System

Abstract

An object of a system according to an embodiment is to efficiently analyze data from a low-spec IoT terminal and perform feedback.SOLUTION: A system according to an embodiment includes a data acquisition unit, a data analysis unit, and a feedback unit. The data acquisition unit acquires data from a low-spec IoT terminal. The data-analyzing unit analyzes the data-acquired by the data-acquiring unit using the generated AI. The feedback unit feeds back the result analyzed by the data analysis unit to the sender.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to efficiently analyze data from low-spec IoT devices and provide feedback.

[0005] The system according to the embodiment aims to efficiently analyze data from low-spec IoT devices and provide feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a data acquisition unit, a data analysis unit, and a feedback unit. The data acquisition unit acquires data from a low-spec IoT device. The data analysis unit analyzes the data acquired by the data acquisition unit using a generation AI. The feedback unit feeds back the results of the analysis by the data analysis unit to the sender. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently analyze data from low-spec IoT terminals and provide feedback. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The communication optimization system according to an embodiment of the present invention is a system that uses generative AI on the receiver side to analyze data and feeds the results back to the sender in order to improve the efficiency of data communication from low-spec IoT devices. This solves the communication path capacity problem that comes with the increase in IoT devices, and is expected to expand the scope of voice, image, and video data.

[0029] A communication optimization system according to an embodiment includes a data acquisition unit, a data analysis unit, and a feedback unit. The data acquisition unit acquires data from a low-spec IoT device. For example, the data acquisition unit collects sensor data from the IoT device. The data acquisition unit can also acquire data via a network. The data acquisition unit can also convert the data into an appropriate format depending on the type of data. For example, the data acquisition unit acquires data from a temperature sensor and converts it into an appropriate format. The data analysis unit analyzes the data acquired by the data acquisition unit using a generation AI. For example, the data analysis unit extracts data features using a generation AI (e.g., a text generation AI or a multimodal generation AI). The data analysis unit can also derive data compression parameters using the generation AI. The data analysis unit can also select an optimal compression method depending on the type of data. For example, the data analysis unit selects an optimal compression method for voice data. The feedback unit feeds back the results of the analysis by the data analysis unit to the sender. For example, the feedback unit transmits the compression parameters to the sender. The feedback unit can also notify the sender of the data compression method. The feedback unit can also provide the sender with feedback content in real time. For example, the feedback unit can provide the sender with compression parameters in real time. This allows the communication optimization system according to the embodiment to improve the efficiency of data communication from low-spec IoT devices and solve the communication path capacity problem.

[0030] The data analysis unit can capture data characteristics and derive optimal compression parameters. The data analysis unit, for example, uses a generation AI to extract data characteristics. For example, the generation AI analyzes data patterns and statistical characteristics. The data analysis unit can also derive optimal compression parameters depending on the type of data. For example, the data analysis unit derives optimal compression parameters for voice data. The data analysis unit can also dynamically adjust the data compression rate. For example, the data analysis unit adjusts the compression rate depending on the load on the communication path. This improves communication efficiency by capturing data characteristics and deriving optimal compression parameters.

[0031] The feedback unit can transmit the compression parameters obtained on the receiver side to the sender side IoT terminal. For example, the feedback unit transmits the compression parameters obtained on the receiver side to the sender side IoT terminal. For example, the feedback unit provides the compression parameters to the sender side in real time. The feedback unit can also automatically apply the compression parameters to the sender side IoT terminal. For example, the feedback unit automatically applies the compression parameters to the sender side IoT terminal and compresses data using the parameters the next time data is transmitted. In this way, transmitting the compression parameters obtained on the receiver side to the sender side IoT terminal enables optimal compression the next time data is transmitted.

[0032] The data acquisition unit can acquire at least one of audio data, image data, and video data. The data acquisition unit acquires, for example, audio data. For example, the data acquisition unit collects audio data using a microphone. The data acquisition unit can also acquire image data. For example, the data acquisition unit collects image data using a camera. The data acquisition unit can also acquire video data. For example, the data acquisition unit collects video data using a video camera. In this way, by acquiring at least one of audio data, image data, and video data, it is possible to apply the optimal compression method to different types of data.

[0033] The data analysis unit can dynamically adjust the data compression rate. The data analysis unit adjusts the compression rate, for example, depending on the load status of the communication path. For example, the data analysis unit increases the compression rate when the communication path is congested and decreases the compression rate when the communication path is clear. The data analysis unit can also adjust the compression rate depending on the type of data. For example, the data analysis unit sets an optimal compression rate for voice data. In this way, the data compression rate can be dynamically adjusted, further improving communication efficiency.

[0034] The data acquisition unit can dynamically adjust the communication frequency of each IoT terminal. For example, the data acquisition unit monitors the communication frequency of each IoT terminal in real time and dynamically adjusts it according to the load status of the communication path. For example, the data acquisition unit reduces the communication frequency when the communication path is congested and increases the communication frequency when the communication path is empty. The data acquisition unit can also automatically adjust the communication frequency of each IoT terminal. For example, the data acquisition unit automatically adjusts the communication frequency based on the load status of the communication path. In this way, the load on the communication path can be optimized in real time by dynamically adjusting the communication frequency of each IoT terminal.

[0035] The data acquisition unit can predict the capacity of the communication path and automatically adjust the communication schedule based on the prediction result. For example, in order to predict the capacity of the communication path, the data acquisition unit analyzes past communication data and develops an algorithm that predicts future communication load. For example, the data acquisition unit predicts communication load at peak times based on past communication patterns. The data acquisition unit can also automatically adjust the communication schedule based on the prediction result. For example, the data acquisition unit predicts the capacity of the communication path and automatically adjusts the communication schedule based on the prediction result. In this way, by predicting the capacity of the communication path and automatically adjusting the communication schedule, communication efficiency can be maximized.

[0036] The data acquisition unit can visualize the capacity of the communication path and provide a dashboard that the user can intuitively understand. For example, the data acquisition unit develops a dashboard that displays the communication load in real time to visualize the capacity of the communication path. For example, the data acquisition unit displays the congestion status of the communication path in graphs or charts. The data acquisition unit can also provide an interface that the user can intuitively understand. For example, the data acquisition unit visualizes the capacity of the communication path and provides a dashboard that the user can intuitively understand. In this way, by visualizing the capacity of the communication path, the user can intuitively understand the communication status.

[0037] The data acquisition unit can automatically select different communication protocols and ensure the optimal communication path. The data acquisition unit, for example, develops an algorithm that automatically selects different communication protocols and builds a system that ensures the optimal communication path. For example, the data acquisition unit switches between protocols such as Wi-Fi, Bluetooth, and cellular communication depending on the situation. The data acquisition unit can also select the optimal communication protocol depending on the situation of the communication path. For example, the data acquisition unit selects cellular communication when the communication path is congested, and selects Wi-Fi when it is clear. In this way, the optimal communication path can be ensured by automatically selecting different communication protocols.

[0038] The data analysis unit distributes the compression process, allowing multiple terminals to cooperate to perform compression. The data analysis unit develops, for example, a system in which multiple terminals cooperate to perform compression in order to distribute the compression process. For example, the data analysis unit compresses data partially at each terminal and integrates the final compression results. The data analysis unit can also automatically assign tasks for distributed processing. For example, the data analysis unit assigns tasks according to the processing capacity of each terminal. In this way, by distributing the compression process, multiple terminals can cooperate to perform compression.

[0039] The data analysis unit can apply the compression algorithm to different data formats (audio, image, video) to increase versatility. For example, the data analysis unit develops the optimal compression method for audio data, image data, and video data in order to apply the compression algorithm to different data formats. For example, the data analysis unit applies an audio codec to audio data and an image compression algorithm to image data. The data analysis unit can also develop a general-purpose compression algorithm for different data formats. For example, the data analysis unit applies a common compression algorithm to audio, image, and video data. This allows the compression algorithm to be applied to different data formats, thereby increasing versatility.

[0040] The data analysis unit performs compression processing on the cloud, thereby reducing the load on low-spec devices. For example, to perform compression processing on the cloud, the data analysis unit constructs a system in which data is sent from low-spec devices to the cloud and compression processing is performed on the cloud. For example, the data analysis unit compresses data using generative AI on a cloud server. The data analysis unit can also optimize the compression processing on the cloud. For example, the data analysis unit optimizes the compression algorithm on the cloud to improve processing speed. In this way, by performing compression processing on the cloud, the load on low-spec devices can be reduced.

[0041] The data analysis unit can perform data analysis on the receiver side in real time and provide instant feedback to the sender. For example, the data analysis unit develops a system that uses a high-performance server or cloud environment to perform data analysis on the receiver side in real time. For example, the data analysis unit can instantly analyze received data and provide feedback to the sender. The data analysis unit can also optimize real-time data analysis. For example, the data analysis unit can improve the processing speed of the algorithm to achieve real-time data analysis. This allows data analysis on the receiver side to be performed in real time and provides instant feedback, thereby improving the efficiency of communication.

[0042] The data analysis unit accumulates data analysis results and can predict optimal compression parameters based on past data. The data analysis unit, for example, accumulates data analysis results and develops an algorithm that predicts optimal compression parameters based on past data. For example, the data analysis unit analyzes past communication data and derives optimal compression parameters. The data analysis unit can also dynamically adjust compression parameters based on accumulated data. For example, the data analysis unit adjusts compression parameters in real time based on past data. In this way, by accumulating data analysis results and predicting optimal compression parameters based on past data, communication efficiency is improved.

[0043] The data analysis unit can share the data analysis results on the receiver side with other IoT devices, thereby improving overall communication efficiency. For example, the data analysis unit develops a data sharing protocol to share the data analysis results on the receiver side with other IoT devices. For example, the data analysis unit transmits compression parameters obtained on the receiver side to other devices, improving overall communication efficiency. The data analysis unit can also automatically adjust the timing of data sharing. For example, the data analysis unit adjusts the timing of data sharing depending on the load status of the communication path. In this way, the data analysis results on the receiver side can be shared with other IoT devices, improving overall communication efficiency.

[0044] The data analysis unit can visualize the data analysis results and provide an interface that allows the user to intuitively understand. For example, the data analysis unit develops an interface using graphs and charts to visualize the data analysis results. For example, the data analysis unit displays the fluctuations in compression parameters in a graph, allowing the user to intuitively understand. The data analysis unit can also optimize the design of the user interface. For example, the data analysis unit provides an interface that is easy for the user to use. In this way, the visualization of the data analysis results allows the user to intuitively understand.

[0045] The feedback unit can automatically analyze the content of the feedback and generate optimal parameters for the next communication in real time. The feedback unit, for example, develops a system using natural language processing technology to automatically analyze the content of the feedback. For example, the feedback unit analyzes text data of the feedback and generates optimal parameters for the next communication. The feedback unit can also analyze the content of the feedback in real time. For example, the feedback unit analyzes the content of the feedback in real time and generates optimal parameters for the next communication. In this way, the efficiency of communication is improved by automatically analyzing the content of the feedback and generating optimal parameters for the next communication in real time.

[0046] The feedback unit can share the feedback content with other IoT terminals to improve overall communication efficiency. For example, the feedback unit develops a data sharing protocol to share the feedback content with other IoT terminals. For example, the feedback unit transmits the feedback content to other terminals to improve overall communication efficiency. The feedback unit can also automatically adjust the timing of data sharing. For example, the feedback unit adjusts the timing of data sharing depending on the load status of the communication path. In this way, the feedback content is shared with other IoT terminals to improve overall communication efficiency.

[0047] The feedback unit can visualize the content of the feedback and provide an interface that allows the user to intuitively understand it. For example, the feedback unit develops an interface using graphs or charts to visualize the content of the feedback. For example, the feedback unit displays the content of the feedback in a graph so that the user can intuitively understand it. The feedback unit can also optimize the design of the user interface. For example, the feedback unit provides an interface that is easy for the user to use. In this way, by visualizing the content of the feedback, the user can intuitively understand it.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The data acquisition unit can acquire environmental data and dynamically adjust communication parameters in response to changes in the environment. For example, the data acquisition unit collects environmental data such as temperature, humidity, and air pressure, and adjusts communication parameters based on this data. The data acquisition unit can also monitor environmental data in real time and respond to sudden environmental changes. For example, the data acquisition unit immediately adjusts communication parameters when a sudden temperature change is detected. This makes it possible to improve communication stability and efficiency by dynamically adjusting communication parameters based on environmental data.

[0050] The data analysis unit can evaluate the security level of the data and perform encryption as necessary. For example, the data analysis unit applies a strong encryption algorithm to highly confidential data. The data analysis unit can also select an appropriate encryption method depending on the type of data. For example, an encryption algorithm specifically for audio data can be applied to audio data, and an encryption algorithm specifically for images can be applied to image data. This makes it possible to improve communication efficiency while ensuring data security.

[0051] The feedback unit can provide feedback in multiple languages ​​to accommodate users who speak different languages. For example, the feedback unit can automatically translate the feedback and provide it in the user's language. The feedback unit can also provide feedback in an appropriate language based on the user's language setting. For example, the feedback unit can provide feedback in English to an English-speaking user and in Spanish to a Spanish-speaking user. This can improve user convenience by providing feedback in multiple languages.

[0052] The data acquisition unit can monitor the power status of each IoT device and adjust the communication frequency to optimize energy consumption. For example, the data acquisition unit can reduce the communication frequency of devices with low battery levels, thereby reducing power consumption. The data acquisition unit can also increase the communication frequency when the power supply is stable. For example, devices connected to a power source will communicate more frequently. This optimizes energy consumption and extends the battery life of IoT devices.

[0053] The data analysis unit can use a combination of different compression algorithms depending on the type of data. For example, a compression algorithm specifically for audio data can be applied to audio data, and a compression algorithm specifically for images can be applied to image data. The data analysis unit can also use a combination of multiple compression algorithms. For example, when audio data and image data are mixed, the optimal compression algorithm can be applied to each data type, improving the overall compression efficiency. This allows for optimal compression depending on the type of data, improving communication efficiency.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data acquisition unit acquires data from low-spec IoT devices. For example, the data acquisition unit can collect sensor data from IoT devices and acquire the data via a network. It can also convert the data into an appropriate format depending on the type of data. For example, it can acquire data from a temperature sensor and convert it into an appropriate format. Step 2: The data analysis unit uses a generation AI to analyze the data acquired by the data acquisition unit. For example, it can use a generation AI (e.g., text generation AI or multimodal generation AI) to extract data features and derive data compression parameters. It can also select the optimal compression method depending on the type of data. For example, it can select the optimal compression method for audio data. Step 3: The feedback unit feeds back the results of the analysis by the data analysis unit to the sender. For example, it can notify the sender of compression parameters and data compression methods in real time.

[0056] (Example 2) The communication optimization system according to an embodiment of the present invention is a system that uses generative AI on the receiver side to analyze data and feeds the results back to the sender in order to improve the efficiency of data communication from low-spec IoT devices. This solves the communication path capacity problem that comes with the increase in IoT devices, and is expected to expand the scope of voice, image, and video data.

[0057] A communication optimization system according to an embodiment includes a data acquisition unit, a data analysis unit, and a feedback unit. The data acquisition unit acquires data from a low-spec IoT device. For example, the data acquisition unit collects sensor data from the IoT device. The data acquisition unit can also acquire data via a network. The data acquisition unit can also convert the data into an appropriate format depending on the type of data. For example, the data acquisition unit acquires data from a temperature sensor and converts it into an appropriate format. The data analysis unit analyzes the data acquired by the data acquisition unit using a generation AI. For example, the data analysis unit extracts data features using a generation AI (e.g., a text generation AI or a multimodal generation AI). The data analysis unit can also derive data compression parameters using the generation AI. The data analysis unit can also select an optimal compression method depending on the type of data. For example, the data analysis unit selects an optimal compression method for voice data. The feedback unit feeds back the results of the analysis by the data analysis unit to the sender. For example, the feedback unit transmits the compression parameters to the sender. The feedback unit can also notify the sender of the data compression method. The feedback unit can also provide the sender with feedback content in real time. For example, the feedback unit can provide the sender with compression parameters in real time. This allows the communication optimization system according to the embodiment to improve the efficiency of data communication from low-spec IoT devices and solve the communication path capacity problem.

[0058] The data analysis unit can capture data characteristics and derive optimal compression parameters. The data analysis unit, for example, uses a generation AI to extract data characteristics. For example, the generation AI analyzes data patterns and statistical characteristics. The data analysis unit can also derive optimal compression parameters depending on the type of data. For example, the data analysis unit derives optimal compression parameters for voice data. The data analysis unit can also dynamically adjust the data compression rate. For example, the data analysis unit adjusts the compression rate depending on the load on the communication path. This improves communication efficiency by capturing data characteristics and deriving optimal compression parameters.

[0059] The feedback unit can transmit the compression parameters obtained on the receiver side to the sender side IoT terminal. For example, the feedback unit transmits the compression parameters obtained on the receiver side to the sender side IoT terminal. For example, the feedback unit provides the compression parameters to the sender side in real time. The feedback unit can also automatically apply the compression parameters to the sender side IoT terminal. For example, the feedback unit automatically applies the compression parameters to the sender side IoT terminal and compresses data using the parameters the next time data is transmitted. In this way, transmitting the compression parameters obtained on the receiver side to the sender side IoT terminal enables optimal compression the next time data is transmitted.

[0060] The data acquisition unit can acquire at least one of audio data, image data, and video data. The data acquisition unit acquires, for example, audio data. For example, the data acquisition unit collects audio data using a microphone. The data acquisition unit can also acquire image data. For example, the data acquisition unit collects image data using a camera. The data acquisition unit can also acquire video data. For example, the data acquisition unit collects video data using a video camera. In this way, by acquiring at least one of audio data, image data, and video data, it is possible to apply the optimal compression method to different types of data.

[0061] The data analysis unit can dynamically adjust the data compression rate. The data analysis unit adjusts the compression rate, for example, depending on the load status of the communication path. For example, the data analysis unit increases the compression rate when the communication path is congested and decreases the compression rate when the communication path is clear. The data analysis unit can also adjust the compression rate depending on the type of data. For example, the data analysis unit sets an optimal compression rate for voice data. In this way, the data compression rate can be dynamically adjusted, further improving communication efficiency.

[0062] The data acquisition unit can dynamically adjust the communication frequency of each IoT terminal. For example, the data acquisition unit monitors the communication frequency of each IoT terminal in real time and dynamically adjusts it according to the load status of the communication path. For example, the data acquisition unit reduces the communication frequency when the communication path is congested and increases the communication frequency when the communication path is empty. The data acquisition unit can also automatically adjust the communication frequency of each IoT terminal. For example, the data acquisition unit automatically adjusts the communication frequency based on the load status of the communication path. In this way, the load on the communication path can be optimized in real time by dynamically adjusting the communication frequency of each IoT terminal.

[0063] The data acquisition unit can predict the capacity of the communication path and automatically adjust the communication schedule based on the prediction result. For example, in order to predict the capacity of the communication path, the data acquisition unit analyzes past communication data and develops an algorithm that predicts future communication load. For example, the data acquisition unit predicts communication load at peak times based on past communication patterns. The data acquisition unit can also automatically adjust the communication schedule based on the prediction result. For example, the data acquisition unit predicts the capacity of the communication path and automatically adjusts the communication schedule based on the prediction result. In this way, by predicting the capacity of the communication path and automatically adjusting the communication schedule, communication efficiency can be maximized.

[0064] The data acquisition unit can dynamically change the communication priority based on the user's emotion using the emotion estimation function. The data acquisition unit, for example, uses the emotion estimation function to dynamically change the communication priority based on the user's emotion. For example, the data acquisition unit lowers the communication priority when the user is feeling stressed. The data acquisition unit can also automatically adjust the communication priority based on the user's emotion. For example, the data acquisition unit uses the emotion estimation function to analyze the user's emotion in real time and dynamically change the communication priority. In this way, by dynamically changing the communication priority based on the user's emotion, the user's stress is reduced and communication efficiency is improved.

[0065] The data acquisition unit can visualize the capacity of the communication path and provide a dashboard that the user can intuitively understand. For example, the data acquisition unit develops a dashboard that displays the communication load in real time to visualize the capacity of the communication path. For example, the data acquisition unit displays the congestion status of the communication path in graphs or charts. The data acquisition unit can also provide an interface that the user can intuitively understand. For example, the data acquisition unit visualizes the capacity of the communication path and provides a dashboard that the user can intuitively understand. In this way, by visualizing the capacity of the communication path, the user can intuitively understand the communication status.

[0066] The data acquisition unit can automatically select different communication protocols and ensure the optimal communication path. The data acquisition unit, for example, develops an algorithm that automatically selects different communication protocols and builds a system that ensures the optimal communication path. For example, the data acquisition unit switches between protocols such as Wi-Fi, Bluetooth, and cellular communication depending on the situation. The data acquisition unit can also select the optimal communication protocol depending on the situation of the communication path. For example, the data acquisition unit selects cellular communication when the communication path is congested, and selects Wi-Fi when it is clear. In this way, the optimal communication path can be ensured by automatically selecting different communication protocols.

[0067] The data acquisition unit can dynamically change the communication priority based on the user's emotion using the emotion estimation function. The data acquisition unit, for example, uses the emotion estimation function to dynamically change the communication priority based on the user's emotion. For example, the data acquisition unit lowers the communication priority when the user is feeling stressed. The data acquisition unit can also automatically adjust the communication priority based on the user's emotion. For example, the data acquisition unit uses the emotion estimation function to analyze the user's emotion in real time and dynamically change the communication priority. In this way, by dynamically changing the communication priority based on the user's emotion, the user's stress is reduced and communication efficiency is improved.

[0068] The data analysis unit distributes the compression process, allowing multiple terminals to cooperate to perform compression. The data analysis unit develops, for example, a system in which multiple terminals cooperate to perform compression in order to distribute the compression process. For example, the data analysis unit compresses data partially at each terminal and integrates the final compression results. The data analysis unit can also automatically assign tasks for distributed processing. For example, the data analysis unit assigns tasks according to the processing capacity of each terminal. In this way, by distributing the compression process, multiple terminals can cooperate to perform compression.

[0069] The data analysis unit can dynamically adjust the compression rate based on the user's emotions using the emotion estimation function. The data analysis unit, for example, uses the emotion estimation function to dynamically adjust the compression rate based on the user's emotions. For example, the data analysis unit reduces the compression rate when the user is feeling stressed. The data analysis unit can also automatically adjust the compression rate based on the user's emotions. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotions in real time and dynamically adjust the compression rate. In this way, by dynamically adjusting the compression rate based on the user's emotions, the user's stress is reduced and communication efficiency is improved.

[0070] The data analysis unit can apply the compression algorithm to different data formats (audio, image, video) to increase versatility. For example, the data analysis unit develops the optimal compression method for audio data, image data, and video data in order to apply the compression algorithm to different data formats. For example, the data analysis unit applies an audio codec to audio data and an image compression algorithm to image data. The data analysis unit can also develop a general-purpose compression algorithm for different data formats. For example, the data analysis unit applies a common compression algorithm to audio, image, and video data. This allows the compression algorithm to be applied to different data formats, thereby increasing versatility.

[0071] The data analysis unit performs compression processing on the cloud, thereby reducing the load on low-spec devices. For example, to perform compression processing on the cloud, the data analysis unit constructs a system in which data is sent from low-spec devices to the cloud and compression processing is performed on the cloud. For example, the data analysis unit compresses data using generative AI on a cloud server. The data analysis unit can also optimize the compression processing on the cloud. For example, the data analysis unit optimizes the compression algorithm on the cloud to improve processing speed. In this way, by performing compression processing on the cloud, the load on low-spec devices can be reduced.

[0072] The data analysis unit can select a compression algorithm based on the user's emotion using the emotion estimation function. The data analysis unit, for example, uses the emotion estimation function to select a compression algorithm based on the user's emotion. For example, if the user is feeling stressed, the data analysis unit selects an algorithm with a low compression rate and fast processing speed. The data analysis unit can also automatically select a compression algorithm based on the user's emotion. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotion in real time and select an optimal compression algorithm. In this way, selecting a compression algorithm based on the user's emotion reduces the user's stress and improves communication efficiency.

[0073] The data analysis unit can perform data analysis on the receiver side in real time and provide instant feedback to the sender. For example, the data analysis unit develops a system that uses a high-performance server or cloud environment to perform data analysis on the receiver side in real time. For example, the data analysis unit can instantly analyze received data and provide feedback to the sender. The data analysis unit can also optimize real-time data analysis. For example, the data analysis unit can improve the processing speed of the algorithm to achieve real-time data analysis. This allows data analysis on the receiver side to be performed in real time and provides instant feedback, thereby improving the efficiency of communication.

[0074] The data analysis unit accumulates data analysis results and can predict optimal compression parameters based on past data. The data analysis unit, for example, accumulates data analysis results and develops an algorithm that predicts optimal compression parameters based on past data. For example, the data analysis unit analyzes past communication data and derives optimal compression parameters. The data analysis unit can also dynamically adjust compression parameters based on accumulated data. For example, the data analysis unit adjusts compression parameters in real time based on past data. In this way, by accumulating data analysis results and predicting optimal compression parameters based on past data, communication efficiency is improved.

[0075] The data analysis unit can dynamically change the priority of data analysis based on the user's emotions using the emotion estimation function. The data analysis unit, for example, uses the emotion estimation function to dynamically change the priority of data analysis based on the user's emotions. For example, the data analysis unit lowers the priority of data analysis when the user is feeling stressed. The data analysis unit can also automatically adjust the priority of data analysis based on the user's emotions. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotions in real time and dynamically change the priority of data analysis. In this way, dynamically changing the priority of data analysis based on the user's emotions reduces the user's stress and improves communication efficiency.

[0076] The data analysis unit can share the data analysis results on the receiver side with other IoT devices, thereby improving overall communication efficiency. For example, the data analysis unit develops a data sharing protocol to share the data analysis results on the receiver side with other IoT devices. For example, the data analysis unit transmits compression parameters obtained on the receiver side to other devices, improving overall communication efficiency. The data analysis unit can also automatically adjust the timing of data sharing. For example, the data analysis unit adjusts the timing of data sharing depending on the load status of the communication path. In this way, the data analysis results on the receiver side can be shared with other IoT devices, improving overall communication efficiency.

[0077] The data analysis unit can visualize the data analysis results and provide an interface that allows the user to intuitively understand. For example, the data analysis unit develops an interface using graphs and charts to visualize the data analysis results. For example, the data analysis unit displays the fluctuations in compression parameters in a graph, allowing the user to intuitively understand. The data analysis unit can also optimize the design of the user interface. For example, the data analysis unit provides an interface that is easy for the user to use. In this way, the visualization of the data analysis results allows the user to intuitively understand.

[0078] The data analysis unit can select a data analysis method based on the user's emotion using the emotion estimation function. The data analysis unit, for example, uses the emotion estimation function to select a data analysis method based on the user's emotion. For example, the data analysis unit selects a simple data analysis method when the user is feeling stressed. The data analysis unit can also automatically select a data analysis method based on the user's emotion. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotion in real time and select the optimal data analysis method. In this way, selecting a data analysis method based on the user's emotion reduces the user's stress and improves communication efficiency.

[0079] The feedback unit can automatically analyze the content of the feedback and generate optimal parameters for the next communication in real time. The feedback unit, for example, develops a system using natural language processing technology to automatically analyze the content of the feedback. For example, the feedback unit analyzes text data of the feedback and generates optimal parameters for the next communication. The feedback unit can also analyze the content of the feedback in real time. For example, the feedback unit analyzes the content of the feedback in real time and generates optimal parameters for the next communication. In this way, the efficiency of communication is improved by automatically analyzing the content of the feedback and generating optimal parameters for the next communication in real time.

[0080] The feedback unit can customize the content of the feedback based on the user's emotion using the emotion estimation function. The feedback unit, for example, uses the emotion estimation function to customize the content of the feedback based on the user's emotion. For example, the feedback unit provides simple feedback when the user is feeling stressed. The feedback unit can also automatically adjust the content of the feedback based on the user's emotion. For example, the feedback unit uses the emotion estimation function to analyze the user's emotion in real time and customize the content of the feedback. In this way, customizing the content of the feedback based on the user's emotion reduces the user's stress and improves communication efficiency.

[0081] The feedback unit can share the feedback content with other IoT terminals to improve overall communication efficiency. For example, the feedback unit develops a data sharing protocol to share the feedback content with other IoT terminals. For example, the feedback unit transmits the feedback content to other terminals to improve overall communication efficiency. The feedback unit can also automatically adjust the timing of data sharing. For example, the feedback unit adjusts the timing of data sharing depending on the load status of the communication path. In this way, the feedback content is shared with other IoT terminals to improve overall communication efficiency.

[0082] The feedback unit can visualize the content of the feedback and provide an interface that allows the user to intuitively understand it. For example, the feedback unit develops an interface using graphs or charts to visualize the content of the feedback. For example, the feedback unit displays the content of the feedback in a graph so that the user can intuitively understand it. The feedback unit can also optimize the design of the user interface. For example, the feedback unit provides an interface that is easy for the user to use. In this way, by visualizing the content of the feedback, the user can intuitively understand it.

[0083] The feedback unit can select a feedback method based on the user's emotion using the emotion estimation function. The feedback unit, for example, uses the emotion estimation function to select a feedback method based on the user's emotion. For example, the feedback unit selects a simple feedback method when the user is feeling stressed. The feedback unit can also automatically select a feedback method based on the user's emotion. For example, the feedback unit uses the emotion estimation function to analyze the user's emotion in real time and select an optimal feedback method. In this way, selecting a feedback method based on the user's emotion reduces the user's stress and improves communication efficiency.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The data acquisition unit can acquire environmental data and dynamically adjust communication parameters in response to changes in the environment. For example, the data acquisition unit collects environmental data such as temperature, humidity, and air pressure, and adjusts communication parameters based on this data. The data acquisition unit can also monitor environmental data in real time and respond to sudden environmental changes. For example, the data acquisition unit immediately adjusts communication parameters when a sudden temperature change is detected. This makes it possible to improve communication stability and efficiency by dynamically adjusting communication parameters based on environmental data.

[0086] The data analysis unit can evaluate the security level of the data and perform encryption as necessary. For example, the data analysis unit applies a strong encryption algorithm to highly confidential data. The data analysis unit can also select an appropriate encryption method depending on the type of data. For example, an encryption algorithm specifically for audio data can be applied to audio data, and an encryption algorithm specifically for images can be applied to image data. This makes it possible to improve communication efficiency while ensuring data security.

[0087] The feedback unit can provide feedback in multiple languages ​​to accommodate users who speak different languages. For example, the feedback unit can automatically translate the feedback and provide it in the user's language. The feedback unit can also provide feedback in an appropriate language based on the user's language setting. For example, the feedback unit can provide feedback in English to an English-speaking user and in Spanish to a Spanish-speaking user. This can improve user convenience by providing feedback in multiple languages.

[0088] The data acquisition unit can monitor the power status of each IoT device and adjust the communication frequency to optimize energy consumption. For example, the data acquisition unit can reduce the communication frequency of devices with low battery levels, thereby reducing power consumption. The data acquisition unit can also increase the communication frequency when the power supply is stable. For example, devices connected to a power source will communicate more frequently. This optimizes energy consumption and extends the battery life of IoT devices.

[0089] The data analysis unit can use a combination of different compression algorithms depending on the type of data. For example, a compression algorithm specifically for audio data can be applied to audio data, and a compression algorithm specifically for images can be applied to image data. The data analysis unit can also use a combination of multiple compression algorithms. For example, when audio data and image data are mixed, the optimal compression algorithm can be applied to each data type, improving the overall compression efficiency. This allows for optimal compression depending on the type of data, improving communication efficiency.

[0090] The data acquisition unit can dynamically adjust the timing of data acquisition based on the user's emotions using the emotion estimation function. For example, the data acquisition unit increases the frequency of data acquisition when the user is relaxed and decreases the frequency when the user is stressed. The data acquisition unit can also automatically adjust the timing of data acquisition based on the user's emotions. For example, the emotion estimation function can be used to analyze the user's emotions in real time and dynamically change the timing of data acquisition. In this way, adjusting the timing of data acquisition based on the user's emotions can reduce the user's stress and improve communication efficiency.

[0091] The data analysis unit can use the emotion estimation function to evaluate the importance of data based on the user's emotions and adjust the compression rate according to the importance. For example, the data analysis unit can lower the compression rate for data that the user perceives as important, thereby preserving detailed information. On the other hand, the data analysis unit can increase the compression rate for data that the user perceives as less important, prioritizing communication efficiency. In this way, by evaluating the importance of data based on the user's emotions and achieving optimal compression, communication efficiency and user satisfaction can be improved.

[0092] The feedback unit can use the emotion estimation function to change the form of feedback based on the user's emotions. For example, if the user is feeling stressed, it provides concise and easy-to-understand feedback, and if the user is relaxed, it provides detailed feedback. The feedback unit can also automatically adjust the form of feedback based on the user's emotions. For example, it can use the emotion estimation function to analyze the user's emotions in real time and select the optimal feedback form. In this way, by changing the form of feedback based on the user's emotions, it is possible to reduce the user's stress and maximize the effectiveness of the feedback.

[0093] The data acquisition unit can dynamically change the priority of data acquisition based on the user's emotions using the emotion estimation function. For example, when the user is concentrating on an important task, the priority of data acquisition can be lowered to reduce the communication load. On the other hand, when the user is relaxed, the priority of data acquisition can be increased to collect detailed data. In this way, by dynamically changing the priority of data acquisition based on the user's emotions, communication efficiency and user comfort can be improved.

[0094] The data analysis unit can dynamically adjust the accuracy of data analysis based on the user's emotions using the emotion estimation function. For example, if the user is feeling stressed, the accuracy of data analysis can be lowered to prioritize processing speed. On the other hand, if the user is relaxed, the accuracy of data analysis can be increased to provide detailed analysis results. In this way, by dynamically adjusting the accuracy of data analysis based on the user's emotions, it is possible to reduce the user's stress and improve communication efficiency.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The data acquisition unit acquires data from low-spec IoT devices. For example, the data acquisition unit can collect sensor data from IoT devices and acquire the data via a network. It can also convert the data into an appropriate format depending on the type of data. For example, it can acquire data from a temperature sensor and convert it into an appropriate format. Step 2: The data analysis unit uses a generation AI to analyze the data acquired by the data acquisition unit. For example, it can use a generation AI (e.g., text generation AI or multimodal generation AI) to extract data features and derive data compression parameters. It can also select the optimal compression method depending on the type of data. For example, it can select the optimal compression method for audio data. Step 3: The feedback unit feeds back the results of the analysis by the data analysis unit to the sender. For example, it can notify the sender of compression parameters and data compression methods in real time.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0145] The data processing system 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 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 data acquisition unit that acquires data from low-spec IoT devices; a data analysis unit that analyzes the data acquired by the data acquisition unit using a generation AI; a feedback unit that feeds back the results of the analysis by the data analysis unit to the sender. A system characterized by:

2. The data analysis unit Identify the characteristics of the data and derive optimal compression parameters 2. The system of claim 1.

3. The data acquisition unit Acquire at least one of audio data, image data, and video data.

2. The system of claim 1.

4. The data acquisition unit Dynamically adjust the communication frequency of each IoT device 2. The system of claim 1.

5. The data analysis unit The compression algorithm of the generation AI will be made lighter, enabling it to run in real time even on low-spec devices.

2. The system of claim 1.

6. The data analysis unit Data analysis at the receiver's end is performed in real time, providing immediate feedback to the sender 2. The system of claim 1.

7. The feedback unit The content of the feedback is automatically analyzed, and optimal parameters for the next communication are generated in real time.

2. The system of claim 1.

8. The data acquisition unit Dynamically change communication priority based on user sentiment 2. The system of claim 1.

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