System

Abstract

An object of the system is to achieve both efficient data transfer and security.SOLUTION: A system according to an embodiment includes a prompt reception unit and a compression system generation unit. The prompt reception unit receives a prompt including an instruction from a user as input information. The compression scheme generation unit generates an optimum data compression scheme based on the prompt received by the prompt reception unit. In addition, the compression scheme generation unit generates a compression scheme incorporating data encryption and authentication protocols, incorporates a multi-layer security protocol for simultaneously ensuring confidentiality, integrity, and availability of data, an optimal data routing algorithm for minimizing a load on a communication infrastructure, and an optimal energy management algorithm for minimizing an amount of power required for data transfer, analyzes an emotion felt by the user with respect to the compression scheme, and generates the compression scheme that most satisfies the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to achieve both efficient data transfer and security, and the increase in infrastructure costs and required power consumption has become an issue.

[0005] The system according to the embodiment aims to achieve both efficient data transfer and security. [Means for solving the problem]

[0006] The system according to the embodiment includes a prompt accepting unit and a compression method generating unit. The prompt accepting unit receives a prompt including an instruction from a user as input information. The compression method generating unit generates an optimal data compression method based on the prompt received by the prompt accepting unit. [Effects of the Invention]

[0007] The system according to the embodiment can achieve both efficient data transfer and security. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) In a data compression system according to an embodiment of the present invention, a generation AI receives prompts containing user instructions as input information and generates an optimal data compression method based on the prompts. This data compression system solves the problem of data transfer pressure, reduces infrastructure costs and power requirements, and enables high-quality video transfer.

[0029] A data compression system according to an embodiment includes a generation AI, a prompt receiving unit, and a compression scheme generating unit. The generation AI receives, as input information, a prompt including instructions from a user. For example, the generation AI can receive a text prompt. The generation AI can also receive an audio prompt. The generation AI can also receive an image prompt. The prompt receiving unit processes the prompt received by the generation AI. For example, the prompt receiving unit analyzes a text prompt and passes it to the generation AI. The prompt receiving unit converts an audio prompt into text using speech recognition technology and passes it to the generation AI. The prompt receiving unit analyzes an image prompt using image recognition technology and passes it to the generation AI. The compression scheme generating unit generates an optimal data compression scheme based on the prompt received by the prompt receiving unit. For example, the compression scheme generating unit generates a compression algorithm that reduces data volume while maintaining video quality. The compression scheme generating unit can also generate a compression scheme that incorporates data encryption and authentication protocols. The compression scheme generating unit can also generate a compression scheme that reduces the load on communication infrastructure by reducing data volume. This allows the data compression system according to the embodiment to generate an optimal data compression method based on a user instruction.

[0030] The compression method generation unit can generate a compression algorithm that reduces the amount of data while maintaining the quality of the video. For example, the compression method generation unit generates an H.264 compression algorithm to reduce the amount of data while maintaining the quality of the video. The compression method generation unit can also generate an H.265 compression algorithm. The compression method generation unit can also generate a VP9 compression algorithm. This allows the amount of data to be reduced while maintaining the quality of the video.

[0031] The compression method generation unit can generate a compression method that incorporates data encryption and authentication protocols. For example, the compression method generation unit generates a compression method that incorporates AES encryption technology for data encryption. The compression method generation unit can also generate a compression method that incorporates RSA encryption technology. The compression method generation unit can also generate a compression method that incorporates the TLS protocol. This makes it possible to generate a compression method that incorporates data encryption and authentication protocols.

[0032] The compression method generation unit can generate a compression method that reduces the load on the communication infrastructure by reducing the amount of data. For example, the compression method generation unit generates an LZW compression algorithm to reduce the amount of data. The compression method generation unit can also generate a JPEG compression algorithm. The compression method generation unit can also generate an MP3 compression algorithm. This makes it possible to generate a compression method that reduces the load on the communication infrastructure.

[0033] The compression method generation unit can generate a compression method that reduces the amount of data, thereby reducing the amount of power required for data transfer. For example, the compression method generation unit generates an LZW compression algorithm to reduce the amount of data. The compression method generation unit can also generate a JPEG compression algorithm. The compression method generation unit can also generate an MP3 compression algorithm. This makes it possible to generate a compression method that reduces the amount of power required for data transfer.

[0034] The compression method generation unit can generate a compression method that achieves high-speed, high-quality video transfer by reducing the amount of data while maintaining video quality. For example, the compression method generation unit generates an H.264 compression algorithm to reduce the amount of data while maintaining video quality. The compression method generation unit can also generate an H.265 compression algorithm. The compression method generation unit can also generate a VP9 compression algorithm. This makes it possible to generate a compression method that achieves high-speed, high-quality video transfer.

[0035] The compression method generation unit can simultaneously generate the optimal compression method for different data formats. For example, the compression method generation unit simultaneously generates the optimal compression method for each format of text data, image data, and audio data. For example, it applies LZW compression to text data, JPEG compression to image data, and MP3 compression to audio data. In addition, in order to simultaneously generate the optimal compression method for different data formats, the compression method generation unit can also have the generation AI learn the compression algorithm for each data format. This makes it possible to simultaneously generate the optimal compression method for different data formats.

[0036] The compression method generation unit can learn performance data of past compression methods and predict the optimal compression method. The compression method generation unit, for example, learns performance data of past compression methods and predicts the optimal compression method. For example, it selects the optimal compression algorithm based on past compression efficiency and data loss rate. The compression method generation unit can also develop an algorithm for the generation AI to learn performance data of past compression methods and predict the optimal compression method. This allows it to learn performance data of past compression methods and predict the optimal compression method.

[0037] The compression method generation unit can adaptively change the compression method to be generated in real time and select the optimal compression method depending on the type of data and the transfer situation. For example, the compression method generation unit can adaptively change the compression method to be generated in real time and select the optimal compression method depending on the type of data and the transfer situation. For example, it can increase the compression rate when the network bandwidth is narrow. The compression method generation unit can also develop an algorithm to adaptively change the compression method generated by the generation AI in real time and select the optimal compression method depending on the type of data and the transfer situation. This makes it possible to select the optimal compression method in real time depending on the type of data and the transfer situation.

[0038] The compression method generation unit can adapt the generated compression method to different network environments. For example, the compression method generation unit adapts the generated compression method to different network environments. For example, in a 5G environment, the compression method generation unit generates a compression method that transfers high-quality video with low latency. The compression method generation unit can also generate a compression method that maintains a stable connection in a Wi-Fi environment. The compression method generation unit can also generate a compression method that minimizes data latency in a satellite communication environment. This makes it possible to generate compression methods that are adapted to different network environments.

[0039] The compression method generator can incorporate a multi-layered security protocol to simultaneously ensure data confidentiality, integrity, and availability. For example, the compression method generator generates a compression method incorporating AES encryption technology to ensure data confidentiality. The compression method generator can also generate a compression method incorporating digital signature technology to ensure data integrity. The compression method generator can also generate a compression method incorporating redundancy technology to ensure data availability. This makes it possible to incorporate a multi-layered security protocol to simultaneously ensure data confidentiality, integrity, and availability.

[0040] The compression method generation unit can learn from past security incident data and predict optimal security measures. The compression method generation unit, for example, learns from past security incident data and predicts optimal security measures. For example, it predicts future attacks based on past attack patterns and generates countermeasures for those attacks. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past security incident data and predict optimal security measures. This makes it possible to learn from past security incident data and predict optimal security measures.

[0041] The compression method generation unit can adapt the security measures it generates to different devices. For example, the compression method generation unit applies a lightweight encryption protocol to smartphones and an advanced firewall to PCs. The compression method generation unit can also apply low-power security measures to IoT devices. This makes it possible to generate security measures that are adapted to different devices.

[0042] The compression method generation unit can adapt the generated security measures to different industries. For example, the compression method generation unit adapts the generated security measures to different industries. For example, advanced encryption technology can be applied to the financial industry, and measures that emphasize data integrity can be applied to the medical industry. Furthermore, measures for content protection can be applied to the entertainment industry. This makes it possible to generate security measures that are adapted to different industries.

[0043] The compression method generation unit can incorporate an optimal data routing algorithm for minimizing the load on the communication infrastructure. The compression method generation unit generates a compression method that incorporates, for example, a shortest path algorithm for minimizing the load on the communication infrastructure. The compression method generation unit can also generate a compression method that incorporates a load balancing algorithm. The compression method generation unit can also generate a compression method that incorporates an algorithm that optimizes the data transfer path. This makes it possible to incorporate an optimal data routing algorithm for minimizing the load on the communication infrastructure.

[0044] The compression method generation unit can learn from past infrastructure cost data and predict optimal cost reduction measures. The compression method generation unit, for example, learns from past infrastructure cost data and predicts optimal cost reduction measures. For example, it proposes efficient operation methods for communication infrastructure based on past data. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past infrastructure cost data and predict optimal cost reduction measures. This makes it possible to learn from past infrastructure cost data and predict optimal cost reduction measures.

[0045] The compression method generation unit can adapt the generated cost reduction measures to different regions. For example, the compression method generation unit adapts the generated cost reduction measures to different regions. For example, the compression method generation unit can improve the efficiency of high-density data transfer in urban areas and propose low-cost communication infrastructure in rural areas. The compression method generation unit can also propose cost reduction measures that improve the efficiency of communication infrastructure development in remote islands. This makes it possible to generate cost reduction measures that are adapted to different regions.

[0046] The compression method generator can adapt the generated cost reduction measures to different communication protocols. For example, the compression method generator can optimize data retransmission control in the TCP / IP protocol and minimize packet loss in the UDP protocol. The compression method generator can also propose a cost reduction measure that optimizes data multiplexing in the HTTP / 2 protocol. This makes it possible to generate cost reduction measures that are adapted to different communication protocols.

[0047] The compression scheme generation unit can incorporate an optimal energy management algorithm to minimize the amount of power required for data transfer. For example, the compression scheme generation unit generates a compression scheme that incorporates a load balancing algorithm to minimize the amount of power required for data transfer. The compression scheme generation unit can also generate a compression scheme that incorporates an energy efficiency algorithm. The compression scheme generation unit can also generate a compression scheme that incorporates an algorithm that optimizes the frequency of data transfer. This makes it possible to incorporate an optimal energy management algorithm to minimize the amount of power required for data transfer.

[0048] The compression method generation unit can learn from past power data and predict optimal energy efficiency measures. The compression method generation unit, for example, learns from past power data and predicts optimal energy efficiency measures. For example, it proposes an efficient operation method for data transfer based on past data. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past power data and predict optimal energy efficiency measures. This makes it possible to learn from past power data and predict optimal energy efficiency measures.

[0049] The compression scheme generator can adapt the energy efficiency measures it generates to different devices. For example, the compression scheme generator can adapt the energy efficiency measures it generates to different devices. For example, it can apply a low-power consumption algorithm to a smartphone and a high-efficiency power management algorithm to a PC. The compression scheme generator can also apply a low-power consumption energy efficiency measure to an IoT device. This makes it possible to generate energy efficiency measures that are adapted to different devices.

[0050] The compression scheme generator can adapt the energy efficiency measures it generates to different energy sources. For example, the compression scheme generator adapts the energy efficiency measures it generates to different energy sources. For example, when renewable energy is used, it generates efficiency measures that respond to fluctuations in the energy supply. Furthermore, when a battery is used, the compression scheme generator can also generate efficiency measures that extend the battery life. Furthermore, when a power grid is used, the compression scheme generator can also generate efficiency measures that optimize power consumption. In this way, it is possible to generate energy efficiency measures that are adapted to different energy sources.

[0051] The compression method generation unit can incorporate an optimal video encoding algorithm to maximize video quality. For example, the compression method generation unit uses H.265 encoding to compress high-quality video with a small amount of data to maximize video quality. The compression method generation unit can also use H.264 encoding to compress high-quality video with a small amount of data. The compression method generation unit can also use VP9 encoding to compress high-quality video with a small amount of data. This makes it possible to incorporate an optimal video encoding algorithm to maximize video quality.

[0052] The compression method generation unit can learn from past video transfer data and predict the optimal video transfer method. The compression method generation unit, for example, learns from past video transfer data and predicts the optimal video transfer method. For example, it can propose a method that optimizes the balance between video quality and transfer speed based on past data. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past video transfer data and predict the optimal video transfer method. This makes it possible to learn from past video transfer data and predict the optimal video transfer method.

[0053] The compression method generation unit can adapt the generated video transmission method to different video formats. For example, the compression method generation unit applies a compression method that maintains high resolution to 4K video and a highly efficient compression method to 8K video. The compression method generation unit can also apply a compression method that maintains a high dynamic range to HDR video. This makes it possible to generate video transmission methods that are adapted to different video formats.

[0054] The compression method generation unit can adapt the generated video transmission method to different network environments. For example, the compression method generation unit adapts the generated video transmission method to different network environments. For example, in a 5G environment, it applies a method that transfers high-quality video with low latency, and in a Wi-Fi environment, it applies a method that maintains a stable connection. The compression method generation unit can also apply a method that minimizes data latency in a satellite communication environment. This makes it possible to generate video transmission methods that are adapted to different network environments.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The compression method generator can adapt the generated compression method to different data formats. For example, it applies LZW compression to text data, JPEG compression to image data, and MP3 compression to audio data. In addition, the compression method generator can also have the generation AI learn the compression algorithms for each data format in order to simultaneously generate the optimal compression method for different data formats. This makes it possible to simultaneously generate the optimal compression method for different data formats.

[0057] The compression method generation unit can learn from past performance data of compression methods and predict the optimal compression method. For example, it selects the optimal compression algorithm based on past compression efficiency and data loss rate. The compression method generation unit can also develop an algorithm for the generation AI to learn from past performance data of compression methods and predict the optimal compression method. This allows it to learn from past performance data of compression methods and predict the optimal compression method.

[0058] The compression method generation unit can adaptively change the compression method to be generated in real time, selecting the optimal compression method depending on the type of data and transfer conditions. For example, it can increase the compression rate when the network bandwidth is narrow. The compression method generation unit can also develop algorithms to adaptively change the compression method generated by the generation AI in real time and select the optimal compression method depending on the type of data and transfer conditions. This makes it possible to select the optimal compression method in real time depending on the type of data and transfer conditions.

[0059] The compression method generator can adapt the generated compression method to different network environments. For example, in a 5G environment, it generates a compression method that transfers high-quality video with low latency. The compression method generator can also generate a compression method that maintains a stable connection in a Wi-Fi environment. The compression method generator can also generate a compression method that minimizes data latency in a satellite communication environment. This makes it possible to generate compression methods that are adaptable to different network environments.

[0060] The compression method generator can incorporate a multi-layered security protocol to simultaneously ensure the confidentiality, integrity, and availability of data. For example, the compression method generator can generate a compression method that incorporates AES encryption technology to ensure the confidentiality of data. The compression method generator can also generate a compression method that incorporates digital signature technology to ensure the integrity of data. The compression method generator can also generate a compression method that incorporates redundancy technology to ensure the availability of data. This makes it possible to incorporate a multi-layered security protocol to simultaneously ensure the confidentiality, integrity, and availability of data.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The prompt receiving unit receives a prompt containing instructions from the user as input information using a generation AI. For example, the prompt receiving unit can receive prompts in text format, audio format, or image format. Audio format prompts are converted into text using speech recognition technology, and image format prompts are analyzed using image recognition technology. Step 2: The compression method generator generates an optimal data compression method based on the prompt received by the prompt receiver. For example, it can generate a compression algorithm that reduces the amount of data while maintaining video quality, a compression method that incorporates data encryption and authentication protocols, or a compression method that reduces the load on the communication infrastructure.

[0063] (Example 2) In a data compression system according to an embodiment of the present invention, a generation AI receives prompts containing user instructions as input information and generates an optimal data compression method based on the prompts. This data compression system solves the problem of data transfer pressure, reduces infrastructure costs and power requirements, and enables high-quality video transfer.

[0064] A data compression system according to an embodiment includes a generation AI, a prompt receiving unit, and a compression scheme generating unit. The generation AI receives, as input information, a prompt including instructions from a user. For example, the generation AI can receive a text prompt. The generation AI can also receive an audio prompt. The generation AI can also receive an image prompt. The prompt receiving unit processes the prompt received by the generation AI. For example, the prompt receiving unit analyzes a text prompt and passes it to the generation AI. The prompt receiving unit converts an audio prompt into text using speech recognition technology and passes it to the generation AI. The prompt receiving unit analyzes an image prompt using image recognition technology and passes it to the generation AI. The compression scheme generating unit generates an optimal data compression scheme based on the prompt received by the prompt receiving unit. For example, the compression scheme generating unit generates a compression algorithm that reduces data volume while maintaining video quality. The compression scheme generating unit can also generate a compression scheme that incorporates data encryption and authentication protocols. The compression scheme generating unit can also generate a compression scheme that reduces the load on communication infrastructure by reducing data volume. This allows the data compression system according to the embodiment to generate an optimal data compression method based on a user instruction.

[0065] The compression method generation unit can generate a compression algorithm that reduces the amount of data while maintaining the quality of the video. For example, the compression method generation unit generates an H.264 compression algorithm to reduce the amount of data while maintaining the quality of the video. The compression method generation unit can also generate an H.265 compression algorithm. The compression method generation unit can also generate a VP9 compression algorithm. This allows the amount of data to be reduced while maintaining the quality of the video.

[0066] The compression method generation unit can generate a compression method that incorporates data encryption and authentication protocols. For example, the compression method generation unit generates a compression method that incorporates AES encryption technology for data encryption. The compression method generation unit can also generate a compression method that incorporates RSA encryption technology. The compression method generation unit can also generate a compression method that incorporates the TLS protocol. This makes it possible to generate a compression method that incorporates data encryption and authentication protocols.

[0067] The compression method generation unit can generate a compression method that reduces the load on the communication infrastructure by reducing the amount of data. For example, the compression method generation unit generates an LZW compression algorithm to reduce the amount of data. The compression method generation unit can also generate a JPEG compression algorithm. The compression method generation unit can also generate an MP3 compression algorithm. This makes it possible to generate a compression method that reduces the load on the communication infrastructure.

[0068] The compression method generation unit can generate a compression method that reduces the amount of data, thereby reducing the amount of power required for data transfer. For example, the compression method generation unit generates an LZW compression algorithm to reduce the amount of data. The compression method generation unit can also generate a JPEG compression algorithm. The compression method generation unit can also generate an MP3 compression algorithm. This makes it possible to generate a compression method that reduces the amount of power required for data transfer.

[0069] The compression method generation unit can generate a compression method that achieves high-speed, high-quality video transfer by reducing the amount of data while maintaining video quality. For example, the compression method generation unit generates an H.264 compression algorithm to reduce the amount of data while maintaining video quality. The compression method generation unit can also generate an H.265 compression algorithm. The compression method generation unit can also generate a VP9 compression algorithm. This makes it possible to generate a compression method that achieves high-speed, high-quality video transfer.

[0070] The compression method generation unit can simultaneously generate the optimal compression method for different data formats. For example, the compression method generation unit simultaneously generates the optimal compression method for each format of text data, image data, and audio data. For example, it applies LZW compression to text data, JPEG compression to image data, and MP3 compression to audio data. In addition, in order to simultaneously generate the optimal compression method for different data formats, the compression method generation unit can also have the generation AI learn the compression algorithm for each data format. This makes it possible to simultaneously generate the optimal compression method for different data formats.

[0071] The compression method generation unit can learn performance data of past compression methods and predict the optimal compression method. The compression method generation unit, for example, learns performance data of past compression methods and predicts the optimal compression method. For example, it selects the optimal compression algorithm based on past compression efficiency and data loss rate. The compression method generation unit can also develop an algorithm for the generation AI to learn performance data of past compression methods and predict the optimal compression method. This allows it to learn performance data of past compression methods and predict the optimal compression method.

[0072] The compression method generation unit can use the emotion estimation function to analyze the emotion a user has toward a compression method and generate a compression method that satisfies the user most. The compression method generation unit can, for example, use the emotion estimation function to analyze the emotion a user has toward a compression method and generate a compression method that satisfies the user most. For example, if a user desires high-quality video, a compression method that reflects that emotion is generated. The compression method generation unit can also use the emotion estimation function to analyze the emotion a user has toward a compression method and develop an algorithm for generating a compression method that satisfies the user most. This makes it possible to generate a compression method that satisfies the user most.

[0073] The compression method generation unit can adaptively change the compression method to be generated in real time and select the optimal compression method depending on the type of data and the transfer situation. For example, the compression method generation unit can adaptively change the compression method to be generated in real time and select the optimal compression method depending on the type of data and the transfer situation. For example, it can increase the compression rate when the network bandwidth is narrow. The compression method generation unit can also develop an algorithm to adaptively change the compression method generated by the generation AI in real time and select the optimal compression method depending on the type of data and the transfer situation. This makes it possible to select the optimal compression method in real time depending on the type of data and the transfer situation.

[0074] The compression method generation unit can adapt the generated compression method to different network environments. For example, the compression method generation unit adapts the generated compression method to different network environments. For example, in a 5G environment, the compression method generation unit generates a compression method that transfers high-quality video with low latency. The compression method generation unit can also generate a compression method that maintains a stable connection in a Wi-Fi environment. The compression method generation unit can also generate a compression method that minimizes data latency in a satellite communication environment. This makes it possible to generate compression methods that are adapted to different network environments.

[0075] The compression method generation unit can use the emotion estimation function to monitor the user's emotion when selecting a compression method in real time and propose the optimal compression method. The compression method generation unit can, for example, use the emotion estimation function to monitor the user's emotion when selecting a compression method in real time and propose the optimal compression method. For example, it can select a compression method that does not cause the user stress. The compression method generation unit can also use the emotion estimation function to monitor the user's emotion when selecting a compression method in real time and develop an algorithm for proposing the optimal compression method. This makes it possible to monitor the user's emotion in real time and propose the optimal compression method.

[0076] The compression method generator can incorporate a multi-layered security protocol to simultaneously ensure data confidentiality, integrity, and availability. For example, the compression method generator generates a compression method incorporating AES encryption technology to ensure data confidentiality. The compression method generator can also generate a compression method incorporating digital signature technology to ensure data integrity. The compression method generator can also generate a compression method incorporating redundancy technology to ensure data availability. This makes it possible to incorporate a multi-layered security protocol to simultaneously ensure data confidentiality, integrity, and availability.

[0077] The compression method generation unit can learn from past security incident data and predict optimal security measures. The compression method generation unit, for example, learns from past security incident data and predicts optimal security measures. For example, it predicts future attacks based on past attack patterns and generates countermeasures for those attacks. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past security incident data and predict optimal security measures. This makes it possible to learn from past security incident data and predict optimal security measures.

[0078] The compression method generation unit can use the emotion estimation function to analyze the user's anxieties and concerns about security and strengthen security measures based on the analysis. The compression method generation unit, for example, can use the emotion estimation function to analyze the user's anxieties and concerns about security and strengthen security measures based on the analysis. For example, the compression method generation unit can identify points that make the user feel uneasy and strengthen those points. The compression method generation unit can also use the emotion estimation function to analyze the user's anxieties and concerns about security and develop an algorithm for strengthening security measures based on the analysis. This makes it possible to analyze the user's anxieties and concerns and strengthen security measures based on the analysis.

[0079] The compression method generation unit can adapt the security measures it generates to different devices. For example, the compression method generation unit applies a lightweight encryption protocol to smartphones and an advanced firewall to PCs. The compression method generation unit can also apply low-power security measures to IoT devices. This makes it possible to generate security measures that are adapted to different devices.

[0080] The compression method generation unit can adapt the generated security measures to different industries. For example, the compression method generation unit adapts the generated security measures to different industries. For example, advanced encryption technology can be applied to the financial industry, and measures that emphasize data integrity can be applied to the medical industry. Furthermore, measures for content protection can be applied to the entertainment industry. This makes it possible to generate security measures that are adapted to different industries.

[0081] The compression method generation unit can use the emotion estimation function to monitor in real time the emotions of a user when selecting security measures and propose optimal security measures. The compression method generation unit, for example, uses the emotion estimation function to monitor in real time the emotions of a user when selecting security measures and propose optimal security measures. For example, it can identify points that make the user feel uneasy and propose measures to strengthen those areas. The compression method generation unit can also use the emotion estimation function to monitor in real time the emotions of a user when selecting security measures and develop an algorithm for proposing optimal security measures. This makes it possible to monitor the user's emotions in real time and propose optimal security measures.

[0082] The compression method generation unit can incorporate an optimal data routing algorithm for minimizing the load on the communication infrastructure. The compression method generation unit generates a compression method that incorporates, for example, a shortest path algorithm for minimizing the load on the communication infrastructure. The compression method generation unit can also generate a compression method that incorporates a load balancing algorithm. The compression method generation unit can also generate a compression method that incorporates an algorithm that optimizes the data transfer path. This makes it possible to incorporate an optimal data routing algorithm for minimizing the load on the communication infrastructure.

[0083] The compression method generation unit can learn from past infrastructure cost data and predict optimal cost reduction measures. The compression method generation unit, for example, learns from past infrastructure cost data and predicts optimal cost reduction measures. For example, it proposes efficient operation methods for communication infrastructure based on past data. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past infrastructure cost data and predict optimal cost reduction measures. This makes it possible to learn from past infrastructure cost data and predict optimal cost reduction measures.

[0084] The compression method generation unit can use the emotion estimation function to analyze the emotions a user has regarding infrastructure costs and propose cost reduction measures that will satisfy the user most. The compression method generation unit can, for example, use the emotion estimation function to analyze the emotions a user has regarding infrastructure costs and propose cost reduction measures that will satisfy the user most. For example, it can identify points that cause the user anxiety regarding cost reduction and strengthen those areas. The compression method generation unit can also use the emotion estimation function to analyze the emotions a user has regarding infrastructure costs and develop an algorithm for proposing cost reduction measures that will satisfy the user most. This makes it possible to analyze the user's emotions and propose optimal cost reduction measures.

[0085] The compression method generation unit can adapt the generated cost reduction measures to different regions. For example, the compression method generation unit adapts the generated cost reduction measures to different regions. For example, the compression method generation unit can improve the efficiency of high-density data transfer in urban areas and propose low-cost communication infrastructure in rural areas. The compression method generation unit can also propose cost reduction measures that improve the efficiency of communication infrastructure development in remote islands. This makes it possible to generate cost reduction measures that are adapted to different regions.

[0086] The compression method generator can adapt the generated cost reduction measures to different communication protocols. For example, the compression method generator can optimize data retransmission control in the TCP / IP protocol and minimize packet loss in the UDP protocol. The compression method generator can also propose a cost reduction measure that optimizes data multiplexing in the HTTP / 2 protocol. This makes it possible to generate cost reduction measures that are adapted to different communication protocols.

[0087] The compression method generation unit can use the emotion estimation function to monitor the user's emotions in real time when selecting a cost-cutting measure and propose optimal cost-cutting measures. The compression method generation unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when selecting a cost-cutting measure and propose optimal cost-cutting measures. For example, it identifies points that make the user feel anxious and proposes cost-cutting measures that strengthen those points. The compression method generation unit can also use the emotion estimation function to monitor the user's emotions in real time when selecting a cost-cutting measure and develop an algorithm for proposing optimal cost-cutting measures. This makes it possible to monitor the user's emotions in real time and propose optimal cost-cutting measures.

[0088] The compression scheme generation unit can incorporate an optimal energy management algorithm to minimize the amount of power required for data transfer. For example, the compression scheme generation unit generates a compression scheme that incorporates a load balancing algorithm to minimize the amount of power required for data transfer. The compression scheme generation unit can also generate a compression scheme that incorporates an energy efficiency algorithm. The compression scheme generation unit can also generate a compression scheme that incorporates an algorithm that optimizes the frequency of data transfer. This makes it possible to incorporate an optimal energy management algorithm to minimize the amount of power required for data transfer.

[0089] The compression method generation unit can learn from past power data and predict optimal energy efficiency measures. The compression method generation unit, for example, learns from past power data and predicts optimal energy efficiency measures. For example, it proposes an efficient operation method for data transfer based on past data. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past power data and predict optimal energy efficiency measures. This makes it possible to learn from past power data and predict optimal energy efficiency measures.

[0090] The compression method generation unit can use the emotion estimation function to analyze the user's emotions regarding power consumption and propose energy efficiency measures that will satisfy the user most. The compression method generation unit, for example, can use the emotion estimation function to analyze the user's emotions regarding power consumption and propose energy efficiency measures that will satisfy the user most. For example, the compression method generation unit can identify points that cause the user anxiety regarding power consumption and strengthen those areas. The compression method generation unit can also use the emotion estimation function to analyze the user's emotions regarding power consumption and develop an algorithm for proposing energy efficiency measures that will satisfy the user most. This makes it possible to analyze the user's emotions and propose optimal energy efficiency measures.

[0091] The compression scheme generator can adapt the energy efficiency measures it generates to different devices. For example, the compression scheme generator can adapt the energy efficiency measures it generates to different devices. For example, it can apply a low-power consumption algorithm to a smartphone and a high-efficiency power management algorithm to a PC. The compression scheme generator can also apply a low-power consumption energy efficiency measure to an IoT device. This makes it possible to generate energy efficiency measures that are adapted to different devices.

[0092] The compression scheme generator can adapt the energy efficiency measures it generates to different energy sources. For example, the compression scheme generator adapts the energy efficiency measures it generates to different energy sources. For example, when renewable energy is used, it generates efficiency measures that respond to fluctuations in the energy supply. Furthermore, when a battery is used, the compression scheme generator can also generate efficiency measures that extend the battery life. Furthermore, when a power grid is used, the compression scheme generator can also generate efficiency measures that optimize power consumption. In this way, it is possible to generate energy efficiency measures that are adapted to different energy sources.

[0093] The compression method generation unit can use the emotion estimation function to monitor the user's emotions in real time when selecting energy efficiency measures and propose optimal energy efficiency measures. The compression method generation unit can, for example, use the emotion estimation function to monitor the user's emotions in real time when selecting energy efficiency measures and propose optimal energy efficiency measures. For example, the compression method generation unit can identify points that the user feels uneasy about and propose efficiency measures that strengthen those points. The compression method generation unit can also use the emotion estimation function to monitor the user's emotions in real time when selecting energy efficiency measures and develop an algorithm for proposing optimal energy efficiency measures. This makes it possible to monitor the user's emotions in real time and propose optimal energy efficiency measures.

[0094] The compression method generation unit can incorporate an optimal video encoding algorithm to maximize video quality. For example, the compression method generation unit uses H.265 encoding to compress high-quality video with a small amount of data to maximize video quality. The compression method generation unit can also use H.264 encoding to compress high-quality video with a small amount of data. The compression method generation unit can also use VP9 encoding to compress high-quality video with a small amount of data. This makes it possible to incorporate an optimal video encoding algorithm to maximize video quality.

[0095] The compression method generation unit can learn from past video transfer data and predict the optimal video transfer method. The compression method generation unit, for example, learns from past video transfer data and predicts the optimal video transfer method. For example, it can propose a method that optimizes the balance between video quality and transfer speed based on past data. The compression method generation unit can also develop an algorithm that enables the generation AI to learn from past video transfer data and predict the optimal video transfer method. This makes it possible to learn from past video transfer data and predict the optimal video transfer method.

[0096] The compression method generation unit can use the emotion estimation function to analyze the emotions a user has regarding video quality and propose a video transfer method that will satisfy the user most. The compression method generation unit can, for example, use the emotion estimation function to analyze the emotions a user has regarding video quality and propose a video transfer method that will satisfy the user most. For example, if a user desires high-quality video, the compression method generation unit can propose a transfer method that reflects that emotion. The compression method generation unit can also use the emotion estimation function to analyze the emotions a user has regarding video quality and develop an algorithm for proposing a video transfer method that will satisfy the user most. This makes it possible to analyze the user's emotions and propose the optimal video transfer method.

[0097] The compression method generation unit can adapt the generated video transmission method to different video formats. For example, the compression method generation unit applies a compression method that maintains high resolution to 4K video and a highly efficient compression method to 8K video. The compression method generation unit can also apply a compression method that maintains a high dynamic range to HDR video. This makes it possible to generate video transmission methods that are adapted to different video formats.

[0098] The compression method generation unit can adapt the generated video transmission method to different network environments. For example, the compression method generation unit adapts the generated video transmission method to different network environments. For example, in a 5G environment, it applies a method that transfers high-quality video with low latency, and in a Wi-Fi environment, it applies a method that maintains a stable connection. The compression method generation unit can also apply a method that minimizes data latency in a satellite communication environment. This makes it possible to generate video transmission methods that are adapted to different network environments.

[0099] The compression method generation unit can use the emotion estimation function to monitor in real time the emotions of a user when selecting a video transfer method and propose an optimal video transfer method. The compression method generation unit can, for example, use the emotion estimation function to monitor in real time the emotions of a user when selecting a video transfer method and propose an optimal video transfer method. For example, if a user desires high-quality video, the compression method generation unit can propose a transfer method that reflects that emotion. The compression method generation unit can also use the emotion estimation function to monitor in real time the emotions of a user when selecting a video transfer method and develop an algorithm for proposing an optimal video transfer method. This makes it possible to monitor a user's emotions in real time and propose an optimal video transfer method.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The compression method generator can adapt the generated compression method to different data formats. For example, it applies LZW compression to text data, JPEG compression to image data, and MP3 compression to audio data. In addition, the compression method generator can also have the generation AI learn the compression algorithms for each data format in order to simultaneously generate the optimal compression method for different data formats. This makes it possible to simultaneously generate the optimal compression method for different data formats.

[0102] The compression method generation unit can learn from past performance data of compression methods and predict the optimal compression method. For example, it selects the optimal compression algorithm based on past compression efficiency and data loss rate. The compression method generation unit can also develop an algorithm for the generation AI to learn from past performance data of compression methods and predict the optimal compression method. This allows it to learn from past performance data of compression methods and predict the optimal compression method.

[0103] The compression method generation unit can adaptively change the compression method to be generated in real time, selecting the optimal compression method depending on the type of data and transfer conditions. For example, it can increase the compression rate when the network bandwidth is narrow. The compression method generation unit can also develop algorithms to adaptively change the compression method generated by the generation AI in real time and select the optimal compression method depending on the type of data and transfer conditions. This makes it possible to select the optimal compression method in real time depending on the type of data and transfer conditions.

[0104] The compression method generator can adapt the generated compression method to different network environments. For example, in a 5G environment, it generates a compression method that transfers high-quality video with low latency. The compression method generator can also generate a compression method that maintains a stable connection in a Wi-Fi environment. The compression method generator can also generate a compression method that minimizes data latency in a satellite communication environment. This makes it possible to generate compression methods that are adaptable to different network environments.

[0105] The compression method generator can incorporate a multi-layered security protocol to simultaneously ensure the confidentiality, integrity, and availability of data. For example, the compression method generator can generate a compression method that incorporates AES encryption technology to ensure the confidentiality of data. The compression method generator can also generate a compression method that incorporates digital signature technology to ensure the integrity of data. The compression method generator can also generate a compression method that incorporates redundancy technology to ensure the availability of data. This makes it possible to incorporate a multi-layered security protocol to simultaneously ensure the confidentiality, integrity, and availability of data.

[0106] The compression method generation unit can use the emotion estimation function to analyze the user's emotions toward the compression method and generate a compression method that satisfies the user most. For example, if the user desires high-quality video, the compression method generation unit generates a compression method that reflects the user's emotions. The compression method generation unit can also use the emotion estimation function to analyze the user's emotions toward the compression method and develop an algorithm for generating a compression method that satisfies the user most. This makes it possible to generate a compression method that satisfies the user most.

[0107] The compression method generation unit can use the emotion estimation function to analyze the user's anxieties and concerns about security and strengthen security measures based on the results. For example, it can identify points that make the user feel uneasy and strengthen those areas. The compression method generation unit can also use the emotion estimation function to analyze the user's anxieties and concerns about security and develop an algorithm for strengthening security measures based on the results. This makes it possible to analyze the user's anxieties and concerns and strengthen security measures based on the results.

[0108] The compression method generation unit can use the emotion estimation function to analyze the user's feelings about infrastructure costs and propose cost-cutting measures that will satisfy the user most. For example, it can identify points where the user feels uneasy about cost-cutting and strengthen those areas. The compression method generation unit can also use the emotion estimation function to analyze the user's feelings about infrastructure costs and develop an algorithm to propose cost-cutting measures that will satisfy the user most. This makes it possible to analyze the user's emotions and propose optimal cost-cutting measures.

[0109] The compression method generation unit can use the emotion estimation function to analyze the user's emotions regarding power consumption and propose energy efficiency measures that will satisfy the user most. For example, it can identify points where the user feels anxious about power consumption and strengthen those areas. The compression method generation unit can also use the emotion estimation function to analyze the user's emotions regarding power consumption and develop an algorithm to propose energy efficiency measures that will satisfy the user most. This makes it possible to analyze the user's emotions and propose optimal energy efficiency measures.

[0110] The compression method generation unit can use the emotion estimation function to analyze the user's emotions regarding video quality and propose a video transmission method that will satisfy the user most. For example, if a user desires high-quality video, the compression method generation unit can propose a transmission method that reflects that emotion. The compression method generation unit can also use the emotion estimation function to analyze the user's emotions regarding video quality and develop an algorithm for proposing a video transmission method that will satisfy the user most. This makes it possible to analyze the user's emotions and propose the optimal video transmission method.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The prompt receiving unit receives a prompt containing instructions from the user as input information using a generation AI. For example, the prompt receiving unit can receive prompts in text format, audio format, or image format. Audio format prompts are converted into text using speech recognition technology, and image format prompts are analyzed using image recognition technology. Step 2: The compression method generator generates an optimal data compression method based on the prompt received by the prompt receiver. For example, it can generate a compression algorithm that reduces the amount of data while maintaining video quality, a compression method that incorporates data encryption and authentication protocols, or a compression method that reduces the load on the communication infrastructure.

[0113] 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.

[0114] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0119] 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0149] 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0154] 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.

[0155] 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.

[0156] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] 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.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0164] 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.

[0165] 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).

[0166] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] 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."

[0168] 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.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0174] The hardware resource that executes the specific process 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 process may be a single processor.

[0175] 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.

[0176] 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.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] 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.

[0179] 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. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with generative AI, a prompt receiving unit that receives a prompt including an instruction from a user as input information; a compression scheme generator that generates an optimal data compression scheme based on the prompt received by the prompt receiver.

2. The compression method generation unit 10. The system of claim 1, wherein the compression method incorporates data encryption and authentication protocols.

3. The compression method generation unit 10. The system of claim 1, incorporating multi-layered security protocols to simultaneously ensure data confidentiality, integrity, and availability.

4. The compression method generation unit 10. The system of claim 1 incorporating an optimal data routing algorithm to minimize the load on the communications infrastructure.

5. The compression method generation unit 10. The system of claim 1 incorporating an optimal energy management algorithm for minimizing the amount of power required for data transfer.

6. The compression method generation unit The system according to claim 1 , wherein the system analyzes user's feelings toward a compression scheme and generates the compression scheme that the user is most satisfied with.

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