the data's characteristics and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is sent. The new dictionary data is also encrypted and sent to the device the next time communication occurs.

[1442] Specific examples

[1443] The server receives the data sent from the temperature sensor and stores it in storage. The received data is decrypted using AES encryption to restore the original room temperature data. This data is then passed to a generative AI system, which extracts periodic patterns and fluctuation characteristics. A new compression dictionary is generated based on the extracted characteristics and saved in the database as version 1.0. This dictionary data is then encrypted in time for the next communication and sent to the device.

[1444] User operation and monitoring

[1445] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings. Furthermore, if any problems occur with data transmission or compression, users can be notified through the interface.

[1446] Specific examples

[1447] The user uses a smartphone app to monitor the data transmission status of the room temperature sensor in real time. The user can change the data acquisition frequency from 10 minutes to 5 minutes from the app's settings screen. If a communication error occurs, the user receives a notification from the app and retries.

[1448] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals, and performs advanced data compression and analysis on the server side, achieving effective communication.

[1449] The processing flow will be explained below.

[1450] Program processing steps

[1451] Terminal side processing

[1452] Step 1:

[1453] The device receives data from built-in sensors and external devices, such as current sensor readings and environmental data.

[1454] Step 2:

[1455] The acquired data is temporarily stored in a buffer. For example, room temperature data is acquired and temporarily stored in the device's memory.

[1456] Step 3:

[1457] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[1458] Step 4:

[1459] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[1460] Step 5:

[1461] The received dictionary data is used to compress the acquired data in the buffer. This compression can be performed even on low-spec devices by using a lightweight algorithm.

[1462] Step 6:

[1463] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[1464] Step 7:

[1465] The encrypted compressed data is sent to a server via the Internet or a dedicated communication protocol.

[1466] Server-side processing

[1467] Step 1:

[1468] The server receives the encrypted data sent from the device and temporarily stores it in storage.

[1469] Step 2:

[1470] The received data is decrypted to restore the original compressed data, for example, using RSA encryption to decrypt the data.

[1471] Step 3:

[1472] The decoded data is passed to a generative AI for data analysis, which extracts data features and generates dictionary data for use in the next data compression.

[1473] Step 4:

[1474] The generated dictionary data is saved in a database. This save is also version-controlled and registered as the latest dictionary data.

[1475] Step 5:

[1476] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent, for example, by encrypting it using AES encryption.

[1477] Step 6:

[1478] The new dictionary data is sent to the terminal at the next communication timing, and this data is used in the next compression process.

[1479] User operation and monitoring

[1480] Step 1:

[1481] Through the interface, users can check the data sent from the device to the server and the compression status in real time. For example, they can view the room temperature data history on a smartphone app.

[1482] Step 2:

[1483] Users use the interface to change basic settings such as compression method, data retrieval frequency, and encryption settings, for example, changing the data retrieval frequency from 10 minutes to 5 minutes.

[1484] Step 3:

[1485] If a communication error or compression failure occurs, the user is notified through the interface, for example, the app receives an error notification and can choose to retry.

[1486] Example 1

[1487] 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."

[1488] In conventional data collection systems, the challenge was to efficiently transmit data from low-spec devices to a server while keeping communication capacity low. Furthermore, the security of the transmitted data was not adequately ensured, resulting in a lack of confidentiality. This could lead to inefficiencies in the data collection and analysis process and higher costs.

[1489] 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.

[1490] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, data compression means for receiving the dictionary data at the terminal and compressing the acquired data, data compression means for compressing data acquired by the terminal using the decrypted dictionary data, data encryption means for encrypting the compressed data and transmitting it to the server, data recovery means for decrypting the received data at the server and generating the original data, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This enables fast and efficient data transmission from the low-spec terminal and secure data communication while reducing communication capacity.

[1491] A "low-spec device" is an electronic device that has limited processing power but is equipped with sensors and data collection functions to acquire and transmit data.

[1492] "Data acquisition means" is a function that collects data from sensors and external devices and temporarily stores it in a buffer inside the terminal.

[1493] The "data transmission means" is a communication function for transmitting data collected by the terminal to the server.

[1494] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for data compression.

[1495] The "dictionary data management means" is a management function for storing the generated dictionary data in a database and transmitting it to the terminal the next time data is transmitted.

[1496] The "data compression means" is a function that compresses data acquired by the terminal using dictionary data.

[1497] The "data encryption means" is a function that encrypts the compressed data in a secure manner and transmits it to the server.

[1498] The "data restoration means" is a function that decrypts data received by the server and restores the original data.

[1499] The "encryption means" is a function for securely encrypting and decrypting data and dictionary data for communication.

[1500] "Generative AI" is an AI technology that analyzes the characteristics of data and generates new data compression dictionaries.

[1501] The system of the present invention achieves efficient and secure data collection and transmission using low-spec terminals, a server, and a user interface. This system uses the following hardware and software.

[1502] Terminal side processing

[1503] The device acquires data from built-in sensors such as a temperature sensor and external devices. For example, the temperature sensor measures the room temperature every minute and temporarily stores this data in a buffer within the device. The device then waits for new dictionary data to be sent from the server and decrypts the received dictionary data using RSA encryption. The decrypted dictionary data is used to compress the acquired temperature data, and the compressed data is then encrypted using the same RSA encryption. The encrypted data is then sent from the device to the server.

[1504] Server-side processing

[1505] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted using AES encryption to restore the original data. The restored data is then analyzed using generative artificial intelligence to extract data characteristics. New dictionary data is generated based on these characteristics and stored in the database. When the next communication is scheduled, this dictionary data is encrypted using RSA encryption and sent back to the device.

[1506] User operation and monitoring

[1507] Users can check the data being sent from their device to the server and its compression status in real time through an interface, such as a smartphone app. Users can also change data retrieval frequency and encryption settings from the app's settings screen. In addition, if a communication error occurs, users will receive a notification from the app and can retry.

[1508] Through each of the above stages, this system efficiently and securely transmits data from low-spec devices, and performs advanced data compression and analysis on the server side to achieve effective communication.

[1509] Specific examples

[1510] The temperature sensor measures the room temperature every minute and stores the data in a buffer. New dictionary data is sent from the server, and the device receives and decrypts the dictionary data. The acquired room temperature data is compressed using the dictionary data and encrypted with RSA encryption. The encrypted data is then sent to the server.

[1511] The server receives the data and temporarily stores it in storage. It decrypts it using AES encryption to restore the original data. It then uses generative artificial intelligence to extract features from the data, generates a new compressed dictionary, and stores it in the database. The dictionary data is then encrypted at the next communication time and sent to the device.

[1512] The user monitors the data transmission status of the room temperature sensor in real time using a smartphone app. The user can change the data acquisition frequency from the app's settings screen, and if a communication error occurs, they will receive a notification and try again.

[1513] Prompt Sentence Examples

[1514] Design a system with the following data processing flow:

[1515] 1. The device acquires data from the sensor and stores it in a buffer.

[1516] 2. The device receives the new dictionary data from the server and decrypts it.

[1517] 3. The data acquired by the terminal is compressed using dictionary data and encrypted with RSA.

[1518] 4. The device sends the encrypted data to the server.

[1519] 5. The server stores the received data in storage and decrypts it using AES encryption.

[1520] 6. The server passes the original data to the generation AI, which extracts features and generates new dictionary data.

[1521] 7. The generated dictionary data is encrypted and sent to the device during the next communication.

[1522] 8. The user monitors the data transmission and compression status through the interface and changes the settings as necessary.

[1523] Please design a specific system based on this flow.

[1524] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1525] The flow of this system's program processing

[1526] Terminal side processing

[1527] Step 1:

[1528] The terminal acquires data from sensors and external devices. Specifically, the temperature sensor measures the room temperature every minute and stores this data in a buffer. The input is an analog signal from the temperature sensor, and the output is digital data stored in the buffer. For example, data for a room temperature of 25°C is acquired and stored in the buffer.

[1529] Step 2:

[1530] The terminal waits for new dictionary data to be sent from the server. When the dictionary data arrives, it is decrypted using RSA encryption. The input is the encrypted dictionary data sent from the server, and the output is the decrypted dictionary data. Specifically, let XYZ be the dictionary data decrypted by RSA encryption.

[1531] Step 3:

[1532] The terminal compresses the acquired data using the received dictionary data. The input is the room temperature data in the buffer and the decoded dictionary data XYZ, and the output is the compressed data. Specifically, the room temperature data of 25°C is compressed using the dictionary data XYZ to generate the compressed data ABCDE.

[1533] Step 4:

[1534] The terminal encrypts the compressed data using RSA encryption. The input is compressed data ABCDE, and the output is encrypted data FGHIJ. The compressed data ABCDE is encrypted using RSA encryption to generate data FGHIJ.

[1535] Step 5:

[1536] The terminal sends encrypted data to the server. The input is the encrypted data FGHIJ, and the output is the data to be sent to the server. The terminal sends data by specifying the IP address and port number.

[1537] Server-side processing

[1538] Step 6:

[1539] The server receives the data sent from the terminal and temporarily stores it in storage. The input is the encrypted data FGHIJ sent from the terminal, and the output is the encrypted data stored in storage. The server temporarily stores the data and stores it securely.

[1540] Step 7:

[1541] The server decrypts the received data and restores the original data. The input is the encrypted data FGHIJ stored in storage, and the output is the restored room temperature data of 25°C. The data FGHIJ is decrypted using AES encryption, and the original room temperature data of 25°C is restored.

[1542] Step 8:

[1543] The server analyzes the restored data using generative artificial intelligence and extracts data features. The input is room temperature data of 25°C, and the output is the extracted data features. The generative artificial intelligence analyzes periodic patterns and fluctuation features to generate feature data.

[1544] Step 9:

[1545] The server generates new dictionary data based on the features and saves it in the database. The input is the extracted data features, and the output is the generated new dictionary data XYZ+1. The server creates a new compression dictionary and saves it in the database.

[1546] Step 10:

[1547] The server encrypts the new dictionary data and sends it to the terminal at the next communication timing. The input is the generated dictionary data XYZ+1, and the output is the encrypted dictionary data. The dictionary data XYZ+1 is encrypted using RSA encryption and sent to the terminal at the next communication timing.

[1548] User operation and monitoring

[1549] Step 11:

[1550] Through the interface, the user can check the data sent from the device to the server and its compression status in real time. The input is the data transmission status from the server, and the output is the information displayed on the user's interface screen. The user monitors the data transmission status of the room temperature sensor using a smartphone app.

[1551] Step 12:

[1552] The user uses the interface to change the data capture frequency and encryption settings. The input is the setting change instruction entered by the user into the app, and the output is the changed data capture frequency and encryption settings. For example, the user changes the data capture frequency from 10 minutes to 5 minutes.

[1553] Step 13:

[1554] If a communication error occurs, the user is notified through the interface. The input is the information about the communication error, and the output is a notification displayed on the user's interface. When the user receives the notification, they can press the retry button to try to resend the data.

[1555] (Application example 1)

[1556] 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."

[1557] In communication systems that use low-spec terminals, transmitting data efficiently and securely is a challenge. Surveillance systems also require video data to be compressed and encrypted and transmitted to a server in real time. However, performing such processing on low-spec terminals is technically difficult, so there is a need to improve the efficiency of data transmission while ensuring security.

[1558] 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.

[1559] In this invention, the server includes a data acquisition means for acquiring data from a low-spec terminal, a data transmission means for transmitting data to the server, a data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, a dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, an encryption means for securely encrypting and decrypting the data and dictionary data for communication, and a monitoring device means for a monitoring device to acquire video data, compress and encrypt it, and transmit it to the server. This enables efficient and secure data transmission even from low-spec terminals, and also enables real-time video data transmission and anomaly detection in a monitoring system.

[1560] A "low-spec device" is a device that has limited processing power and memory compared to typical high-performance devices.

[1561] "Data acquisition means" refers to methods and devices for collecting information from devices such as various sensors and cameras.

[1562] "Data transmission means" refers to a method or apparatus for transferring acquired data to a server or other device.

[1563] A "server" is a computer system that provides services to client devices over a network.

[1564] "Generative AI" is an AI technology that has the ability to analyze data and generate new data.

[1565] A "data analysis means" is a method or device for processing received data and extracting useful information.

[1566] "Dictionary data" is a database for efficiently handling specific patterns and expressions in data compression and data management.

[1567] The "dictionary data management means" refers to a method or device for storing the generated dictionary data and using it as needed.

[1568] A "cryptographic means" is a method or device for encrypting and decrypting data for secure communication.

[1569] A "surveillance device" is a device such as a camera or sensor used to monitor a specific area.

[1570] A "compression means" is a method or device for compressing data to reduce the volume of the data.

[1571] An "encryption means" is a method or device for encrypting data to protect it from third parties.

[1572] A "decryption means" is a method or device for restoring encrypted data to its original form.

[1573] "Adaptively repeating the transmission and reception of dictionary data with the data transmission means" refers to a process of efficiently transmitting and receiving data according to the network state and the properties of the data.

[1574] "Monitoring devices acquire video data in real time, compress, encrypt, and transmit it, and then analyze the data to detect abnormalities" refers to the process of instantly processing video data acquired by devices such as surveillance cameras to check for safety and abnormalities.

[1575] A system for implementing the present invention comprises a low-spec terminal, a server, and a monitoring device.

[1576] Hardware and software used

[1577] The present invention uses the following hardware and software.

[1578] Hardware: Low-spec devices (e.g., IoT devices and simple sensors), servers, and surveillance devices (e.g., surveillance cameras).

[1579] Software: Python, OpenCV, RSA, PyCryptodome, Requests library.

[1580] Program processing overview

[1581] Terminal side processing

[1582] The device first acquires data from built-in sensors and monitoring devices. This data is temporarily stored in a buffer. It then waits for new dictionary data to be sent from the server, and decrypts it when it receives it. The acquired data is compressed using the received dictionary data, and the compressed data is then encrypted. The encrypted data is then sent to the server. This series of steps allows data to be transmitted efficiently and securely, even on low-spec devices.

[1583] Processing on the monitoring device

[1584] The surveillance device (surveillance camera) captures video data in real time. This video data is temporarily stored in a buffer. The stored video data is compressed and encrypted in the same way as on low-spec devices. Dictionary data sent from the server is used for compression, and AES and RSA are used for encryption. The encrypted video data is then sent to the server.

[1585] Server-side processing

[1586] The server receives data sent from the terminals and monitoring devices and temporarily stores it in storage. The received data is decrypted and the restored data is analyzed using generative artificial intelligence. The generative artificial intelligence extracts features from the data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used the next time data is transmitted. The new dictionary data is also encrypted and sent to the terminals and monitoring devices at the next communication timing.

[1587] Specific examples

[1588] For example, suppose a surveillance camera monitors an office entrance in real time and sends the video data to a server. This data is temporarily stored in a buffer and compressed using dictionary data sent from the server. The compressed data is encrypted with AES encryption, and the AES key is encrypted with RSA encryption. The encrypted data is sent to the server, where it is decrypted and analyzed. New dictionary data generated based on the results of this analysis is used for the next communication.

[1589] Prompt Sentence Examples

[1590] "How can I compress and encrypt surveillance camera data more efficiently?"

[1591] In this way, the system of the present invention efficiently and securely transmits data from low-spec terminals and monitoring devices, and performs advanced data compression and analysis on the server side, achieving effective communication.

[1592] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1593] Step 1:

[1594] The terminal acquires data from sensors and monitoring devices.

[1595] Input: Raw data from sensors and surveillance cameras (e.g., temperature data and video data).

[1596] Specific operation: The device reads data from the built-in sensors and camera and temporarily stores it in a buffer.

[1597] Output: Temporarily stored raw data.

[1598] Step 2:

[1599] The terminal receives the dictionary data from the server and decrypts it.

[1600] Input: Encrypted dictionary data sent from the server.

[1601] Specific operation: The terminal waits for dictionary data sent from the server, and when it receives it, it decrypts the dictionary data using RSA encryption.

[1602] Output: Decoded dictionary data.

[1603] Step 3:

[1604] The raw data acquired by the terminal is compressed using the decrypted dictionary data.

[1605] Input: Raw data, decoded dictionary data.

[1606] Specific operation: The terminal refers to the dictionary data and applies a compression algorithm to the raw data to reduce the data size.

[1607] Output: Compressed data.

[1608] Step 4:

[1609] The terminal encrypts the compressed data using AES encryption and sends it to the server.

[1610] Input: Compressed data, AES key.

[1611] Specific operation: The terminal encrypts the compressed data using the AES algorithm, and the AES key is re-encrypted with RSA before being sent to the server.

[1612] Output: The encrypted data and the encrypted AES key.

[1613] Step 5:

[1614] The server receives the encrypted data and stores it temporarily in storage.

[1615] Input: Encrypted data, Encrypted AES key.

[1616] Specific operation: The server receives the data sent from the terminal and stores it in storage.

[1617] Output: The stored encrypted data.

[1618] Step 6:

[1619] The server decrypts the received data and analyzes the restored data using generative artificial intelligence.

[1620] Input: Stored encrypted data, encrypted AES key.

[1621] How it works: The server decrypts the encrypted AES key with RSA, then uses that AES key to decrypt the stored data, and then passes the recovered data to a generative AI for analysis.

[1622] Output: Analysis results and feature extraction data.

[1623] Step 7:

[1624] The server generates new dictionary data based on the analysis results and stores it in the database.

[1625] Input: Analysis results, feature extraction data.

[1626] Specific operation: The server creates new dictionary data based on the analysis results of the generative artificial intelligence, and stores the dictionary data in a database under version control.

[1627] Output: The generated dictionary data.

[1628] Step 8:

[1629] The server encrypts the new dictionary data and sends it to the device the next time it communicates.

[1630] Input: The generated dictionary data.

[1631] Specific operation: The server encrypts the new dictionary data with AES, encrypts the AES key with RSA, and then sends it to the terminal during the next data communication.

[1632] Output: Encrypted dictionary data.

[1633] In this way, the system of the present invention ensures efficient and secure transmission of data through processing steps, and also leverages generative artificial intelligence to improve data compression efficiency and enable real-time analysis of video data from surveillance devices.

[1634] 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.

[1635] System Configuration

[1636] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[1637] Program processing overview

[1638] Terminal side processing

[1639] The device acquires data from built-in sensors and external devices. This data is temporarily stored in a buffer. In addition, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The recognized emotion data is also stored in a buffer. The device then waits for new dictionary data sent from the server and decrypts it when it receives it. The acquired data and emotion data are compressed and encrypted using the received dictionary data. The encrypted data is then sent to the server.

[1640] Specific examples

[1641] The temperature sensor measures the room temperature and stores this data in a buffer. At the same time, the emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. The device then receives new dictionary data from the server and decrypts it. The temperature and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[1642] Server-side processing

[1643] The server receives the data sent from the device and temporarily stores it in storage. The received data is decrypted and the restored data is passed to the generative AI for analysis. The AI ​​extracts the characteristics of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and will be used for the next data transmission. The dictionary data is then encrypted and sent to the device at the next communication timing.

[1644] Specific examples

[1645] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and saved as version 1.0 in the database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[1646] User operation and monitoring

[1647] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, users will be notified through the interface if any problems occur with data transmission or compression.

[1648] Specific examples

[1649] Users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine in real time. They can change the emotion recognition settings of the emotion engine from the app's settings screen, changing the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them and they can take appropriate measures.

[1650] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, achieving effective communication and emotion recognition.

[1651] The processing flow will be explained below.

[1652] Program processing steps

[1653] Terminal side processing

[1654] Step 1:

[1655] The device collects data from built-in sensors, such as a temperature sensor that measures the current room temperature in real time.

[1656] Step 2:

[1657] The device activates an emotion engine and analyzes the user's facial expressions and voice data to recognize their emotions. This emotion data is labeled as "happy" or "sad."

[1658] Step 3:

[1659] The acquired data and emotion data are temporarily stored in a buffer. For example, room temperature data and emotion data such as "happy" are stored.

[1660] Step 4:

[1661] The device waits for new dictionary data to be sent from the server, which typically occurs at regular intervals.

[1662] Step 5:

[1663] Upon receiving the dictionary data, the terminal decrypts the data, for example, decrypting dictionary data encrypted with AES encryption.

[1664] Step 6:

[1665] The received dictionary data is used to compress the acquired data and emotion data in the buffer. For example, the dictionary data is used to compress the data using the LZMA algorithm.

[1666] Step 7:

[1667] The compressed data is then further encrypted to prepare for secure communication, for example by encrypting the data using RSA encryption.

[1668] Step 8:

[1669] The encrypted compressed data is sent to the server using either HTTP over the Internet or a dedicated protocol.

[1670] Server-side processing

[1671] Step 1:

[1672] The server waits for the encrypted data sent from the device and receives it when it arrives. The received data is temporarily stored in storage.

[1673] Step 2:

[1674] The received encrypted data is decrypted, for example, using RSA encryption.

[1675] Step 3:

[1676] The decoded data is passed to a generative AI for analysis, which extracts features from the data and emotion data and generates dictionary data for use in the next compression.

[1677] Step 4:

[1678] The generated dictionary data is saved in the database. This save also performs version control and is used as the latest dictionary data.

[1679] Step 5:

[1680] The dictionary data is encrypted and prepared for distribution to the terminal the next time data is sent. For example, encryption is performed using AES encryption.

[1681] Step 6:

[1682] At the next communication timing, new dictionary data is sent to the terminal, and the dictionary data is used for the next data compression.

[1683] User operation and monitoring

[1684] Step 1:

[1685] Through the interface, users can check the data sent from their device to the server and its compression status in real time, for example by viewing the data transmission history on a smartphone app.

[1686] Step 2:

[1687] The user uses the interface to change basic settings such as compression method, data capture frequency, encryption settings, emotion recognition settings for the emotion engine, etc. For example, setting the sensitivity of the emotion engine.

[1688] Step 3:

[1689] If a communication error or compression failure occurs, the user is notified through the interface, for example, by receiving an error message via a push notification in the app.

[1690] This series of processing steps enables data containing emotional data to be sent efficiently and securely even on low-spec devices. The server analyzes the data, generates dictionary data that will be useful for the next data compression, and distributes it to the device. Users can check the data status in real time and make necessary setting changes to optimize system performance.

[1691] Example 2

[1692] 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."

[1693] In conventional low-spec devices, it has been difficult to efficiently and securely acquire and transmit data due to limited resources. Furthermore, even in systems that acquire user emotion data in real time and analyze it on the server side, many issues remain, such as improving data compression efficiency and reducing communication capacity. The present invention aims to solve these issues by providing a system that achieves effective communication and emotion recognition by efficiently and securely transmitting data and emotion data acquired from low-spec devices and performing advanced data analysis and compression on the server side.

[1694] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1695] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, emotion data acquisition means for recognizing user emotions using an emotion engine and acquiring the data, data transmission means for transmitting data to the server, data analysis means for analyzing data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, compression means for compressing the data and emotion data acquired by the terminal using the dictionary data received from the server, encryption means for securely encrypting and decrypting the data and dictionary data for communication, and user interface means for allowing the user to monitor the operating status of the terminal and server in real time and change settings. This enables simultaneous acquisition of data and emotion data from the low-spec terminal, realizes effective data analysis and compression on the server side, and enables highly efficient data transmission while reducing communication capacity.

[1696] A "low-spec terminal" is a computing device that has limited computing power and memory, but is equipped with sensors and basic input means.

[1697] "Data acquisition means" is a function that allows a terminal to collect data from sensors and external devices and temporarily store it in a buffer.

[1698] The "emotion data acquisition means" is a function that collects emotion data in real time using an emotion engine that recognizes the user's emotions.

[1699] The "data transmission means" is a communication function for transmitting data acquired by the terminal to the server.

[1700] The "data analysis means" is a function that uses generative artificial intelligence to analyze data received by the server and generate dictionary data to be used for future data compression.

[1701] The "dictionary data management means" is a function that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[1702] "Compression means" is a function that efficiently compresses the data and emotion data acquired by the terminal.

[1703] The "encryption means" is a function that securely encrypts data and dictionary data and decrypts received data.

[1704] The "user interface means" is an interface that allows the user to monitor the operating status of the terminal and server in real time and change various settings.

[1705] "Generative AI" is an AI technology that analyzes incoming data, extracts its features, and uses them for future data compression and analysis.

[1706] The present invention is a system consisting of a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires data and sends it to the server. The server analyzes the received data and generates and manages dictionary data used for compression. The data and dictionary data are encrypted and communicated securely. The emotion engine recognizes the user's emotions and provides that data to the terminal.

[1707] The device acquires data from built-in sensors and external devices. For example, a DHT11 temperature sensor can be used. This data is temporarily stored in a buffer. The device also has an emotion engine that recognizes the user's emotions in real time. The emotion engine can use the OpenCV library. The recognized emotion data is also stored in the buffer.

[1708] Next, the device waits for new dictionary data to be sent from the server. Upon receiving it, it decrypts the RSA encryption using the OpenSSL library. The acquired data and emotion data are compressed using the received dictionary data. The LZ77 algorithm can be used for compression. The data is then RSA encrypted again using the OpenSSL library. The encrypted data is then sent to the server.

[1709] The server receives data sent from the device using the HTTPS protocol and temporarily stores it in storage. The storage used here could be an AWS S3 bucket, for example. The server then decrypts the received data and restores the original data. This decryption also uses the OpenSSL library. The restored data is passed to a generative AI system, which can use a custom TensorFlow-based model. The AI ​​extracts features from the data and emotion data and generates dictionary data to be used for the next data compression. The generated dictionary data is stored in a PostgreSQL database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[1710] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. This interface uses a React.js-based web application. Users can also use the interface to change basic settings such as the compression method, data acquisition frequency, encryption settings, and emotion recognition settings for the emotion engine. Furthermore, if any problems occur with data transmission or compression, users will be notified via Firebase Cloud Messaging.

[1711] For example, a temperature sensor measures the room temperature and stores this data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and generates emotion data, such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives new dictionary data from the server and decrypts it. The temperature data and emotion data are efficiently compressed and then encrypted using RSA encryption. The encrypted data is then sent to the server.

[1712] The server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This data is then passed to a generative AI system, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated based on the extracted features and stored in a database. This dictionary data is then encrypted in time for the next communication and sent to the device.

[1713] An example prompt might be:

[1714] Please update the information as follows to generate the latest dictionary data:

[1715] Acquired temperature data: 22.5℃

[1716] Recognized emotion data: Happy

[1717] Acquisition frequency: Every 5 minutes

[1718]

[1719] Adjust the settings for receiving the latest dictionary data from the server, compressing and encrypting the data before sending it:

[1720] RSA encryption key: 2048 bits

[1721] Compression algorithm: LZ77

[1722] Communication protocol: HTTPS

[1723] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[1724] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1725] Step 1: Get the data

[1726] The device acquires data from built-in sensors (e.g., temperature sensor DHT11) and external devices.

[1727] Input: Room temperature data measured by a temperature sensor

[1728] Data processing: Temperature data is saved in a buffer

[1729] Output: Buffered room temperature data

[1730] Specific operation: The temperature sensor measures the room temperature as 22.5°C and stores the data in the terminal's buffer.

[1731] Step 2: Recognizing Emotional Data

[1732] The device uses an emotion engine (e.g., OpenCV library) to recognize the user's emotions in real time.

[1733] Input: User's facial expression data

[1734] Data Computing: Recognizing Emotions from Facial Expressions Using OpenCV Library

[1735] Output: Recognized emotion data

[1736] Specific operation: The emotion engine analyzes the user's facial expression, generates emotion data of "happy," and stores this in a buffer.

[1737] Step 3: Receiving and Decoding Dictionary Data

[1738] The terminal waits for new dictionary data to be sent from the server.

[1739] Input: Encrypted dictionary data sent from the server

[1740] Data Calculation: Decrypting dictionary data using the OpenSSL library

[1741] Output: Decoded dictionary data

[1742] Specific operation: The terminal receives the dictionary data using the HTTPS protocol, decrypts the RSA encryption, and loads the dictionary data into memory.

[1743] Step 4: Compress the data

[1744] The terminal compresses the acquired data and emotion data using the received dictionary data.

[1745] Input: Room temperature data and emotion data stored in the buffer, decoded dictionary data

[1746] Data operation: Compress data using the LZ77 algorithm

[1747] Output: Compressed data

[1748] Specific operation: Temperature data and emotion data are efficiently compressed using the LZ77 algorithm to generate compressed data.

[1749] Step 5: Encrypt the data

[1750] The terminal encrypts the compressed data.

[1751] Input: Compressed data

[1752] Data Computation: RSA encryption using the OpenSSL library

[1753] Output: Encrypted data

[1754] Specific operation: RSA encrypts the compressed data to generate encrypted data.

[1755] Step 6: Sending data

[1756] The terminal transmits the encrypted data to the server.

[1757] Input: Encrypted data

[1758] Data processing: Send data using HTTPS protocol

[1759] Output: Data sent to the server

[1760] What it does: Encrypts data and sends it to the server using the HTTPS protocol.

[1761] Step 7: Receiving Data

[1762] The server receives the data sent from the terminal and temporarily stores it in storage.

[1763] Input: Encrypted data sent from the device

[1764] Data processing: Save to storage

[1765] Output: Encrypted data stored in storage

[1766] Specific operation: The server saves the received data to an AWS S3 bucket using the HTTPS protocol.

[1767] Step 8: Decrypt and recover data

[1768] The server decrypts the received data and restores the original data.

[1769] Input: Encrypted and stored data

[1770] Data Computing: Decrypting RSA Encryption Using the OpenSSL Library

[1771] Output: Recovered room temperature data and emotion data

[1772] Specific operation: Decrypt the RSA encryption and restore the original temperature data (note: 22.5°C) and emotion data (e.g., "happy").

[1773] Step 9: Analyze the data and generate dictionary data

[1774] The server extracts features from the data and emotional data passed to the generative AI and generates new dictionary data.

[1775] Input: Recovered room temperature data and emotion data

[1776] Data Computation: Extracting Periodic Patterns and Emotional Fluctuations with TensorFlow

[1777] Output: New compression dictionary

[1778] How it works: The TensorFlow model analyzes periodic patterns and emotional fluctuations to generate new dictionary data.

[1779] Step 10: Preparing to save and send dictionary data

[1780] The server stores the generated dictionary data in a database, encrypts it, and prepares to send it to the terminal at the next communication timing.

[1781] Input: New dictionary data

[1782] Data processing: Dictionary data is stored in a database and encrypted.

[1783] Output: Encrypted dictionary data

[1784] What it does: It saves the new dictionary data in a PostgreSQL database and encrypts it using the OpenSSL library.

[1785] Step 11: Monitor users and change settings

[1786] The user monitors the operating status of the terminal and server through the interface and changes the settings as necessary.

[1787] Input: Real-time terminal and server activity

[1788] Data Calculation: Changing various settings

[1789] Output: Changed settings

[1790] What it does: Use a React.js-based web application to change data collection frequency, emotion recognition settings, and receive notification of communication errors.

[1791] (Application example 2)

[1792] 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."

[1793] Conventional systems that collect data from low-spec devices and analyze it on a server have had problems with data compression efficiency and communication costs. Furthermore, to improve the user experience, it is necessary to collect and appropriately analyze user emotion data in real time, but current systems are also insufficient in this regard. To solve these issues, a data compression and analysis system incorporating emotion recognition technology is needed.

[1794] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1795] In this invention, the server includes data acquisition means for acquiring data from the low-spec terminal, data transmission means for transmitting data to the server, data analysis means for analyzing the data received by the server using generative artificial intelligence and generating dictionary data to be used for compression, dictionary data management means for saving the generated dictionary data in a database and transmitting it from the server to the terminal for use the next time data is transmitted, emotion recognition means for recognizing the user's emotions in real time, data compression means for combining the acquired emotion data and sensor data, compressing the data and transmitting it to the server, and encryption means for securely encrypting and decrypting the data and dictionary data for communication. This makes it possible to effectively collect emotion data while keeping communication costs down during data compression and transmission.

[1796] A "low-spec terminal" is an information processing device with limited resources such as computing power and memory capacity.

[1797] "Data acquisition means" refers to a function or device for collecting data from low-spec terminals.

[1798] "Data transmission means" refers to a function or device for transmitting collected data to a server.

[1799] "Data analysis means" refers to a function or device that uses generative artificial intelligence to analyze data received on the server side and create dictionary data to be used for compression.

[1800] The "dictionary data management means" is a function or device that stores the generated dictionary data in a database and transmits it from the server to the terminal for use the next time data is transmitted.

[1801] "Emotion recognition means" refers to a function or device for detecting and analyzing a user's emotions in real time.

[1802] "Data compression means" refers to a function or device that combines acquired emotion data and sensor data, compresses it efficiently, and transmits it to the server.

[1803] "Encryption means" refers to a function or device for securely encrypting and decrypting data and dictionary data for communication.

[1804] "Generative AI" is an AI that has the ability to analyze collected data and extract specific patterns and characteristics.

[1805] System Configuration

[1806] The system of the present invention includes a low-spec terminal, a server, a user interface, and an emotion engine. The low-spec terminal acquires sensor data and emotion data and transmits it to the server. The system includes the following main components:

[1807] Low-spec device: Equipped with sensors and emotion engines to collect data.

[1808] Server: Analyzes the received data and generates dictionary data to be used for compression.

[1809] User interface: The user can check the data transmission status and emotion data, and change settings.

[1810] Emotion engine: Recognizes user emotions in real time and provides them as data.

[1811] Program processing overview

[1812] Terminal side processing

[1813] The device acquires data from its built-in sensors and emotion engine and temporarily stores it in a buffer. The acquired data receives dictionary data sent from the server, decrypts it, and then compresses it. The compressed data is encrypted using RSA encryption and sent to the server.

[1814] For example, a temperature sensor measures the room temperature and stores the data in a buffer. At the same time, an emotion engine analyzes the user's facial expressions and voice to generate emotion data such as "happy" or "sad." This emotion data is also stored in the buffer. Next, the device receives and decrypts new dictionary data sent from the server. The temperature and emotion data are efficiently compressed, then encrypted using RSA encryption and sent to the server.

[1815] Server-side processing

[1816] The server receives the data sent from the device and temporarily stores it in storage. It then decrypts the received data and restores the original data. The restored data is passed to a generative AI for analysis. The AI ​​extracts features of the data and emotional data and generates dictionary data to be used for the next data compression. This dictionary data is stored in a database and sent to the device the next time it communicates.

[1817] As a concrete example, the server receives data sent from the temperature sensor and emotion engine and stores it in storage. The received data is decrypted using RSA encryption to restore the original temperature and emotion data. This is then passed to the generative AI, which extracts periodic patterns and emotional fluctuations. A new compressed dictionary is generated and stored in the database. This dictionary data is then sent to the device in time for the next communication.

[1818] User operation and monitoring

[1819] Through the interface, users can check the data being sent from their device to the server and its compression status in real time. They can also use the interface to change basic settings such as compression method, data acquisition frequency, encryption settings, and emotion recognition settings. Furthermore, if a problem occurs, users will receive notifications through the interface.

[1820] As a concrete example, users can use a smartphone app to monitor the data transmission status of the room temperature sensor and emotion engine. They can change the emotion recognition settings of the emotion engine from the app's settings screen and change the data acquisition frequency from 10 minutes to 5 minutes. In addition, if a communication error occurs, the app will notify them so that they can take appropriate measures.

[1821] Prompt sentence for generative AI model

[1822] As a concrete example, the following prompt can be used, using emotion data and sensor information from a generative AI model:

[1823] "The room temperature data is 26 degrees and the user is smiling. What would you suggest in this case?"

[1824] "The user has a sad expression and the room temperature is 18 degrees. What promotion would work?"

[1825] In this way, the system of the present invention efficiently and securely transmits data and emotion data from low-spec devices, and performs advanced data compression and analysis on the server side, thereby achieving effective communication and emotion recognition.

[1826] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1827] Step 1:

[1828] The device collects sensor data.

[1829] Input: Data from temperature sensor, camera, and microphone.

[1830] Specific operation: The temperature sensor measures the room temperature, the camera captures the user's facial expressions, and the microphone captures voice data.

[1831] Output: Sensor data and raw image and audio data.

[1832] Step 2:

[1833] The device collects emotional data.

[1834] Input: Raw image and audio data.

[1835] Specific operation: The emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[1836] Output: Emotion data (e.g., the user's emotional state, such as "happy" or "sad").

[1837] Step 3:

[1838] The device stores sensor data and emotion data in a buffer.

[1839] Input: Sensor data and emotion data.

[1840] Specific operation: The acquired sensor data and emotion data are temporarily stored in a buffer in memory.

[1841] Output: The data stored in the buffer.

[1842] Step 4:

[1843] The terminal receives the dictionary data from the server and decrypts it.

[1844] Input: Encrypted dictionary data sent from the server.

[1845] Specific operation: The device receives the encrypted dictionary data and decrypts it using the decryption key.

[1846] Output: Decoded dictionary data.

[1847] Step 5:

[1848] The device compresses sensor data and emotion data.

[1849] Input: Buffered data and decoded dictionary data.

[1850] Specific operation: Sensor data and emotion data are compressed using dictionary data.

[1851] Output: Compressed data.

[1852] Step 6:

[1853] The terminal encrypts the compressed data and sends it to the server.

[1854] Input: Compressed data.

[1855] Specific operation: The compressed data is encrypted using the RSA encryption method and sent to the server.

[1856] Output: The encrypted data sent to the server.

[1857] Step 7:

[1858] The server receives the data and stores it temporarily.

[1859] Input: Encrypted data sent from the terminal.

[1860] Specific operation: The server receives the data and temporarily stores it in storage.

[1861] Output: Encrypted data in storage.

[1862] Step 8:

[1863] The server decrypts and recovers the encrypted data.

[1864] Input: Encrypted data stored in storage.

[1865] Specific operation: The data is decrypted using the decryption key to restore the original sensor data and emotion data.

[1866] Output: The recovered data.

[1867] Step 9:

[1868] The server passes the restored data to a generative artificial intelligence for analysis.

[1869] Input: Recovered data.

[1870] How it works: Data is passed to a generative AI to extract cyclical patterns and emotional fluctuations.

[1871] Output: Analysis results and feature data.

[1872] Step 10:

[1873] The server generates new dictionary data and stores it in the database.

[1874] Input: Analysis results and feature data.

[1875] Specific operation: The server generates new dictionary data based on this data to be used for the next data compression and stores it in the database.

[1876] Output: The new dictionary data stored in the database.

[1877] Step 11:

[1878] The server encrypts the new dictionary data and sends it to the device during the next communication.

[1879] Input: New dictionary data.

[1880] Specific operation: The new dictionary data is encrypted and sent to the device the next time communication occurs.

[1881] Output: Encrypted dictionary data.

[1882] 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.

[1883] 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.

[1884] 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.

[1885] 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.

[1886] 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.

[1887] 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.

[1888] 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).

[1889] 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.

[1890] 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."

[1891] 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.

[1892] 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).

[1893] 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.

[1894] 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.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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.

[1902] 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.

[1903] The following is further disclosed regarding the above embodiment.

[1904] (Claim 1)

[1905] A data acquisition means for acquiring data from a low-spec terminal;

[1906] data transmission means for transmitting data to the server;

[1907] a data analysis means for analyzing the data received by the server using generative artificial intelligence to generate dictionary data to be used for compression;

[1908] a dictionary data management means for storing the generated dictionary data in a database and transmitting the dictionary data from the server to the terminal for use the next time data is transmitted;

[1909] cryptographic means for securely encrypting and decrypting data and dictionary data for communication;

[1910] A system including:

[1911] (Claim 2)

[1912] 2. The system according to claim 1, wherein the dictionary data generated by analyzing the received data improves data compression efficiency.

[1913] (Claim 3)

[1914] 2. The system according to claim 1, wherein data is transmitted efficiently while reducing communication capacity by adaptively repeating the sending and receiving of dictionary data and data transmission.

[1915] "Example 1"

[1916] (Claim 1)

[1917] A data acquisition means for acquiring data from a low-spec terminal;

[1918] data transmission means for transmitting data to the server;

[1919] a data analysis means for analyzing the data received by the server using generative artificial intelligence to generate dictionary data to be used for compression;

[1920] a dictionary data management means for storing the generated dictionary data in a database and transmitting the dictionary data from the server to the terminal for use the next time data is transmitted;

[1921] a data compression means for compressing the acquired data received by the terminal;

[1922] a data compression means for compressing data acquired by the terminal using the decoded dictionary data;

[1923] a data encryption means for encrypting the compressed data and transmitting the encrypted data to a server;

[1924] a data recovery means for the server to decrypt the received data and generate the original data;

[1925] cryptographic means for securely encrypting and decrypting data and dictionary data for communication;

[1926] A system including:

[1927] (Claim 2)

[1928] 2. The system accor...

Claims

1. A data acquisition means for acquiring data from a low-spec terminal; data transmission means for transmitting data to the server; a data analysis means for analyzing the data received by the server using generative artificial intelligence to generate dictionary data to be used for compression; a dictionary data management means for storing the generated dictionary data in a database and transmitting the dictionary data from the server to the terminal for use the next time data is transmitted; cryptographic means for securely encrypting and decrypting data and dictionary data for communication; A system including:

2. 2. The system according to claim 1, wherein the data compression efficiency is improved by using dictionary data generated by analyzing the received data.

3. 2. The system according to claim 1, wherein data is transmitted efficiently while suppressing communication capacity by adaptively repeating transmission and reception of dictionary data and data transmission.

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