system

Abstract

The system according to the embodiment aims to achieve efficient data compression in data communication from low-spec IoT terminals. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a utilizing unit. The receiving unit receives data. The analyzing unit analyzes the data received by the receiving unit. The generating unit generates dictionary data for compression based on the data analyzed by the analyzing unit. The utilizing unit uses the dictionary data generated by the generating unit in the next communication.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently compress data when communicating with low-spec IoT devices.

[0005] The system according to the embodiment aims to achieve efficient data compression in data communication from low-spec IoT terminals. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a utilizing unit. The receiving unit receives data. The analyzing unit analyzes the data received by the receiving unit. The generating unit generates dictionary data for compression based on the data analyzed by the analyzing unit. The utilizing unit utilizes the dictionary data generated by the generating unit in the next communication. [Effects of the Invention]

[0007] The system according to the embodiment can achieve efficient data compression in data communication from low-spec IoT terminals. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A communication system according to an embodiment of the present invention is a system for efficiently communicating large amounts of data transmitted from IoT devices. This communication system aims to reduce the capacity (power, channel, human resources, etc.) of communication paths through which large amounts of data are transmitted from IoT devices, which are expected to become increasingly compact and reach 29 billion by 2030. While data compression using generative AI can achieve high compression rates, it has the drawback of being time-consuming and unsuitable for low-spec senders, such as IoT devices. Therefore, this invention assumes multiple communications from low-spec devices, and asynchronously analyzes each communication using generative AI on the receiver side. The analyzed data compression parameters (dictionary data) are used from the next communication, enabling continuously evolving communications. This mechanism captures the characteristics of the data and enables optimized, unique communications, potentially enabling developments in voice, image, and video data as well. For example, the receiver receives data transmitted from an IoT device, analyzes the data using generative AI, and generates compression parameters (dictionary data). This dictionary data is then used from the next communication. As a result, the data being communicated is gradually compressed more efficiently, reducing the capacity of the communication path. For example, in the case of voice data, the generation AI analyzes the characteristics of the voice data during the first communication and generates dictionary data for compression. This dictionary data can be used to efficiently compress the voice data during subsequent communications. Similarly, for image and video data, the generation AI captures the characteristics of the data and generates dictionary data for compression, which is then used during subsequent communications, achieving efficient communication. In this way, the present invention uses the generation AI to capture the characteristics of the data and achieve optimized, unique communication, thereby enabling efficient communication of large amounts of data transmitted from IoT devices. This allows the communication system to efficiently communicate large amounts of data transmitted from IoT devices.

[0029] A communication system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a utilizing unit. The receiving unit receives data transmitted from an IoT device. The receiving unit can receive data transmitted from, for example, a low-spec IoT device. The receiving unit can efficiently receive data regardless of the type or format of the data. For example, the receiving unit can receive data in various formats, such as text data, audio data, image data, and video data. The analyzing unit uses a generating AI to analyze the data received by the receiving unit. The analyzing unit, for example, uses the generating AI to analyze the characteristics of the data and generate dictionary data for compression. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, and can analyze the characteristics of the data in detail. For example, the analyzing unit uses the generating AI to analyze the characteristics of audio data and generate dictionary data for compression. The analyzing unit can also use the generating AI to analyze the characteristics of image data and generate dictionary data for compression. The analyzing unit can also use the generating AI to analyze the characteristics of video data and generate dictionary data for compression. The generating unit generates dictionary data for compression based on the data analyzed by the analyzing unit. The generation unit generates efficient dictionary data that captures the characteristics of the data, for example, using a generation AI. The generation AI can analyze the characteristics of the data in detail and generate dictionary data for optimal compression. For example, the generation unit generates dictionary data that captures the characteristics of audio data using a generation AI. The generation unit can also generate dictionary data that captures the characteristics of image data using a generation AI. The generation unit can also generate dictionary data that captures the characteristics of video data using a generation AI. The utilization unit uses the dictionary data generated by the generation unit in the next communication. For example, the utilization unit improves communication efficiency by using the generated dictionary data in the next communication. The utilization unit clarifies specific methods and criteria for using the generated dictionary data in the next communication. For example, the utilization unit uses the dictionary data based on the communication protocol and the timing of use. This enables the communication system according to the embodiment to efficiently receive and analyze data and generate and utilize dictionary data.

[0030] The generation unit can analyze data characteristics using a generation AI and generate dictionary data for compression. The generation unit can analyze data characteristics using a generation AI, for example, and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze data characteristics in detail. For example, the generation unit can analyze audio data characteristics using a generation AI and generate dictionary data for compression. The generation unit can also analyze image data characteristics using a generation AI and generate dictionary data for compression. Furthermore, the generation unit can analyze video data characteristics using a generation AI and generate dictionary data for compression. This enables efficient generation of dictionary data that captures data characteristics by using a generation AI. Some or all of the above-described processing in the generation unit can be performed using a generation AI, for example, or can be performed without using a generation AI. For example, the generation unit can analyze data characteristics using a generation AI and generate dictionary data for compression.

[0031] The utilization unit can use the generated dictionary data in the next communication. The utilization unit, for example, uses the generated dictionary data in the next communication. The utilization unit clarifies the specific method and criteria for using the generated dictionary data in the next communication. For example, the utilization unit uses the dictionary data based on the communication protocol and the timing of use. In this way, the generated dictionary data is used in the next communication, thereby improving communication efficiency. Some or all of the above-mentioned processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can use the generated dictionary data in the next communication.

[0032] The analysis unit can analyze the voice data and generate dictionary data for compression. The analysis unit can, for example, use a generation AI to analyze the characteristics of the voice data and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the characteristics of the voice data in detail. For example, the analysis unit can analyze the characteristics of the voice data using the generation AI and generate dictionary data for compression. This makes it possible to generate efficient dictionary data that captures the characteristics of the voice data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can analyze the characteristics of the voice data using the generation AI and generate dictionary data for compression.

[0033] The analysis unit can analyze the image data and generate dictionary data for compression. The analysis unit can, for example, use a generation AI to analyze the characteristics of the image data and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the characteristics of the image data in detail. For example, the analysis unit can analyze the characteristics of the image data using the generation AI and generate dictionary data for compression. This makes it possible to generate efficient dictionary data that captures the characteristics of the image data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can analyze the characteristics of the image data using the generation AI and generate dictionary data for compression.

[0034] The analysis unit can analyze the video data and generate dictionary data for compression. The analysis unit can, for example, use a generation AI to analyze the characteristics of the video data and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the characteristics of the video data in detail. For example, the analysis unit can analyze the characteristics of the video data using the generation AI and generate dictionary data for compression. This makes it possible to generate efficient dictionary data that captures the characteristics of the video data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can analyze the characteristics of the video data using the generation AI and generate dictionary data for compression.

[0035] The receiving unit can select different receiving modes depending on the type of data. For example, the receiving unit selects different receiving modes depending on the type of data. The receiving unit can select the optimal receiving mode based on the type and format of data. For example, the receiving unit selects a low-latency mode for audio data, emphasizing real-time performance. The receiving unit can select a high-compression mode for image data, reducing the amount of data. Furthermore, the receiving unit can select a balanced mode for video data, balancing image quality and data amount. This enables efficient communication by selecting the optimal receiving mode depending on the type of data. Some or all of the above-described processing in the receiving unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the receiving unit can determine the type of data using a generation AI and select the optimal receiving mode.

[0036] The receiving unit can evaluate the reliability of a data sender and prioritize receiving highly reliable data. For example, the receiving unit can evaluate the reliability of a data sender and prioritize receiving highly reliable data. The receiving unit can evaluate the reliability based on the sender's past communication history, authentication information, geographical location information, etc. For example, the receiving unit can evaluate the sender's past communication history and prioritize receiving data from highly reliable senders. The receiving unit can also check the sender's authentication information and prioritize receiving data from highly reliable senders. Furthermore, the receiving unit can take the sender's geographical location information into consideration and prioritize receiving data from highly reliable senders. This prioritizes receiving highly reliable data, thereby improving the reliability of communication. Some or all of the above-described processing in the receiving unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the receiving unit can evaluate the reliability of a sender using a generation AI and prioritize receiving highly reliable data.

[0037] The receiving unit can select data to receive taking into consideration the geographical location information of the data sender. The receiving unit, for example, selects data to receive taking into consideration the geographical location information of the data sender. The receiving unit can determine the priority of data reception based on the geographical location information of the sender. For example, the receiving unit prioritizes reception of data when the sender is nearby. Furthermore, the receiving unit can postpone reception of data when the sender is far away. Furthermore, when the sender is in a specific region, the receiving unit can prioritize receiving data from that region. This enables efficient data reception by selecting data based on the geographical location information of the sender. Some or all of the above-described processing in the receiving unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the receiving unit can evaluate the geographical location information of the sender using a generation AI and determine the priority of data reception.

[0038] The receiving unit can filter received data by referring to the past communication history of the data sender. The receiving unit, for example, filters received data by referring to the past communication history of the data sender. The receiving unit can evaluate the reliability of data based on the past communication history of the data sender and filter out low-reliability data. For example, the receiving unit filters out low-reliability data from the past communication history of the sender. The receiving unit can also filter out low-importance data from the past communication history of the sender. Furthermore, the receiving unit can filter data based on a specific pattern from the past communication history of the sender. In this way, low-reliability data can be eliminated by filtering data based on the past communication history. Some or all of the above-described processing in the receiving unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the receiving unit can evaluate the past communication history of the sender using a generation AI and filter out data.

[0039] The analysis unit can apply different analysis algorithms depending on the type of data. For example, the analysis unit applies different analysis algorithms depending on the type of data. The analysis unit can select the optimal analysis algorithm based on the type and format of the data. For example, the analysis unit can apply a voice recognition algorithm in the case of voice data. The analysis unit can apply an image recognition algorithm in the case of image data. Furthermore, the analysis unit can apply a video analysis algorithm in the case of video data. This enables efficient data analysis by applying the optimal analysis algorithm depending on the type of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the type of data using a generation AI and apply the optimal analysis algorithm.

[0040] The analysis unit can perform analysis taking into account attribute information of the data sender. The analysis unit, for example, performs analysis taking into account attribute information of the data sender. The analysis unit can select an appropriate analysis method based on the attribute information of the sender. For example, the analysis unit can perform a detailed analysis if the sender is highly reliable. Furthermore, the analysis unit can perform a simplified analysis if the sender is unreliable. Furthermore, the analysis unit can select an appropriate analysis method based on the attribute information of the sender. This enables efficient data analysis by selecting an appropriate analysis method based on the attribute information of the sender. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can evaluate the attribute information of the sender using a generation AI and select an appropriate analysis method.

[0041] The analysis unit can perform analysis taking into account geographical location information of the data sender. The analysis unit can, for example, perform analysis taking into account geographical location information of the data sender. The analysis unit can select an appropriate analysis method based on the geographical location information of the data sender. For example, the analysis unit can perform a detailed analysis if the sender is located close by. Furthermore, the analysis unit can perform a simplified analysis if the sender is located far away. Furthermore, the analysis unit can select an appropriate analysis method based on the geographical location information of the sender. This enables efficient data analysis by selecting an appropriate analysis method based on the geographical location information of the sender. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can evaluate the geographical location information of the sender using a generation AI and select an appropriate analysis method.

[0042] The analysis unit can improve the accuracy of the analysis by referring to literature related to the data. The analysis unit can, for example, improve the accuracy of the analysis by referring to literature related to the data. The analysis unit can apply the latest analysis method based on the related literature to improve the accuracy of the analysis results. For example, the analysis unit can refer to literature related to the data and apply the latest analysis method. The analysis unit can also refer to literature related to the data to improve the accuracy of the analysis results. Furthermore, the analysis unit can refer to literature related to the data and select an appropriate analysis method. In this way, by referring to the related literature, the latest analysis method can be applied and the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to refer to related literature to improve the accuracy of the analysis.

[0043] The generation unit can apply different dictionary data generation algorithms depending on the type of data. For example, the generation unit applies different dictionary data generation algorithms depending on the type of data. The generation unit can select the optimal dictionary data generation algorithm based on the type and format of the data. For example, in the case of voice data, the generation unit can apply a voice recognition algorithm to generate dictionary data. In addition, in the case of image data, the generation unit can apply an image recognition algorithm to generate dictionary data. Furthermore, in the case of video data, the generation unit can apply a video analysis algorithm to generate dictionary data. This enables efficient generation of dictionary data by applying the optimal dictionary data generation algorithm depending on the type of data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the type of data using a generation AI and apply the optimal dictionary data generation algorithm.

[0044] The generation unit can generate dictionary data taking into account attribute information of a data sender. The generation unit generates dictionary data, for example, taking into account the attribute information of the data sender. The generation unit can select an appropriate dictionary data generation method based on the attribute information of the sender. For example, the generation unit generates detailed dictionary data when the sender is highly reliable. Furthermore, the generation unit can generate simplified dictionary data when the sender is unreliable. Furthermore, the generation unit can select an appropriate dictionary data generation method based on the attribute information of the sender. This enables efficient generation of dictionary data by selecting an appropriate dictionary data generation method based on the attribute information of the sender. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can evaluate the attribute information of the sender using a generation AI and select an appropriate dictionary data generation method.

[0045] The generation unit can generate dictionary data taking into account geographical location information of a data transmission source. The generation unit generates dictionary data taking into account, for example, the geographical location information of the data transmission source. The generation unit can select an appropriate dictionary data generation method based on the geographical location information of the transmission source. For example, the generation unit generates detailed dictionary data when the transmission source is located nearby. Furthermore, the generation unit can generate simplified dictionary data when the transmission source is located far away. Furthermore, the generation unit can select an appropriate dictionary data generation method based on the geographical location information of the transmission source. This enables efficient generation of dictionary data by selecting an appropriate dictionary data generation method based on the geographical location information of the transmission source. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can evaluate the geographical location information of the transmission source using a generation AI and select an appropriate dictionary data generation method.

[0046] The generation unit can improve the accuracy of the dictionary data by referring to literature related to the data. The generation unit can, for example, improve the accuracy of the dictionary data by referring to literature related to the data. The generation unit can improve the accuracy of the dictionary data by applying the latest dictionary data generation method based on the related literature. For example, the generation unit can refer to literature related to the data and apply the latest dictionary data generation method. The generation unit can also improve the accuracy of the dictionary data by referring to literature related to the data. Furthermore, the generation unit can refer to literature related to the data and select an appropriate dictionary data generation method. As a result, by referring to the related literature, the latest dictionary data generation method can be applied and the accuracy of the dictionary data can be improved. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can improve the accuracy of the dictionary data by referring to literature related to the data using a generation AI.

[0047] The utilization unit can apply different dictionary data utilization algorithms depending on the type of data. The utilization unit, for example, applies different dictionary data utilization algorithms depending on the type of data. The utilization unit can select the optimal dictionary data utilization algorithm based on the type and format of the data. For example, in the case of voice data, the utilization unit can utilize the dictionary data by applying a voice recognition algorithm. In addition, in the case of image data, the utilization unit can utilize the dictionary data by applying an image recognition algorithm. Furthermore, in the case of video data, the utilization unit can utilize the dictionary data by applying a video analysis algorithm. This enables efficient use of dictionary data by applying the optimal dictionary data utilization algorithm depending on the type of data. Some or all of the above-mentioned processing in the utilization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the utilization unit can determine the type of data using a generation AI and apply the optimal dictionary data utilization algorithm.

[0048] The utilization unit can utilize the dictionary data taking into consideration attribute information of the data sender. The utilization unit, for example, utilizes the dictionary data taking into consideration attribute information of the data sender. The utilization unit can select an appropriate dictionary data utilization method based on the attribute information of the sender. For example, the utilization unit utilizes detailed dictionary data when the sender is highly reliable. Furthermore, the utilization unit can utilize simplified dictionary data when the sender is unreliable. Furthermore, the utilization unit can select an appropriate dictionary data utilization method based on the attribute information of the sender. This enables efficient utilization of dictionary data by selecting an appropriate dictionary data utilization method based on the attribute information of the sender. Some or all of the above-described processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can evaluate the attribute information of the sender using a generation AI and select an appropriate dictionary data utilization method.

[0049] The utilization unit can utilize the dictionary data taking into account the geographical location information of the data transmission source. The utilization unit, for example, utilizes the dictionary data taking into account the geographical location information of the data transmission source. The utilization unit can select an appropriate dictionary data utilization method based on the geographical location information of the data transmission source. For example, the utilization unit preferentially utilizes detailed dictionary data when the transmission source is located nearby. Furthermore, the utilization unit can utilize simplified dictionary data when the transmission source is located far away. Furthermore, the utilization unit can select an appropriate dictionary data utilization method based on the geographical location information of the transmission source. This enables efficient utilization of dictionary data by selecting an appropriate dictionary data utilization method based on the geographical location information of the transmission source. Some or all of the above-described processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can evaluate the geographical location information of the transmission source using a generation AI and select an appropriate dictionary data utilization method.

[0050] The utilization unit can improve the accuracy of dictionary data utilization by referring to literature related to the data. The utilization unit, for example, can improve the accuracy of dictionary data utilization by referring to literature related to the data. The utilization unit can improve the accuracy of dictionary data utilization by applying the latest dictionary data utilization method based on the related literature. For example, the utilization unit can refer to literature related to the data and apply the latest dictionary data utilization method. The utilization unit can also improve the accuracy of dictionary data utilization by referring to literature related to the data. Furthermore, the utilization unit can refer to literature related to the data and select an appropriate dictionary data utilization method. As a result, by referring to the related literature, the latest dictionary data utilization method can be applied and the accuracy of dictionary data utilization can be improved. Some or all of the above-mentioned processing in the utilization unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the utilization unit can refer to literature related to the data and improve the accuracy of dictionary data utilization.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The communication system can further include an energy management unit. The energy management unit monitors the remaining battery level of the IoT terminal and can adjust the data transmission frequency when the remaining battery level is low. For example, when the remaining battery level is 20% or less, the data transmission frequency is halved. The energy management unit can also increase the data transmission frequency when the terminal is charging. Furthermore, the energy management unit can learn the optimal energy consumption pattern according to the usage status of the terminal, thereby achieving efficient energy management.

[0053] The communication system may further include a security unit. The security unit may check the encryption status of received data and automatically encrypt unencrypted data. For example, if received text data is not encrypted, the security unit may apply AES encryption. The security unit may also check authentication information of the data sender and block data from unauthorized senders. Furthermore, the security unit may have a data tampering detection function and may check the integrity of received data.

[0054] The communication system may further include a data priority management unit. The data priority management unit evaluates the importance of received data and can prioritize processing of highly important data. For example, urgent data may be processed immediately, while less important data may be processed later. The data priority management unit may also customize data priorities based on user settings. Furthermore, the data priority management unit may learn past data processing history and automatically adjust the optimal data processing order.

[0055] The communication system may further include a data format conversion unit. The data format conversion unit can convert received data into a different format. For example, the data format conversion unit can convert received voice data into text data. The data format conversion unit can also compress image data before transmission. Furthermore, the data format conversion unit can convert video data into still images to reduce the amount of data. This allows data of different formats to be communicated efficiently.

[0056] The communication system may further include a data cache unit. The data cache unit temporarily stores received data and can reuse it as needed. For example, if the same data is transmitted multiple times, the data cache unit reuses the data that was previously stored. The data cache unit can also retain data when communication is interrupted and resume transmission after reconnection. Furthermore, the data cache unit can set a data retention period and automatically delete old data.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The receiver receives data sent from the IoT device. The receiver can efficiently receive data in various formats (text data, audio data, image data, video data, etc.), including data sent from low-spec IoT devices. Step 2: The analysis unit uses the generation AI to analyze the data received by the receiving unit. The analysis unit uses the generation AI (e.g., text generation AI or multimodal generation AI) to analyze the characteristics of the data in detail and generate dictionary data for compression. Step 3: The generator generates dictionary data for compression based on the data analyzed by the analyzer. The generator uses AI to generate efficient dictionary data that captures the characteristics of the data. Step 4: The utilization unit utilizes the dictionary data generated by the generation unit in the next communication. By utilizing the generated dictionary data in the next communication, the utilization unit improves communication efficiency.

[0059] (Example 2) A communication system according to an embodiment of the present invention is a system for efficiently communicating large amounts of data transmitted from IoT devices. This communication system aims to reduce the capacity (power, channel, human resources, etc.) of communication paths through which large amounts of data are transmitted from IoT devices, which are expected to become increasingly compact and reach 29 billion by 2030. While data compression using generative AI can achieve high compression rates, it has the drawback of being time-consuming and unsuitable for low-spec senders, such as IoT devices. Therefore, this invention assumes multiple communications from low-spec devices, and asynchronously analyzes each communication using generative AI on the receiver side. The analyzed data compression parameters (dictionary data) are used from the next communication, enabling continuously evolving communications. This mechanism captures the characteristics of the data and enables optimized, unique communications, potentially enabling developments in voice, image, and video data as well. For example, the receiver receives data transmitted from an IoT device, analyzes the data using generative AI, and generates compression parameters (dictionary data). This dictionary data is then used from the next communication. As a result, the data being communicated is gradually compressed more efficiently, reducing the capacity of the communication path. For example, in the case of voice data, the generation AI analyzes the characteristics of the voice data during the first communication and generates dictionary data for compression. This dictionary data can be used to efficiently compress the voice data during subsequent communications. Similarly, for image and video data, the generation AI captures the characteristics of the data and generates dictionary data for compression, which is then used during subsequent communications, achieving efficient communication. In this way, the present invention uses the generation AI to capture the characteristics of the data and achieve optimized, unique communication, thereby enabling efficient communication of large amounts of data transmitted from IoT devices. This allows the communication system to efficiently communicate large amounts of data transmitted from IoT devices.

[0060] A communication system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a utilizing unit. The receiving unit receives data transmitted from an IoT device. The receiving unit can receive data transmitted from, for example, a low-spec IoT device. The receiving unit can efficiently receive data regardless of the type or format of the data. For example, the receiving unit can receive data in various formats, such as text data, audio data, image data, and video data. The analyzing unit uses a generating AI to analyze the data received by the receiving unit. The analyzing unit, for example, uses the generating AI to analyze the characteristics of the data and generate dictionary data for compression. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, and can analyze the characteristics of the data in detail. For example, the analyzing unit uses the generating AI to analyze the characteristics of audio data and generate dictionary data for compression. The analyzing unit can also use the generating AI to analyze the characteristics of image data and generate dictionary data for compression. The analyzing unit can also use the generating AI to analyze the characteristics of video data and generate dictionary data for compression. The generating unit generates dictionary data for compression based on the data analyzed by the analyzing unit. The generation unit generates efficient dictionary data that captures the characteristics of the data, for example, using a generation AI. The generation AI can analyze the characteristics of the data in detail and generate dictionary data for optimal compression. For example, the generation unit generates dictionary data that captures the characteristics of audio data using a generation AI. The generation unit can also generate dictionary data that captures the characteristics of image data using a generation AI. The generation unit can also generate dictionary data that captures the characteristics of video data using a generation AI. The utilization unit uses the dictionary data generated by the generation unit in the next communication. For example, the utilization unit improves communication efficiency by using the generated dictionary data in the next communication. The utilization unit clarifies specific methods and criteria for using the generated dictionary data in the next communication. For example, the utilization unit uses the dictionary data based on the communication protocol and the timing of use. This enables the communication system according to the embodiment to efficiently receive and analyze data and generate and utilize dictionary data.

[0061] The generation unit can analyze data characteristics using a generation AI and generate dictionary data for compression. The generation unit can analyze data characteristics using a generation AI, for example, and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze data characteristics in detail. For example, the generation unit can analyze audio data characteristics using a generation AI and generate dictionary data for compression. The generation unit can also analyze image data characteristics using a generation AI and generate dictionary data for compression. Furthermore, the generation unit can analyze video data characteristics using a generation AI and generate dictionary data for compression. This enables efficient generation of dictionary data that captures data characteristics by using a generation AI. Some or all of the above-described processing in the generation unit can be performed using a generation AI, for example, or can be performed without using a generation AI. For example, the generation unit can analyze data characteristics using a generation AI and generate dictionary data for compression.

[0062] The utilization unit can use the generated dictionary data in the next communication. The utilization unit, for example, uses the generated dictionary data in the next communication. The utilization unit clarifies the specific method and criteria for using the generated dictionary data in the next communication. For example, the utilization unit uses the dictionary data based on the communication protocol and the timing of use. In this way, the generated dictionary data is used in the next communication, thereby improving communication efficiency. Some or all of the above-mentioned processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can use the generated dictionary data in the next communication.

[0063] The analysis unit can analyze the voice data and generate dictionary data for compression. The analysis unit can, for example, use a generation AI to analyze the characteristics of the voice data and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the characteristics of the voice data in detail. For example, the analysis unit can analyze the characteristics of the voice data using the generation AI and generate dictionary data for compression. This makes it possible to generate efficient dictionary data that captures the characteristics of the voice data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can analyze the characteristics of the voice data using the generation AI and generate dictionary data for compression.

[0064] The analysis unit can analyze the image data and generate dictionary data for compression. The analysis unit can, for example, use a generation AI to analyze the characteristics of the image data and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the characteristics of the image data in detail. For example, the analysis unit can analyze the characteristics of the image data using the generation AI and generate dictionary data for compression. This makes it possible to generate efficient dictionary data that captures the characteristics of the image data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can analyze the characteristics of the image data using the generation AI and generate dictionary data for compression.

[0065] The analysis unit can analyze the video data and generate dictionary data for compression. The analysis unit can, for example, use a generation AI to analyze the characteristics of the video data and generate dictionary data for compression. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the characteristics of the video data in detail. For example, the analysis unit can analyze the characteristics of the video data using the generation AI and generate dictionary data for compression. This makes it possible to generate efficient dictionary data that captures the characteristics of the video data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can analyze the characteristics of the video data using the generation AI and generate dictionary data for compression.

[0066] The receiving unit can estimate a user's emotion and adjust the timing of data reception based on the estimated user's emotion. For example, the receiving unit can estimate a user's emotion and adjust the timing of data reception based on the estimated user's emotion. The receiving unit can estimate a user's emotion using an emotion recognition algorithm or sensor data. For example, if the user is feeling stressed, the receiving unit can delay the timing of data reception to reduce the user's burden. Furthermore, if the user is relaxed, the receiving unit can advance the timing of data reception to achieve efficient communication. Furthermore, if the user is in a hurry, the receiving unit can optimize the timing of data reception to achieve rapid communication. This reduces the user's burden by adjusting the timing of data reception according to the user's emotion. Some or all of the above-described processing in the receiving unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the receiving unit can estimate a user's emotion using an emotion recognition algorithm and adjust the timing of data reception.

[0067] The receiving unit can select different receiving modes depending on the type of data. For example, the receiving unit selects different receiving modes depending on the type of data. The receiving unit can select the optimal receiving mode based on the type and format of data. For example, the receiving unit selects a low-latency mode for audio data, emphasizing real-time performance. The receiving unit can select a high-compression mode for image data, reducing the amount of data. Furthermore, the receiving unit can select a balanced mode for video data, balancing image quality and data amount. This enables efficient communication by selecting the optimal receiving mode depending on the type of data. Some or all of the above-described processing in the receiving unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the receiving unit can determine the type of data using a generation AI and select the optimal receiving mode.

[0068] The receiving unit can evaluate the reliability of a data sender and prioritize receiving highly reliable data. For example, the receiving unit can evaluate the reliability of a data sender and prioritize receiving highly reliable data. The receiving unit can evaluate the reliability based on the sender's past communication history, authentication information, geographical location information, etc. For example, the receiving unit can evaluate the sender's past communication history and prioritize receiving data from highly reliable senders. The receiving unit can also check the sender's authentication information and prioritize receiving data from highly reliable senders. Furthermore, the receiving unit can take the sender's geographical location information into consideration and prioritize receiving data from highly reliable senders. This prioritizes receiving highly reliable data, thereby improving the reliability of communication. Some or all of the above-described processing in the receiving unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the receiving unit can evaluate the reliability of a sender using a generation AI and prioritize receiving highly reliable data.

[0069] The receiving unit can estimate the user's emotion and determine the priority of data to be received based on the estimated user's emotion. For example, the receiving unit can estimate the user's emotion and determine the priority of data to be received based on the estimated user's emotion. The receiving unit can estimate the user's emotion using an emotion recognition algorithm or sensor data. For example, if the user is feeling stressed, the receiving unit can postpone receiving less important data. Furthermore, if the user is relaxed, the receiving unit can prioritize receiving more important data. Furthermore, if the user is in a hurry, the receiving unit can prioritize receiving more urgent data. This enables efficient data reception by determining the priority of data according to the user's emotion. Some or all of the above-described processing in the receiving unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the receiving unit can estimate the user's emotion using a generation AI and determine the priority of data to be received.

[0070] The receiving unit can select data to receive taking into consideration the geographical location information of the data sender. The receiving unit, for example, selects data to receive taking into consideration the geographical location information of the data sender. The receiving unit can determine the priority of data reception based on the geographical location information of the sender. For example, the receiving unit prioritizes reception of data when the sender is nearby. Furthermore, the receiving unit can postpone reception of data when the sender is far away. Furthermore, when the sender is in a specific region, the receiving unit can prioritize receiving data from that region. This enables efficient data reception by selecting data based on the geographical location information of the sender. Some or all of the above-described processing in the receiving unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the receiving unit can evaluate the geographical location information of the sender using a generation AI and determine the priority of data reception.

[0071] The receiving unit can filter received data by referring to the past communication history of the data sender. The receiving unit, for example, filters received data by referring to the past communication history of the data sender. The receiving unit can evaluate the reliability of data based on the past communication history of the data sender and filter out low-reliability data. For example, the receiving unit filters out low-reliability data from the past communication history of the sender. The receiving unit can also filter out low-importance data from the past communication history of the sender. Furthermore, the receiving unit can filter data based on a specific pattern from the past communication history of the sender. In this way, low-reliability data can be eliminated by filtering data based on the past communication history. Some or all of the above-described processing in the receiving unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the receiving unit can evaluate the past communication history of the sender using a generation AI and filter out data.

[0072] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. The analysis unit can estimate the user's emotions using an emotion recognition algorithm or sensor data. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is in a hurry. Furthermore, the analysis unit can reduce the frequency of analysis when the user is stressed. This enables efficient data analysis by adjusting the data analysis method according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can estimate the user's emotions using a generation AI and adjust the data analysis method.

[0073] The analysis unit can apply different analysis algorithms depending on the type of data. For example, the analysis unit applies different analysis algorithms depending on the type of data. The analysis unit can select the optimal analysis algorithm based on the type and format of the data. For example, the analysis unit can apply a voice recognition algorithm in the case of voice data. The analysis unit can apply an image recognition algorithm in the case of image data. Furthermore, the analysis unit can apply a video analysis algorithm in the case of video data. This enables efficient data analysis by applying the optimal analysis algorithm depending on the type of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the type of data using a generation AI and apply the optimal analysis algorithm.

[0074] The analysis unit can perform analysis taking into account attribute information of the data sender. The analysis unit, for example, performs analysis taking into account attribute information of the data sender. The analysis unit can select an appropriate analysis method based on the attribute information of the sender. For example, the analysis unit can perform a detailed analysis if the sender is highly reliable. Furthermore, the analysis unit can perform a simplified analysis if the sender is unreliable. Furthermore, the analysis unit can select an appropriate analysis method based on the attribute information of the sender. This enables efficient data analysis by selecting an appropriate analysis method based on the attribute information of the sender. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can evaluate the attribute information of the sender using a generation AI and select an appropriate analysis method.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using an emotion recognition algorithm or sensor data. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This enables highly visible display by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can estimate the user's emotions using a generation AI and adjust the display method of the analysis results.

[0076] The analysis unit can perform analysis taking into account geographical location information of the data sender. The analysis unit can, for example, perform analysis taking into account geographical location information of the data sender. The analysis unit can select an appropriate analysis method based on the geographical location information of the data sender. For example, the analysis unit can perform a detailed analysis if the sender is located close by. Furthermore, the analysis unit can perform a simplified analysis if the sender is located far away. Furthermore, the analysis unit can select an appropriate analysis method based on the geographical location information of the sender. This enables efficient data analysis by selecting an appropriate analysis method based on the geographical location information of the sender. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can evaluate the geographical location information of the sender using a generation AI and select an appropriate analysis method.

[0077] The analysis unit can improve the accuracy of the analysis by referring to literature related to the data. The analysis unit can, for example, improve the accuracy of the analysis by referring to literature related to the data. The analysis unit can apply the latest analysis method based on the related literature to improve the accuracy of the analysis results. For example, the analysis unit can refer to literature related to the data and apply the latest analysis method. The analysis unit can also refer to literature related to the data to improve the accuracy of the analysis results. Furthermore, the analysis unit can refer to literature related to the data and select an appropriate analysis method. In this way, by referring to the related literature, the latest analysis method can be applied and the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can use a generation AI to refer to related literature to improve the accuracy of the analysis.

[0078] The generation unit can estimate the user's emotion and adjust the dictionary data generation method based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the dictionary data generation method based on the estimated user's emotion. The generation unit can estimate the user's emotion using an emotion recognition algorithm or sensor data. For example, the generation unit can generate detailed dictionary data when the user is relaxed. Furthermore, the generation unit can generate simplified dictionary data when the user is in a hurry. Furthermore, the generation unit can reduce the frequency of dictionary data generation when the user is stressed. This enables efficient dictionary data generation by adjusting the dictionary data generation method according to the user's emotion. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can estimate the user's emotion using a generation AI and adjust the dictionary data generation method.

[0079] The generation unit can apply different dictionary data generation algorithms depending on the type of data. For example, the generation unit applies different dictionary data generation algorithms depending on the type of data. The generation unit can select the optimal dictionary data generation algorithm based on the type and format of the data. For example, in the case of voice data, the generation unit can apply a voice recognition algorithm to generate dictionary data. In addition, in the case of image data, the generation unit can apply an image recognition algorithm to generate dictionary data. Furthermore, in the case of video data, the generation unit can apply a video analysis algorithm to generate dictionary data. This enables efficient generation of dictionary data by applying the optimal dictionary data generation algorithm depending on the type of data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the type of data using a generation AI and apply the optimal dictionary data generation algorithm.

[0080] The generation unit can generate dictionary data taking into account attribute information of a data sender. The generation unit generates dictionary data, for example, taking into account the attribute information of the data sender. The generation unit can select an appropriate dictionary data generation method based on the attribute information of the sender. For example, the generation unit generates detailed dictionary data when the sender is highly reliable. Furthermore, the generation unit can generate simplified dictionary data when the sender is unreliable. Furthermore, the generation unit can select an appropriate dictionary data generation method based on the attribute information of the sender. This enables efficient generation of dictionary data by selecting an appropriate dictionary data generation method based on the attribute information of the sender. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can evaluate the attribute information of the sender using a generation AI and select an appropriate dictionary data generation method.

[0081] The generation unit can estimate the user's emotion and adjust the display method of the generated dictionary data based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the display method of the generated dictionary data based on the estimated user's emotion. The generation unit can estimate the user's emotion using an emotion recognition algorithm or sensor data. For example, if the user is nervous, the generation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that focuses on the main points. This enables highly visible display by adjusting the display method of the dictionary data according to the user's emotion. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can estimate the user's emotion using a generation AI and adjust the display method of the generated dictionary data.

[0082] The generation unit can generate dictionary data taking into account geographical location information of a data transmission source. The generation unit generates dictionary data taking into account, for example, the geographical location information of the data transmission source. The generation unit can select an appropriate dictionary data generation method based on the geographical location information of the transmission source. For example, the generation unit generates detailed dictionary data when the transmission source is located nearby. Furthermore, the generation unit can generate simplified dictionary data when the transmission source is located far away. Furthermore, the generation unit can select an appropriate dictionary data generation method based on the geographical location information of the transmission source. This enables efficient generation of dictionary data by selecting an appropriate dictionary data generation method based on the geographical location information of the transmission source. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can evaluate the geographical location information of the transmission source using a generation AI and select an appropriate dictionary data generation method.

[0083] The generation unit can improve the accuracy of the dictionary data by referring to literature related to the data. The generation unit can, for example, improve the accuracy of the dictionary data by referring to literature related to the data. The generation unit can improve the accuracy of the dictionary data by applying the latest dictionary data generation method based on the related literature. For example, the generation unit can refer to literature related to the data and apply the latest dictionary data generation method. The generation unit can also improve the accuracy of the dictionary data by referring to literature related to the data. Furthermore, the generation unit can refer to literature related to the data and select an appropriate dictionary data generation method. As a result, by referring to the related literature, the latest dictionary data generation method can be applied and the accuracy of the dictionary data can be improved. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can improve the accuracy of the dictionary data by referring to literature related to the data using a generation AI.

[0084] The utilization unit can estimate the user's emotions and adjust the usage of dictionary data based on the estimated user's emotions. For example, the utilization unit can estimate the user's emotions and adjust the usage of dictionary data based on the estimated user's emotions. The utilization unit can estimate the user's emotions using an emotion recognition algorithm or sensor data. For example, the utilization unit can use detailed dictionary data when the user is relaxed. Furthermore, the utilization unit can use simplified dictionary data when the user is in a hurry. Furthermore, the utilization unit can reduce the frequency of dictionary data usage when the user is stressed. This enables efficient use of dictionary data by adjusting the usage of dictionary data according to the user's emotions. Some or all of the above-described processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can estimate the user's emotions using a generation AI and adjust the usage of dictionary data.

[0085] The utilization unit can apply different dictionary data utilization algorithms depending on the type of data. The utilization unit, for example, applies different dictionary data utilization algorithms depending on the type of data. The utilization unit can select the optimal dictionary data utilization algorithm based on the type and format of the data. For example, in the case of voice data, the utilization unit can utilize the dictionary data by applying a voice recognition algorithm. In addition, in the case of image data, the utilization unit can utilize the dictionary data by applying an image recognition algorithm. Furthermore, in the case of video data, the utilization unit can utilize the dictionary data by applying a video analysis algorithm. This enables efficient use of dictionary data by applying the optimal dictionary data utilization algorithm depending on the type of data. Some or all of the above-mentioned processing in the utilization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the utilization unit can determine the type of data using a generation AI and apply the optimal dictionary data utilization algorithm.

[0086] The utilization unit can utilize the dictionary data taking into consideration attribute information of the data sender. The utilization unit, for example, utilizes the dictionary data taking into consideration attribute information of the data sender. The utilization unit can select an appropriate dictionary data utilization method based on the attribute information of the sender. For example, the utilization unit utilizes detailed dictionary data when the sender is highly reliable. Furthermore, the utilization unit can utilize simplified dictionary data when the sender is unreliable. Furthermore, the utilization unit can select an appropriate dictionary data utilization method based on the attribute information of the sender. This enables efficient utilization of dictionary data by selecting an appropriate dictionary data utilization method based on the attribute information of the sender. Some or all of the above-described processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can evaluate the attribute information of the sender using a generation AI and select an appropriate dictionary data utilization method.

[0087] The utilization unit can estimate the user's emotions and determine the usage priority of dictionary data based on the estimated user's emotions. The utilization unit can, for example, estimate the user's emotions and determine the usage priority of dictionary data based on the estimated user's emotions. The utilization unit can estimate the user's emotions using an emotion recognition algorithm or sensor data. For example, the utilization unit can prioritize the use of detailed dictionary data when the user is relaxed. Furthermore, the utilization unit can prioritize the use of simplified dictionary data when the user is in a hurry. Furthermore, the utilization unit can reduce the frequency of dictionary data usage when the user is stressed. This enables efficient use of dictionary data by determining the usage priority of dictionary data according to the user's emotions. Some or all of the above-described processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can estimate the user's emotions using a generation AI and determine the usage priority of dictionary data.

[0088] The utilization unit can utilize the dictionary data taking into account the geographical location information of the data transmission source. The utilization unit, for example, utilizes the dictionary data taking into account the geographical location information of the data transmission source. The utilization unit can select an appropriate dictionary data utilization method based on the geographical location information of the data transmission source. For example, the utilization unit preferentially utilizes detailed dictionary data when the transmission source is located nearby. Furthermore, the utilization unit can utilize simplified dictionary data when the transmission source is located far away. Furthermore, the utilization unit can select an appropriate dictionary data utilization method based on the geographical location information of the transmission source. This enables efficient utilization of dictionary data by selecting an appropriate dictionary data utilization method based on the geographical location information of the transmission source. Some or all of the above-described processing in the utilization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the utilization unit can evaluate the geographical location information of the transmission source using a generation AI and select an appropriate dictionary data utilization method.

[0089] The utilization unit can improve the accuracy of dictionary data utilization by referring to literature related to the data. The utilization unit, for example, can improve the accuracy of dictionary data utilization by referring to literature related to the data. The utilization unit can improve the accuracy of dictionary data utilization by applying the latest dictionary data utilization method based on the related literature. For example, the utilization unit can refer to literature related to the data and apply the latest dictionary data utilization method. The utilization unit can also improve the accuracy of dictionary data utilization by referring to literature related to the data. Furthermore, the utilization unit can refer to literature related to the data and select an appropriate dictionary data utilization method. As a result, by referring to the related literature, the latest dictionary data utilization method can be applied and the accuracy of dictionary data utilization can be improved. Some or all of the above-mentioned processing in the utilization unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the utilization unit can refer to literature related to the data and improve the accuracy of dictionary data utilization. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, generating unit, and utilizing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the smart device 14 and receives data transmitted from an IoT terminal. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data using a generation AI. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dictionary data for compression based on the analyzed data. The utilizing unit is realized, for example, by the control unit 46A of the smart device 14 and uses the generated dictionary data in the next communication. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, generating unit, and utilizing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the smart glasses 214 and receives data transmitted from an IoT terminal. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data using a generation AI. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dictionary data for compression based on the analyzed data. The utilizing unit is realized, for example, by the control unit 46A of the smart glasses 214 and uses the generated dictionary data in the next communication. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, generating unit, and utilizing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the headset type terminal 314 and receives data transmitted from the IoT terminal. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data using a generation AI. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dictionary data for compression based on the analyzed data. The utilizing unit is realized, for example, by the control unit 46A of the headset type terminal 314 and uses the generated dictionary data in the next communication. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, generating unit, and utilizing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the robot 414 and receives data transmitted from an IoT terminal. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data using a generation AI. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dictionary data for compression based on the analyzed data. The utilizing unit is realized, for example, by the control unit 46A of the robot 414 and uses the generated dictionary data in the next communication.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The communication system can further include an energy management unit. The energy management unit monitors the remaining battery level of the IoT terminal and can adjust the data transmission frequency when the remaining battery level is low. For example, when the remaining battery level is 20% or less, the data transmission frequency is halved. The energy management unit can also increase the data transmission frequency when the terminal is charging. Furthermore, the energy management unit can learn the optimal energy consumption pattern according to the usage status of the terminal, thereby achieving efficient energy management.

[0092] The communication system may further include a security unit. The security unit may check the encryption status of received data and automatically encrypt unencrypted data. For example, if received text data is not encrypted, the security unit may apply AES encryption. The security unit may also check authentication information of the data sender and block data from unauthorized senders. Furthermore, the security unit may have a data tampering detection function and may check the integrity of received data.

[0093] The communication system may further include a data priority management unit. The data priority management unit evaluates the importance of received data and can prioritize processing of highly important data. For example, urgent data may be processed immediately, while less important data may be processed later. The data priority management unit may also customize data priorities based on user settings. Furthermore, the data priority management unit may learn past data processing history and automatically adjust the optimal data processing order.

[0094] The communication system may further include a data format conversion unit. The data format conversion unit can convert received data into a different format. For example, the data format conversion unit can convert received voice data into text data. The data format conversion unit can also compress image data before transmission. Furthermore, the data format conversion unit can convert video data into still images to reduce the amount of data. This allows data of different formats to be communicated efficiently.

[0095] The communication system may further include a data cache unit. The data cache unit temporarily stores received data and can reuse it as needed. For example, if the same data is transmitted multiple times, the data cache unit reuses the data that was previously stored. The data cache unit can also retain data when communication is interrupted and resume transmission after reconnection. Furthermore, the data cache unit can set a data retention period and automatically delete old data.

[0096] The communication system may further include an emotion feedback unit. The emotion feedback unit may estimate the user's emotion and provide feedback based on the estimated emotion. For example, if the user is feeling stressed, the emotion feedback unit may provide relaxing music. Also, if the user is relaxed, the emotion feedback unit may send a notification to improve concentration. Furthermore, the emotion feedback unit may adjust the way data is displayed according to the user's emotion to improve visibility.

[0097] The communication system may further include an emotion history management unit. The emotion history management unit records the user's emotion history and can predict future emotions based on past emotion data. For example, it can analyze past emotion data and predict that the user will be prone to stress during certain time periods. The emotion history management unit can also learn the user's emotion patterns and provide optimal feedback. Furthermore, the emotion history management unit can visualize the user's emotion history and grasp changes in emotions.

[0098] The communication system may further include an emotion-based notification unit. The emotion-based notification unit may estimate the user's emotion and adjust the content and timing of notifications based on the estimated emotion. For example, if the user is relaxed, important notifications may be sent. Alternatively, if the user is stressed, notifications may be withheld. Furthermore, the emotion-based notification unit may determine the priority of notifications according to the user's emotion, thereby realizing efficient notification management.

[0099] The communication system may further include an emotion-based data filtering unit. The emotion-based data filtering unit may estimate the user's emotion and filter received data based on the estimated emotion. For example, if the user is feeling stressed, data of low importance may be filtered out. Alternatively, if the user is relaxed, all data may be received. Furthermore, the emotion-based data filtering unit may adjust the priority of data according to the user's emotion, thereby achieving efficient data reception.

[0100] The communication system may further include an emotion-based data display unit. The emotion-based data display unit may estimate the user's emotion and adjust the data display method based on the estimated emotion. For example, if the user is nervous, a simple, highly visible display method may be provided. Alternatively, if the user is relaxed, a display method including detailed information may be provided. Furthermore, if the user is in a hurry, the emotion-based data display unit may provide a display method that focuses on the main points. This allows for a highly visible display by adjusting the data display method according to the user's emotion.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The receiver receives data sent from the IoT device. The receiver can efficiently receive data in various formats (text data, audio data, image data, video data, etc.), including data sent from low-spec IoT devices. Step 2: The analysis unit uses the generation AI to analyze the data received by the receiving unit. The analysis unit uses the generation AI (e.g., text generation AI or multimodal generation AI) to analyze the characteristics of the data in detail and generate dictionary data for compression. Step 3: The generator generates dictionary data for compression based on the data analyzed by the analyzer. The generator uses AI to generate efficient dictionary data that captures the characteristics of the data. Step 4: The utilization unit utilizes the dictionary data generated by the generation unit in the next communication. By utilizing the generated dictionary data in the next communication, the utilization unit improves communication efficiency.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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. a receiving unit for receiving data; an analysis unit that analyzes the data received by the receiving unit; a generation unit that generates dictionary data for compression based on the data analyzed by the analysis unit; a utilization unit that utilizes the dictionary data generated by the generation unit in the next communication. A system characterized by:

2. The generation unit Analyze data characteristics using generative AI and generate dictionary data for compression 2. The system of claim 1.

3. The utilization unit includes: Use the generated dictionary data in the next communication 2. The system of claim 1.

4. The analysis unit Analyzes audio data and generates dictionary data for compression 2. The system of claim 1.

5. The analysis unit Analyzes image data and generates dictionary data for compression 2. The system of claim 1.

6. The analysis unit Analyzes video data and generates dictionary data for compression 2. The system of claim 1.

7. The receiving unit Estimates user emotions and adjusts data reception timing based on the estimated user emotions 2. The system of claim 1.

8. The receiving unit Select different receiving modes depending on the type of data 2. The system of claim 1.

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