system

Abstract

The system according to the embodiment aims to efficiently collect local information such as road conditions and store information. [Solution] A system according to an embodiment includes a photographing unit, an analysis unit, and a transmission unit. The photographing unit photographs the area in front of the vehicle. The analysis unit analyzes the video photographed by the photographing unit and detects specific information. The transmission unit transmits the information detected by the analysis unit to a server.

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 technologies have had the problem of making it difficult to efficiently collect local information such as road conditions and store information.

[0005] The system according to the embodiment aims to efficiently collect local information such as road conditions and store information. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera unit, an analyzer, and a transmitter. The camera unit captures an image of the area in front of the vehicle. The analyzer analyzes the image captured by the camera unit and detects specific information. The transmitter transmits the information detected by the analyzer to a server. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect local information such as road conditions and store information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system according to an embodiment of the present invention is a system that builds a local information database by equipping vehicle dashcams (dashcams) with AI and communication capabilities and distributing them free of charge. This system captures the road ahead, analyzes the captured footage using AI, and detects information such as roadside store openings and closings, road openings, traffic accidents, and construction work, which it then transmits to a server. The server then builds a local information database based on the transmitted information and provides the latest local information updated in real time. This allows the system to efficiently collect local information without the high costs associated with Google Street View photography. For example, even if 100,000 dashcams, each costing ¥20,000, were distributed, the total cost would be ¥2 billion. Furthermore, communication fees would be free for a certain period of time, contributing to an increase in SIM card subscriptions by telecommunications companies. Furthermore, by partnering with automobile insurance companies, the system can offer premium discounts and special offers to encourage user adoption.

[0029] A local information collection system according to an embodiment includes a camera unit, an analyzer, and a transmitter. The camera unit captures an image of the area in front of the vehicle. The camera unit, for example, uses a high-resolution camera to clearly capture important information such as road signs and store signs. The camera unit also has an adjustment function for capturing high-quality images even at night or in bad weather. The analyzer analyzes the image captured by the camera unit and detects specific information. The analyzer detects store signs and road signs using image recognition technology, for example. The analyzer can also automatically identify events such as traffic accidents and construction work using AI. The transmitter transmits the information detected by the analyzer to a server. The transmitter transmits data to the server using, for example, a communication protocol. The transmitter selects the optimal transmission method depending on the communication environment to ensure data reliability. As a result, the local information collection system according to an embodiment can build a database of local information by capturing an image of the area in front of the vehicle, analyzing the image, and transmitting specific information to a server.

[0030] The analysis unit can detect store signs and road signs using image recognition technology. Image recognition technology includes, for example, deep learning, pattern recognition, and object detection algorithms. The analysis unit can detect store signs using, for example, deep learning. The analysis unit can also detect road signs using pattern recognition technology. The analysis unit can also identify specific objects in the video using object detection algorithms. As a result, store signs and road signs can be accurately detected using image recognition technology.

[0031] The transmitting unit can transmit data to the server using a communication protocol. Examples of communication protocols include HTTP, HTTPS, and MQTT. The transmitting unit transmits data to the server using, for example, HTTP. The transmitting unit can also transmit data while ensuring data security using HTTPS. The transmitting unit can also transmit data efficiently with a low bandwidth using MQTT. As a result, by using a communication protocol, data can be reliably transmitted to the server.

[0032] The server includes a database construction unit that constructs a database of local information based on the received information. The database construction unit, for example, organizes the received information and registers it in a database. The database construction unit also includes an algorithm that eliminates duplicate information and efficiently organizes data. Furthermore, the database construction unit can analyze the relevance of data and create indexes that enable efficient searches. This allows the server to provide the latest local information by constructing a database of local information based on the received information.

[0033] The database construction unit can include an update unit that updates the database in real time. For example, the update unit reflects new information received in the database in real time. The update unit also has the function of checking the consistency of the data and automatically correcting errors. Furthermore, the update unit can compare the data with past data to detect changes and notify the user of necessary information. This allows the database to be updated in real time, ensuring that the latest information is always provided.

[0034] The system is equipped with a privacy protection unit that protects users' privacy. The privacy protection unit, for example, anonymizes data to protect personal information. The privacy protection unit can also set detailed access permissions for data to strengthen security. Furthermore, the privacy protection unit has a function to clarify the purpose of data use and notify the user. This protects users' privacy and allows them to use the system with peace of mind.

[0035] The system includes a communication management unit that manages free communication periods. The communication management unit, for example, sets free communication periods and notifies users. The communication management unit also has the function of monitoring and optimizing communication volume in real time. Furthermore, the communication management unit can dynamically change communication protocols to improve communication efficiency. This allows for management of free communication periods, which can encourage user usage.

[0036] The system includes a reward provision unit that provides rewards in partnership with automobile insurance companies. The reward provision unit provides, for example, insurance premium discounts and rewards based on the user's usage. The reward provision unit can also analyze the user's past usage history and provide optimal rewards. Furthermore, the reward provision unit has a function to customize rewards based on the user's current situation. This makes it possible to encourage user usage by providing rewards in partnership with automobile insurance companies.

[0037] The camera unit can automatically adjust the camera settings according to the weather and time of day. For example, the camera unit can increase the camera's sensitivity to capture brighter images at night. It can also activate the camera's waterproof function to prevent lens fogging in rainy weather. It can also adjust the camera's exposure to reduce excessive brightness on sunny days. This allows the camera settings to be automatically adjusted according to the weather and time of day to capture optimal footage.

[0038] The imaging unit can dynamically change the imaging range based on the vehicle's speed and location information. For example, when the vehicle is traveling on a highway, the imaging unit captures a wide range. When the vehicle is traveling in an urban area, the imaging unit can also capture a detailed range. When the vehicle is stopped, the imaging unit can also capture detailed information about the surroundings. This allows the imaging range to be dynamically changed based on the vehicle's speed and location information, making it possible to capture an appropriate range.

[0039] The photographing unit can take photographs at high resolution when a specific event is detected. For example, when a traffic accident is detected, the photographing unit takes photographs at a higher resolution than normal. Furthermore, when a construction site is detected, the photographing unit can take photographs at high resolution to record detailed information. Furthermore, when a road opening is detected, the photographing unit can take photographs at high resolution to record details of the new road. In this way, detailed information can be recorded by taking photographs at high resolution when a specific event is detected.

[0040] The image capturing unit can analyze the sound in the car and capture important information. For example, the image capturing unit can analyze conversations in the car and capture important information. It can also analyze warning sounds in the car and capture important information. It can also analyze music in the car and capture important information. In this way, important information can be captured by analyzing the sound in the car.

[0041] The camera unit can detect the movement of other vehicles and record dangerous situations. For example, if another vehicle suddenly brakes, the camera unit records that movement. It can also record the movement of another vehicle if it suddenly changes lanes. It can also record the movement of another vehicle if it behaves abnormally. In this way, by detecting the movement of other vehicles, dangerous situations can be recorded.

[0042] The camera unit can record the external environment of the vehicle and store it together with the video. For example, the camera unit can record the external temperature when taking a picture and store it together with the video. It can also record the external humidity when taking a picture and store it together with the video. It can also record the external air pressure when taking a picture and store it together with the video. In this way, by recording the external environment of the vehicle, detailed information can be stored together with the video.

[0043] The analysis unit can identify specific objects in the video. For example, the analysis unit can identify pedestrians in the video and analyze their movements. It can also identify bicycles in the video and analyze their movements. It can also identify vehicles in the video and analyze their movements. This allows for detailed analysis by identifying specific objects in the video.

[0044] The analysis unit can dynamically adjust the frame rate of the video so that important information is not missed. For example, the analysis unit can increase the frame rate for scenes containing important information to perform detailed analysis. For normal scenes, the analysis unit can also set the frame rate to a lower value for efficient analysis. Furthermore, when an important event occurs, the frame rate can be dynamically adjusted for analysis. In this way, by dynamically adjusting the frame rate of the video, important information can be analyzed without being missed.

[0045] The analysis unit can detect abnormalities by comparing with past data. For example, the analysis unit can detect abnormal movements by comparing with past data. It can also detect abnormal sounds by comparing with past data. It can also detect abnormal environmental changes by comparing with past data. In this way, it is possible to detect abnormalities by comparing with past data.

[0046] The analysis unit can analyze the audio data and link it to the video. For example, the analysis unit can analyze the audio data in the video and extract important information. It can also link the audio data with the video and display the analysis results. It can also analyze the audio data and identify specific events in the video. In this way, by analyzing the audio data, it is possible to provide detailed information linked to the video.

[0047] The analysis unit can integrate and analyze data from other sensors. For example, the analysis unit can integrate GPS data to analyze vehicle location information. It can also integrate accelerometer data to analyze vehicle movement. It can also integrate data from other sensors to perform comprehensive analysis. This makes it possible to perform comprehensive analysis by integrating data from other sensors.

[0048] The analysis unit can improve the accuracy of analysis by automatically adjusting the color tone and brightness of the image. For example, the analysis unit can improve the accuracy of analysis by automatically adjusting the color tone of the image. It can also improve the accuracy of analysis by automatically adjusting the brightness of the image. It can also improve the accuracy of analysis by automatically adjusting the contrast of the image. In this way, the analysis accuracy is improved by automatically adjusting the color tone and brightness of the image.

[0049] The transmitting unit can dynamically change the data compression rate to optimize the amount of communication. For example, when the communication environment is good, the transmitting unit can lower the compression rate to transmit high-quality data. When the communication environment is poor, the transmitting unit can also increase the compression rate to reduce the amount of communication. The compression rate can also be dynamically changed depending on the importance of the data. In this way, the amount of communication can be optimized by dynamically changing the data compression rate.

[0050] The transmitter can improve the reliability of data by using a plurality of communication protocols. The transmitter, for example, improves the reliability of data by using a plurality of communication protocols. The transmitter can also improve the reliability of data by ensuring redundancy of the communication protocols. The transmitter can also improve the reliability of data by dynamically changing the selection of the communication protocol. In this way, the reliability of data is improved by using a plurality of communication protocols.

[0051] The transmitting unit can set the priority of data and transmit important information preferentially. The transmitting unit sets the priority of data, for example, to transmit important information preferentially. Also, the transmitting unit can send important information quickly, putting ordinary information on hold. Also, the transmission priority can be dynamically changed depending on the importance of the data. In this way, by setting the priority of data, important information can be sent quickly.

[0052] The transmitting unit can encrypt data to strengthen security. For example, the transmitting unit can encrypt data when transmitting it to strengthen security. Furthermore, it can perform strong encryption when transmitting important data. It can also perform standard encryption when transmitting ordinary data. In this way, data encryption strengthens security.

[0053] The transmitting unit can select the optimal transmission method depending on the communication environment. For example, the transmitting unit can transmit data at high speed in a Wi-Fi environment. It can also transmit data with reduced communication volume in a 4G environment. It can also transmit large amounts of data at high speed in a 5G environment. This allows efficient data transmission by selecting the optimal transmission method depending on the communication environment.

[0054] The transmitting unit can set multiple destinations for data transmission to ensure backups. The transmitting unit can, for example, set multiple destinations for data transmission to ensure backups. It can also transmit important data to multiple servers to ensure data redundancy. It can also transmit normal data to multiple destinations to improve data reliability. Thus, by setting multiple destinations for data transmission, data redundancy can be ensured.

[0055] The database construction unit can eliminate duplicate data and organize data efficiently. For example, the database construction unit automatically detects and eliminates duplicate data when constructing a database. It can also use an efficient data organization algorithm when constructing a database. It can also set rules for eliminating duplicate data when constructing a database. This makes it possible to efficiently organize data by eliminating duplicate data.

[0056] The database construction unit can analyze the relevance of data and enable efficient searches. For example, the database construction unit automatically analyzes the relevance of data when constructing a database. It can also group highly related data when constructing a database. It can also create indexes to enable efficient searches when constructing a database. In this way, analyzing the relevance of data enables efficient searches.

[0057] The database construction unit can automatically back up data. For example, the database construction unit automatically backs up data periodically when constructing a database. Furthermore, when constructing a database, it is also possible to prioritize the backup of important data. It is also possible to set a backup schedule when constructing a database. In this way, the safety of data is ensured by automatically backing up data.

[0058] The database construction unit can integrate different data formats. For example, the database construction unit integrates text data and image data when constructing a database. It can also integrate voice data and text data when constructing a database. It can also integrate different data formats when constructing a database to enable efficient searches. In this way, integrating different data formats enables efficient searches.

[0059] The database construction unit can link with external data sources. For example, the database construction unit may link with public databases to acquire data when constructing the database. The database construction unit may also link with external data sources to integrate data when constructing the database. The database construction unit may also acquire data in real time from external data sources when constructing the database. This allows more information to be acquired by linking with external data sources.

[0060] The database construction unit can set data access permissions and strengthen security. For example, the database construction unit sets data access permissions in detail when constructing the database. It can also set strict access permissions for important data when constructing the database. It can also dynamically change the access permission settings when constructing the database. In this way, security can be strengthened by setting data access permissions.

[0061] The update unit can check the consistency of data and automatically correct errors. For example, the update unit automatically checks the consistency of data when updating. It can also automatically correct errors when updating. It can also set rules for checking the consistency of data when updating. This allows the reliability of data to be improved by checking the consistency of data and automatically correcting errors.

[0062] The update unit can detect changes by comparing with past data and notify the user of necessary information. For example, the update unit automatically detects changes by comparing with past data at the time of updating. If changes are detected at the time of updating, the update unit can also notify the user of necessary information. Rules can also be set for detecting changes by comparing with past data at the time of updating. This allows the user to quickly notify the user of necessary information by detecting changes by comparing with past data.

[0063] The update unit performs version management of data and can make past data referable. The update unit, for example, automatically performs version management of data when updating. It can also perform version management to make past data referable when updating. It can also set rules for performing version management of data when updating. In this way, performing version management of data makes past data referable and makes data tracking easier.

[0064] The update unit compresses data and can reduce the amount of communication. For example, the update unit compresses data when updating to reduce the amount of communication. It is also possible to compress important data preferentially when updating. It is also possible to set rules for compressing data when updating. In this way, data compression can reduce the amount of communication.

[0065] The update unit can synchronize data between different devices. For example, the update unit automatically synchronizes data between different devices when updating. It can also prioritize synchronization of important data when updating. It can also set rules for synchronizing data between devices when updating. This allows data consistency to be maintained by synchronizing data between different devices.

[0066] The update unit can record the history of changes to data, making it possible to trace the data. For example, the update unit automatically records the history of changes to data when updating. It can also prioritize recording the history of changes to important data when updating. It can also set rules for recording the history of changes to data when updating. By recording the history of changes to data, it becomes easier to trace the data.

[0067] The privacy protection unit can anonymize data to protect personal information. For example, the privacy protection unit automatically anonymizes data when privacy protection is in effect. Furthermore, the privacy protection unit can also prioritize the anonymization of important data when privacy protection is in effect. Furthermore, rules for anonymizing data when privacy protection is in effect can be set. In this way, the protection of personal information is strengthened by anonymizing data.

[0068] The privacy protection unit can set data access permissions in detail. For example, the privacy protection unit sets data access permissions in detail when privacy is being protected. Also, it can set access permissions for important data strictly when privacy is being protected. It can also dynamically change the settings of access permissions when privacy is being protected. In this way, security can be strengthened by setting data access permissions in detail.

[0069] The privacy protection unit can clarify the purpose of data use and notify the user. For example, the privacy protection unit clarifies the purpose of data use when privacy is being protected and notifies the user. It can also prioritize the notification of the purpose of important data use when privacy is being protected. It can also set rules for notifying the purpose of data use when privacy is being protected. In this way, by clarifying the purpose of data use, it is possible to gain the trust of users.

[0070] The privacy protection unit can encrypt data and strengthen security. For example, the privacy protection unit automatically encrypts data when privacy protection is enabled. Furthermore, important data can be encrypted preferentially when privacy protection is enabled. Furthermore, rules for encrypting data when privacy protection is enabled can be set. In this way, data encryption strengthens security.

[0071] The privacy protection unit can restrict the provision of data to third parties. For example, the privacy protection unit automatically restricts the provision of data to third parties when privacy is being protected. Furthermore, when privacy is being protected, the privacy protection unit can also preferentially restrict the provision of important data. Furthermore, when privacy is being protected, rules can be set to restrict the provision of data to third parties. This strengthens the protection of personal information by restricting the provision of data to third parties.

[0072] The privacy protection unit can use data after obtaining the user's consent. For example, the privacy protection unit can use data after obtaining the user's consent when protecting privacy. Furthermore, the privacy protection unit can also prioritize obtaining the user's consent for the use of important data when protecting privacy. Furthermore, rules for obtaining the user's consent when protecting privacy can be set. In this way, the privacy protection unit can gain the user's trust by using data after obtaining the user's consent.

[0073] The communication management unit can monitor and optimize communication volume in real time. For example, the communication management unit monitors communication volume in real time during communication management. It can also use an algorithm to optimize communication volume during communication management. It can also perform optimization based on the results of monitoring communication volume during communication management. As a result, efficient communication is possible by monitoring and optimizing communication volume in real time.

[0074] The communication management unit can dynamically change the communication protocol to improve communication efficiency. The communication management unit, for example, dynamically changes the communication protocol during communication management. Also, during communication management, it can select a protocol to improve communication efficiency. Also, it can automatically change the communication protocol during communication management. In this way, by dynamically changing the communication protocol, communication efficiency is improved.

[0075] The communication management unit can automatically detect communication errors and perform retransmission. For example, the communication management unit automatically detects communication errors during communication management. Also, if a communication error occurs during communication management, it can automatically perform retransmission. Also, rules for detecting communication errors and performing retransmission can be set during communication management. This automatically detects communication errors and performs retransmission, improving the reliability of communication.

[0076] The communication management unit can select the optimal communication method depending on the communication environment. For example, the communication management unit selects a high-speed communication method in a Wi-Fi environment. It can also select a communication method with reduced communication volume in a 4G environment. It can also select a high-speed, high-capacity communication method in a 5G environment. This enables efficient communication by selecting the optimal communication method depending on the communication environment.

[0077] The communication management unit compresses communication data and can reduce the amount of communication. For example, the communication management unit automatically compresses communication data when managing communication. It can also compress important data preferentially when managing communication. It can also set rules for compressing communication data when managing communication. In this way, it is possible to reduce the amount of communication by compressing communication data.

[0078] The communication management unit can set communication priorities and transmit important data preferentially. The communication management unit, for example, automatically sets communication priorities when managing communications. It can also transmit important data preferentially when managing communications. It can also set rules for setting communication priorities when managing communications. By setting communication priorities, important data can be transmitted quickly.

[0079] The reward provision unit can analyze the user's past usage history and provide the optimal reward. For example, the reward provision unit analyzes the user's past usage history when providing a reward and provides the optimal reward. Furthermore, when providing a reward, the reward provision unit can customize the content of the reward based on the user's past usage history. Furthermore, when providing a reward, the reward provision timing can be adjusted based on the user's past usage history. In this way, the optimal reward can be provided by analyzing the user's past usage history.

[0080] The reward provision unit can customize a reward based on the user's current situation. For example, the reward provision unit analyzes the user's driving situation when providing a reward and provides an optimal reward. The reward provision unit can also customize the content of the reward based on the user's current situation when providing a reward. The reward provision unit can also adjust the timing of providing the reward based on the user's driving situation when providing a reward. In this way, by customizing a reward based on the user's current situation, more appropriate rewards can be provided.

[0081] The reward provision unit can improve the reward content by reflecting user feedback. For example, the reward provision unit improves the reward content based on user feedback when providing the reward. The reward provision unit can also adjust the reward provision method by reflecting user feedback when providing the reward. The reward content can also be customized based on user feedback when providing the reward. In this way, the reward content can be improved by reflecting user feedback.

[0082] The reward provision unit can provide an optimal reward by taking into account the user's geographical location information. For example, the reward provision unit provides an optimal reward based on the user's geographical location information when providing a reward. The reward provision unit can also customize the content of the reward based on the user's current location when providing a reward. The reward provision unit can also adjust the timing of providing the reward by taking into account the user's geographical location information when providing a reward. In this way, the optimal reward can be provided by taking into account the user's geographical location information.

[0083] The reward provision unit can analyze the user's social media activity and provide a related reward. For example, the reward provision unit can analyze the user's social media activity when providing a reward and provide a related reward. Furthermore, the reward content can be customized based on the user's social media posts when providing a reward. Furthermore, the timing of providing a reward can be adjusted taking the user's social media activity into consideration when providing a reward. In this way, a related reward can be provided by analyzing the user's social media activity.

[0084] The reward provision unit can customize the content of the reward by reflecting the user's past feedback. For example, the reward provision unit customizes the content of the reward based on the user's past feedback when providing the reward. Furthermore, the reward provision unit can adjust the method of providing the reward by reflecting the user's past feedback when providing the reward. Furthermore, the reward provision timing can be adjusted based on the user's past feedback when providing the reward. In this way, the content of the reward can be customized by reflecting the user's past feedback.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit can learn the user's driving style and adjust the accuracy of analysis based on the driving style. For example, if the user frequently brakes suddenly, the analysis unit can take that movement into account to improve the accuracy of traffic accident detection. Also, if the user frequently uses expressways, the analysis unit can perform analysis specialized for detecting events on expressways. Furthermore, if the user often drives at night, the accuracy of nighttime video analysis can be improved. This makes it possible to adjust the analysis accuracy according to the user's driving style, and more accurate information can be provided.

[0087] The transmitter takes into account the user's driving conditions when transmitting data, and can minimize data transmission while driving. For example, when the user is driving on a highway, data transmission can be temporarily stopped and transmitted all at once when the user arrives at a safe location. In addition, when the user is stuck in traffic, data can be compressed and transmitted to reduce communication volume. Furthermore, when the user is parked, normal data transmission can be performed. This makes it possible to optimize data transmission according to the driving conditions, achieving safe and efficient data transmission.

[0088] The transmitting unit can set multiple destinations for data transmission to ensure backups. For example, multiple destinations for data transmission can be set to ensure backups. Important data can also be sent to multiple servers to ensure data redundancy. Furthermore, regular data can be sent to multiple destinations to improve data reliability. Thus, by setting multiple destinations for data transmission, data redundancy can be ensured.

[0089] The analysis unit can dynamically adjust the frame rate of the video to ensure that important information is not missed. For example, in scenes containing important information, the frame rate can be increased for detailed analysis. In addition, in normal scenes, the frame rate can be set lower for efficient analysis. Furthermore, when an important event occurs, the frame rate can be dynamically adjusted for analysis. This allows for dynamic adjustment of the video frame rate, making it possible to analyze without missing any important information.

[0090] The database construction unit can integrate different data formats. For example, text data and image data can be integrated when building a database. Voice data and text data can also be integrated when building a database. Furthermore, different data formats can be integrated when building a database to enable efficient searches. In this way, integrating different data formats enables efficient searches.

[0091] The communication management unit can automatically detect communication errors and perform retransmission. For example, it can automatically detect communication errors during communication management. It can also automatically perform retransmission if a communication error occurs during communication management. Furthermore, it can also set rules for detecting communication errors and performing retransmission during communication management. This allows for automatic detection of communication errors and retransmission, improving the reliability of communication.

[0092] The processing flow of the first embodiment will be briefly explained below.

[0093] Step 1: The camera unit captures the area in front of the vehicle. The camera unit uses a high-resolution camera to clearly capture important information such as road signs and store signs. It also has adjustment functions to capture high-quality images even at night or in bad weather. Step 2: The analysis unit analyzes the video captured by the camera unit and detects specific information. The analysis unit uses image recognition technology to detect store signs and road signs, and uses AI to automatically identify events such as traffic accidents and construction work. Step 3: The transmitter sends the information detected by the analyzer to the server. The transmitter uses a communication protocol to send the data to the server, selecting the optimal transmission method depending on the communication environment to ensure the reliability of the data.

[0094] (Example 2) The system according to an embodiment of the present invention is a system that builds a local information database by equipping vehicle dashcams (dashcams) with AI and communication capabilities and distributing them free of charge. This system captures the road ahead, analyzes the captured footage using AI, and detects information such as roadside store openings and closings, road openings, traffic accidents, and construction, and transmits it to a server. The server then builds a local information database based on the transmitted information and provides the latest local information updated in real time. This allows the system to efficiently collect local information without the high costs associated with Google Street View. For example, even if 100,000 dashcams, each costing ¥20,000, were distributed, the total cost would be ¥2 billion. Furthermore, communication fees would be free for a certain period of time, contributing to an increase in SIM card subscriptions by telecommunications companies. Furthermore, by partnering with automobile insurance companies, the system can offer discounts and special offers on insurance premiums, encouraging user adoption.

[0095] A local information collection system according to an embodiment includes a camera unit, an analyzer, and a transmitter. The camera unit captures an image of the area in front of the vehicle. The camera unit, for example, uses a high-resolution camera to clearly capture important information such as road signs and store signs. The camera unit also has an adjustment function for capturing high-quality images even at night or in bad weather. The analyzer analyzes the image captured by the camera unit and detects specific information. The analyzer detects store signs and road signs using image recognition technology, for example. The analyzer can also automatically identify events such as traffic accidents and construction work using AI. The transmitter transmits the information detected by the analyzer to a server. The transmitter transmits data to the server using, for example, a communication protocol. The transmitter selects the optimal transmission method depending on the communication environment to ensure data reliability. As a result, the local information collection system according to an embodiment can build a database of local information by capturing an image of the area in front of the vehicle, analyzing the image, and transmitting specific information to a server.

[0096] The analysis unit can detect store signs and road signs using image recognition technology. Image recognition technology includes, for example, deep learning, pattern recognition, and object detection algorithms. The analysis unit can detect store signs using, for example, deep learning. The analysis unit can also detect road signs using pattern recognition technology. The analysis unit can also identify specific objects in the video using object detection algorithms. As a result, store signs and road signs can be accurately detected using image recognition technology.

[0097] The transmitting unit can transmit data to the server using a communication protocol. Examples of communication protocols include HTTP, HTTPS, and MQTT. The transmitting unit transmits data to the server using, for example, HTTP. The transmitting unit can also transmit data while ensuring data security using HTTPS. The transmitting unit can also transmit data efficiently with a low bandwidth using MQTT. As a result, by using a communication protocol, data can be reliably transmitted to the server.

[0098] The server includes a database construction unit that constructs a database of local information based on the received information. The database construction unit, for example, organizes the received information and registers it in a database. The database construction unit also includes an algorithm that eliminates duplicate information and efficiently organizes data. Furthermore, the database construction unit can analyze the relevance of data and create indexes that enable efficient searches. This allows the server to provide the latest local information by constructing a database of local information based on the received information.

[0099] The database construction unit can include an update unit that updates the database in real time. For example, the update unit reflects new information received in the database in real time. The update unit also has the function of checking the consistency of the data and automatically correcting errors. Furthermore, the update unit can compare the data with past data to detect changes and notify the user of necessary information. This allows the database to be updated in real time, ensuring that the latest information is always provided.

[0100] The system is equipped with a privacy protection unit that protects users' privacy. The privacy protection unit, for example, anonymizes data to protect personal information. The privacy protection unit can also set detailed access permissions for data to strengthen security. Furthermore, the privacy protection unit has a function to clarify the purpose of data use and notify the user. This protects users' privacy and allows them to use the system with peace of mind.

[0101] The system includes a communication management unit that manages free communication periods. The communication management unit, for example, sets free communication periods and notifies users. The communication management unit also has the function of monitoring and optimizing communication volume in real time. Furthermore, the communication management unit can dynamically change communication protocols to improve communication efficiency. This allows for management of free communication periods, which can encourage user usage.

[0102] The system includes a reward provision unit that provides rewards in partnership with automobile insurance companies. The reward provision unit provides, for example, insurance premium discounts and rewards based on the user's usage. The reward provision unit can also analyze the user's past usage history and provide optimal rewards. Furthermore, the reward provision unit has a function to customize rewards based on the user's current situation. This makes it possible to encourage user usage by providing rewards in partnership with automobile insurance companies.

[0103] The photographing unit can estimate the user's emotions and adjust the timing of photographing based on the estimated user emotions. The photographing unit, for example, photographs the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is relaxed, photographing can be performed at a normal photographing timing. Also, if the user is nervous, photographing can be performed frequently so as not to miss important moments. Also, if the user is in a hurry, photographing important information can be prioritized. In this way, by adjusting the timing of photographing according to the user's emotions, it is possible to photograph important moments without missing them.

[0104] The camera unit can automatically adjust the camera settings according to the weather and time of day. For example, the camera unit can increase the camera's sensitivity to capture brighter images at night. It can also activate the camera's waterproof function to prevent lens fogging in rainy weather. It can also adjust the camera's exposure to reduce excessive brightness on sunny days. This allows the camera settings to be automatically adjusted according to the weather and time of day to capture optimal footage.

[0105] The imaging unit can dynamically change the imaging range based on the vehicle's speed and location information. For example, when the vehicle is traveling on a highway, the imaging unit captures a wide range. When the vehicle is traveling in an urban area, the imaging unit can also capture a detailed range. When the vehicle is stopped, the imaging unit can also capture detailed information about the surroundings. This allows the imaging range to be dynamically changed based on the vehicle's speed and location information, making it possible to capture an appropriate range.

[0106] The photographing unit can take photographs at high resolution when a specific event is detected. For example, when a traffic accident is detected, the photographing unit takes photographs at a higher resolution than normal. Furthermore, when a construction site is detected, the photographing unit can take photographs at high resolution to record detailed information. Furthermore, when a road opening is detected, the photographing unit can take photographs at high resolution to record details of the new road. In this way, detailed information can be recorded by taking photographs at high resolution when a specific event is detected.

[0107] The image capturing unit can estimate the user's emotions and determine the priority of the images to be captured based on the estimated user emotions. The image capturing unit, for example, captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the image capturing unit can capture images with normal priority. Also, if the user is nervous, the image capturing unit can prioritize capturing important information. Also, if the user is in a hurry, the image capturing unit can capture images so as not to miss important moments. In this way, by determining the priority of the images to be captured according to the user's emotions, important information can be captured with priority.

[0108] The image capturing unit can analyze the sound in the car and capture important information. For example, the image capturing unit can analyze conversations in the car and capture important information. It can also analyze warning sounds in the car and capture important information. It can also analyze music in the car and capture important information. In this way, important information can be captured by analyzing the sound in the car.

[0109] The camera unit can detect the movement of other vehicles and record dangerous situations. For example, if another vehicle suddenly brakes, the camera unit records that movement. It can also record the movement of another vehicle if it suddenly changes lanes. It can also record the movement of another vehicle if it behaves abnormally. In this way, by detecting the movement of other vehicles, dangerous situations can be recorded.

[0110] The camera unit can record the external environment of the vehicle and store it together with the video. For example, the camera unit can record the external temperature when taking a picture and store it together with the video. It can also record the external humidity when taking a picture and store it together with the video. It can also record the external air pressure when taking a picture and store it together with the video. In this way, by recording the external environment of the vehicle, detailed information can be stored together with the video.

[0111] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The analysis unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the analysis can be performed with normal analysis accuracy. Also, if the user is nervous, the analysis accuracy can be increased so as not to miss important information. Also, if the user is in a hurry, the analysis accuracy can be adjusted to perform the analysis quickly. In this way, by adjusting the analysis accuracy according to the user's emotions, it is possible to analyze without missing important information.

[0112] The analysis unit can identify specific objects in the video. For example, the analysis unit can identify pedestrians in the video and analyze their movements. It can also identify bicycles in the video and analyze their movements. It can also identify vehicles in the video and analyze their movements. This allows for detailed analysis by identifying specific objects in the video.

[0113] The analysis unit can dynamically adjust the frame rate of the video so that important information is not missed. For example, the analysis unit can increase the frame rate for scenes containing important information to perform detailed analysis. For normal scenes, the analysis unit can also set the frame rate to a lower value for efficient analysis. Furthermore, when an important event occurs, the frame rate can be dynamically adjusted for analysis. In this way, by dynamically adjusting the frame rate of the video, important information can be analyzed without being missed.

[0114] The analysis unit can detect abnormalities by comparing with past data. For example, the analysis unit can detect abnormal movements by comparing with past data. It can also detect abnormal sounds by comparing with past data. It can also detect abnormal environmental changes by comparing with past data. In this way, it is possible to detect abnormalities by comparing with past data.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, detailed analysis results can be displayed. Also, if the user is nervous, important information can be highlighted and displayed. Also, if the user is in a hurry, analysis results that focus on the main points can be displayed. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided.

[0116] The analysis unit can analyze the audio data and link it to the video. For example, the analysis unit can analyze the audio data in the video and extract important information. It can also link the audio data with the video and display the analysis results. It can also analyze the audio data and identify specific events in the video. In this way, by analyzing the audio data, it is possible to provide detailed information linked to the video.

[0117] The analysis unit can integrate and analyze data from other sensors. For example, the analysis unit can integrate GPS data to analyze vehicle location information. It can also integrate accelerometer data to analyze vehicle movement. It can also integrate data from other sensors to perform comprehensive analysis. This makes it possible to perform comprehensive analysis by integrating data from other sensors.

[0118] The analysis unit can improve the accuracy of analysis by automatically adjusting the color tone and brightness of the image. For example, the analysis unit can improve the accuracy of analysis by automatically adjusting the color tone of the image. It can also improve the accuracy of analysis by automatically adjusting the brightness of the image. It can also improve the accuracy of analysis by automatically adjusting the contrast of the image. In this way, the analysis accuracy is improved by automatically adjusting the color tone and brightness of the image.

[0119] The transmission unit can estimate the user's emotion and adjust the timing of data transmission based on the estimated user emotion. The transmission unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, data can be transmitted at a normal timing. Also, if the user is nervous, important information can be transmitted with priority. Also, if the user is in a hurry, data can be transmitted quickly. In this way, by adjusting the timing of data transmission according to the user's emotion, important information can be transmitted at an appropriate time.

[0120] The transmitting unit can dynamically change the data compression rate to optimize the amount of communication. For example, when the communication environment is good, the transmitting unit can lower the compression rate to transmit high-quality data. When the communication environment is poor, the transmitting unit can also increase the compression rate to reduce the amount of communication. The compression rate can also be dynamically changed depending on the importance of the data. In this way, the amount of communication can be optimized by dynamically changing the data compression rate.

[0121] The transmitter can improve the reliability of data by using a plurality of communication protocols. The transmitter, for example, improves the reliability of data by using a plurality of communication protocols. The transmitter can also improve the reliability of data by ensuring redundancy of the communication protocols. The transmitter can also improve the reliability of data by dynamically changing the selection of the communication protocol. In this way, the reliability of data is improved by using a plurality of communication protocols.

[0122] The transmitting unit can set the priority of data and transmit important information preferentially. The transmitting unit sets the priority of data, for example, to transmit important information preferentially. Also, the transmitting unit can send important information quickly, putting ordinary information on hold. Also, the transmission priority can be dynamically changed depending on the importance of the data. In this way, by setting the priority of data, important information can be sent quickly.

[0123] The transmitting unit can estimate the user's emotion and adjust the content of the transmission data based on the estimated user's emotion. The transmitting unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, normal data can be transmitted. Also, if the user is nervous, important information can be transmitted with priority. Also, if the user is in a hurry, data can be transmitted quickly. In this way, important information can be transmitted with priority by adjusting the content of the transmission data according to the user's emotion.

[0124] The transmitting unit can encrypt data to strengthen security. For example, the transmitting unit can encrypt data when transmitting it to strengthen security. Furthermore, it can perform strong encryption when transmitting important data. It can also perform standard encryption when transmitting ordinary data. In this way, data encryption strengthens security.

[0125] The transmitting unit can select the optimal transmission method depending on the communication environment. For example, the transmitting unit can transmit data at high speed in a Wi-Fi environment. It can also transmit data with reduced communication volume in a 4G environment. It can also transmit large amounts of data at high speed in a 5G environment. This allows efficient data transmission by selecting the optimal transmission method depending on the communication environment.

[0126] The transmitting unit can set multiple destinations for data transmission to ensure backups. The transmitting unit can, for example, set multiple destinations for data transmission to ensure backups. It can also transmit important data to multiple servers to ensure data redundancy. It can also transmit normal data to multiple destinations to improve data reliability. Thus, by setting multiple destinations for data transmission, data redundancy can be ensured.

[0127] The database construction unit can estimate the user's emotions and adjust the database construction method based on the estimated user emotions. The database construction unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, a normal database construction method can be used. Also, if the user is nervous, important information can be registered in the database with priority. Also, if the user is in a hurry, the database can be constructed quickly. In this way, by adjusting the database construction method according to the user's emotions, efficient database construction is possible.

[0128] The database construction unit can eliminate duplicate data and organize data efficiently. For example, the database construction unit automatically detects and eliminates duplicate data when constructing a database. It can also use an efficient data organization algorithm when constructing a database. It can also set rules for eliminating duplicate data when constructing a database. This makes it possible to efficiently organize data by eliminating duplicate data.

[0129] The database construction unit can analyze the relevance of data and enable efficient searches. For example, the database construction unit automatically analyzes the relevance of data when constructing a database. It can also group highly related data when constructing a database. It can also create indexes to enable efficient searches when constructing a database. In this way, analyzing the relevance of data enables efficient searches.

[0130] The database construction unit can automatically back up data. For example, the database construction unit automatically backs up data periodically when constructing a database. Furthermore, when constructing a database, it is also possible to prioritize the backup of important data. It is also possible to set a backup schedule when constructing a database. In this way, the safety of data is ensured by automatically backing up data.

[0131] The database construction unit can estimate the user's emotions and adjust the display method of the database based on the estimated user emotions. The database construction unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, a detailed database display can be provided. Also, if the user is nervous, important information can be highlighted. Also, if the user is in a hurry, a database display that focuses on the main points can be provided. In this way, by adjusting the display method of the database according to the user's emotions, more appropriate information can be provided.

[0132] The database construction unit can integrate different data formats. For example, the database construction unit integrates text data and image data when constructing a database. It can also integrate voice data and text data when constructing a database. It can also integrate different data formats when constructing a database to enable efficient searches. In this way, integrating different data formats enables efficient searches.

[0133] The database construction unit can link with external data sources. For example, the database construction unit may link with public databases to acquire data when constructing the database. The database construction unit may also link with external data sources to integrate data when constructing the database. The database construction unit may also acquire data in real time from external data sources when constructing the database. This allows more information to be acquired by linking with external data sources.

[0134] The database construction unit can set data access permissions and strengthen security. For example, the database construction unit sets data access permissions in detail when constructing the database. It can also set strict access permissions for important data when constructing the database. It can also dynamically change the access permission settings when constructing the database. In this way, security can be strengthened by setting data access permissions.

[0135] The update unit can estimate the user's emotion and adjust the update frequency of the database based on the estimated user's emotion. The update unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the database can be updated at a normal update frequency. Also, if the user is nervous, important information can be updated with priority. Also, if the user is in a hurry, the database can be updated quickly. In this way, by adjusting the update frequency of the database according to the user's emotion, important information can be updated at an appropriate time.

[0136] The update unit can check the consistency of data and automatically correct errors. For example, the update unit automatically checks the consistency of data when updating. It can also automatically correct errors when updating. It can also set rules for checking the consistency of data when updating. This allows the reliability of data to be improved by checking the consistency of data and automatically correcting errors.

[0137] The update unit can detect changes by comparing with past data and notify the user of necessary information. For example, the update unit automatically detects changes by comparing with past data at the time of updating. If changes are detected at the time of updating, the update unit can also notify the user of necessary information. Rules can also be set for detecting changes by comparing with past data at the time of updating. This allows the user to quickly notify the user of necessary information by detecting changes by comparing with past data.

[0138] The update unit performs version management of data and can make past data referable. The update unit, for example, automatically performs version management of data when updating. It can also perform version management to make past data referable when updating. It can also set rules for performing version management of data when updating. In this way, performing version management of data makes past data referable and makes data tracking easier.

[0139] The update unit can estimate the user's emotions and determine the priority of update data based on the estimated user emotions. The update unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the update unit updates data with normal priority. Also, if the user is nervous, the update unit can prioritize important information. Also, if the user is in a hurry, the update unit can quickly update data. In this way, by determining the priority of update data according to the user's emotions, important information can be updated with priority.

[0140] The update unit compresses data and can reduce the amount of communication. For example, the update unit compresses data when updating to reduce the amount of communication. It is also possible to compress important data preferentially when updating. It is also possible to set rules for compressing data when updating. In this way, data compression can reduce the amount of communication.

[0141] The update unit can synchronize data between different devices. For example, the update unit automatically synchronizes data between different devices when updating. It can also prioritize synchronization of important data when updating. It can also set rules for synchronizing data between devices when updating. This allows data consistency to be maintained by synchronizing data between different devices.

[0142] The update unit can record the history of changes to data, making it possible to trace the data. For example, the update unit automatically records the history of changes to data when updating. It can also prioritize recording the history of changes to important data when updating. It can also set rules for recording the history of changes to data when updating. By recording the history of changes to data, it becomes easier to trace the data.

[0143] The privacy protection unit can estimate the user's emotion and adjust the level of privacy protection based on the estimated user's emotion. For example, the privacy protection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, a normal privacy protection level can be applied. Also, if the user is nervous, the privacy protection level can be increased. Also, if the user is in a hurry, privacy protection can be performed quickly. In this way, appropriate privacy protection can be achieved by adjusting the level of privacy protection according to the user's emotion.

[0144] The privacy protection unit can anonymize data to protect personal information. For example, the privacy protection unit automatically anonymizes data when privacy protection is in effect. Furthermore, the privacy protection unit can also prioritize the anonymization of important data when privacy protection is in effect. Furthermore, rules for anonymizing data when privacy protection is in effect can be set. In this way, the protection of personal information is strengthened by anonymizing data.

[0145] The privacy protection unit can set data access permissions in detail. For example, the privacy protection unit sets data access permissions in detail when privacy is being protected. Also, it can set access permissions for important data strictly when privacy is being protected. It can also dynamically change the settings of access permissions when privacy is being protected. In this way, security can be strengthened by setting data access permissions in detail.

[0146] The privacy protection unit can clarify the purpose of data use and notify the user. For example, the privacy protection unit clarifies the purpose of data use when privacy is being protected and notifies the user. It can also prioritize the notification of the purpose of important data use when privacy is being protected. It can also set rules for notifying the purpose of data use when privacy is being protected. In this way, by clarifying the purpose of data use, it is possible to gain the trust of users.

[0147] The privacy protection unit can estimate the user's emotions and adjust the privacy protection notification method based on the estimated user emotions. The privacy protection unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, a normal notification method is used. Also, if the user is nervous, important information can be emphasized and notified. Also, if the user is in a hurry, a quick notification can be sent. In this way, by adjusting the privacy protection notification method according to the user's emotions, appropriate notifications can be provided.

[0148] The privacy protection unit can encrypt data and strengthen security. For example, the privacy protection unit automatically encrypts data when privacy protection is enabled. Furthermore, important data can be encrypted preferentially when privacy protection is enabled. Furthermore, rules for encrypting data when privacy protection is enabled can be set. In this way, data encryption strengthens security.

[0149] The privacy protection unit can restrict the provision of data to third parties. For example, the privacy protection unit automatically restricts the provision of data to third parties when privacy is being protected. Furthermore, when privacy is being protected, the privacy protection unit can also preferentially restrict the provision of important data. Furthermore, when privacy is being protected, rules can be set to restrict the provision of data to third parties. This strengthens the protection of personal information by restricting the provision of data to third parties.

[0150] The privacy protection unit can use data after obtaining the user's consent. For example, the privacy protection unit can use data after obtaining the user's consent when protecting privacy. Furthermore, the privacy protection unit can also prioritize obtaining the user's consent for the use of important data when protecting privacy. Furthermore, rules for obtaining the user's consent when protecting privacy can be set. In this way, the privacy protection unit can gain the user's trust by using data after obtaining the user's consent.

[0151] The communication management unit can estimate the user's emotions and adjust the free communication period based on the estimated user emotions. The communication management unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the normal free communication period can be applied. Also, if the user is nervous, the free communication period can be extended. Also, if the user is in a hurry, the free communication period can be quickly applied. In this way, by adjusting the free communication period according to the user's emotions, it is possible to provide appropriate services.

[0152] The communication management unit can monitor and optimize communication volume in real time. For example, the communication management unit monitors communication volume in real time during communication management. It can also use an algorithm to optimize communication volume during communication management. It can also perform optimization based on the results of monitoring communication volume during communication management. As a result, efficient communication is possible by monitoring and optimizing communication volume in real time.

[0153] The communication management unit can dynamically change the communication protocol to improve communication efficiency. The communication management unit, for example, dynamically changes the communication protocol during communication management. Also, during communication management, it can select a protocol to improve communication efficiency. Also, it can automatically change the communication protocol during communication management. In this way, by dynamically changing the communication protocol, communication efficiency is improved.

[0154] The communication management unit can automatically detect communication errors and perform retransmission. For example, the communication management unit automatically detects communication errors during communication management. Also, if a communication error occurs during communication management, it can automatically perform retransmission. Also, rules for detecting communication errors and performing retransmission can be set during communication management. This automatically detects communication errors and performs retransmission, improving the reliability of communication.

[0155] The communication management unit can estimate the user's emotions and adjust the notification method of communication management based on the estimated user emotions. The communication management unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, a normal notification method is used. Also, if the user is nervous, important information can be emphasized and notified. Also, if the user is in a hurry, a quick notification can be sent. In this way, by adjusting the notification method of communication management according to the user's emotions, appropriate notifications can be provided.

[0156] The communication management unit can select the optimal communication method depending on the communication environment. For example, the communication management unit selects a high-speed communication method in a Wi-Fi environment. It can also select a communication method with reduced communication volume in a 4G environment. It can also select a high-speed, high-capacity communication method in a 5G environment. This enables efficient communication by selecting the optimal communication method depending on the communication environment.

[0157] The communication management unit compresses communication data and can reduce the amount of communication. For example, the communication management unit automatically compresses communication data when managing communication. It can also compress important data preferentially when managing communication. It can also set rules for compressing communication data when managing communication. In this way, it is possible to reduce the amount of communication by compressing communication data.

[0158] The communication management unit can set communication priorities and transmit important data preferentially. The communication management unit, for example, automatically sets communication priorities when managing communications. It can also transmit important data preferentially when managing communications. It can also set rules for setting communication priorities when managing communications. By setting communication priorities, important data can be transmitted quickly.

[0159] The reward provision unit can estimate the user's emotion and adjust the reward content based on the estimated user emotion. For example, the reward provision unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, a regular reward can be provided. Also, if the user is nervous, the reward content can be enhanced. Also, if the user is in a hurry, the reward can be provided quickly. In this way, by adjusting the reward content according to the user's emotion, more appropriate rewards can be provided.

[0160] The reward provision unit can analyze the user's past usage history and provide the optimal reward. For example, the reward provision unit analyzes the user's past usage history when providing a reward and provides the optimal reward. Furthermore, when providing a reward, the reward provision unit can customize the content of the reward based on the user's past usage history. Furthermore, when providing a reward, the reward provision timing can be adjusted based on the user's past usage history. In this way, the optimal reward can be provided by analyzing the user's past usage history.

[0161] The reward provision unit can customize a reward based on the user's current situation. For example, the reward provision unit analyzes the user's driving situation when providing a reward and provides an optimal reward. The reward provision unit can also customize the content of the reward based on the user's current situation when providing a reward. The reward provision unit can also adjust the timing of providing the reward based on the user's driving situation when providing a reward. In this way, by customizing a reward based on the user's current situation, more appropriate rewards can be provided.

[0162] The reward provision unit can improve the reward content by reflecting user feedback. For example, the reward provision unit improves the reward content based on user feedback when providing the reward. The reward provision unit can also adjust the reward provision method by reflecting user feedback when providing the reward. The reward content can also be customized based on user feedback when providing the reward. In this way, the reward content can be improved by reflecting user feedback.

[0163] The reward provision unit can estimate the user's emotion and adjust the timing of providing the reward based on the estimated user's emotion. For example, the reward provision unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the reward can be provided at a normal timing. Also, if the user is nervous, the timing of providing the reward can be advanced. Also, if the user is in a hurry, the reward can be provided quickly. In this way, by adjusting the timing of providing the reward according to the user's emotion, the reward can be provided at a more appropriate timing.

[0164] The reward provision unit can provide an optimal reward by taking into account the user's geographical location information. For example, the reward provision unit provides an optimal reward based on the user's geographical location information when providing a reward. The reward provision unit can also customize the content of the reward based on the user's current location when providing a reward. The reward provision unit can also adjust the timing of providing the reward by taking into account the user's geographical location information when providing a reward. In this way, the optimal reward can be provided by taking into account the user's geographical location information.

[0165] The reward provision unit can analyze the user's social media activity and provide a related reward. For example, the reward provision unit can analyze the user's social media activity when providing a reward and provide a related reward. Furthermore, the reward content can be customized based on the user's social media posts when providing a reward. Furthermore, the timing of providing a reward can be adjusted taking the user's social media activity into consideration when providing a reward. In this way, a related reward can be provided by analyzing the user's social media activity.

[0166] The reward provision unit can customize the content of the reward by reflecting the user's past feedback. For example, the reward provision unit customizes the content of the reward based on the user's past feedback when providing the reward. Furthermore, the reward provision unit can adjust the method of providing the reward by reflecting the user's past feedback when providing the reward. Furthermore, the reward provision timing can be adjusted based on the user's past feedback when providing the reward. In this way, the content of the reward can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, transmission unit, database construction unit, privacy protection unit, communication management unit, and reward provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14 and captures images of the area in front of the vehicle. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured images. The transmission unit is realized by the communication I / F 44 of the smart device 14 and transmits the analyzed information to a server. The database construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a local information database based on the received information. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data anonymization and access permission settings. The communication management unit is realized by the control unit 46A of the smart device 14 and manages the free communication period. The reward provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides rewards through partnerships with automobile insurance companies. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, transmission unit, database construction unit, privacy protection unit, communication management unit, and reward provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214 and captures images of the area in front of the vehicle. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured images. The transmission unit is realized by the communication I / F 44 of the smart glasses 214 and transmits the analyzed information to a server. The database construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a local information database based on the received information. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data anonymization and access permission settings. The communication management unit is realized by the control unit 46A of the smart glasses 214 and manages the free communication period. The reward provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides rewards through partnerships with automobile insurance companies. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, transmission unit, database construction unit, privacy protection unit, communication management unit, and reward provision unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset type terminal 314 and captures images of the area in front of the vehicle. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured images. The transmission unit is realized by the communication I / F 44 of the headset type terminal 314 and transmits the analyzed information to a server. The database construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a local information database based on the received information. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data anonymization and access permission settings. The communication management unit is realized by the control unit 46A of the headset type terminal 314 and manages the free communication fee period. The reward provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides rewards through partnerships with automobile insurance companies. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, transmission unit, database construction unit, privacy protection unit, communication management unit, and reward provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414 and captures images of the area in front of the vehicle. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured images. The transmission unit is realized by the communication I / F 44 of the robot 414 and transmits the analyzed information to a server. The database construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a local information database based on the received information. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data anonymization and access permission settings. The communication management unit is realized by the control unit 46A of the robot 414 and manages the free communication fee period. The reward provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides rewards through partnerships with automobile insurance companies.

[0167] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0168] The analysis unit can learn the user's driving style and adjust the accuracy of analysis based on the driving style. For example, if the user frequently brakes suddenly, the analysis unit can take that movement into account to improve the accuracy of traffic accident detection. Also, if the user frequently uses expressways, the analysis unit can perform analysis specialized for detecting events on expressways. Furthermore, if the user often drives at night, the accuracy of nighttime video analysis can be improved. This makes it possible to adjust the analysis accuracy according to the user's driving style, and more accurate information can be provided.

[0169] The transmitter takes into account the user's driving conditions when transmitting data, and can minimize data transmission while driving. For example, when the user is driving on a highway, data transmission can be temporarily stopped and transmitted all at once when the user arrives at a safe location. In addition, when the user is stuck in traffic, data can be compressed and transmitted to reduce communication volume. Furthermore, when the user is parked, normal data transmission can be performed. This makes it possible to optimize data transmission according to the driving conditions, achieving safe and efficient data transmission.

[0170] The database construction unit can estimate the user's emotions and adjust the database update frequency based on the estimated user's emotions. For example, if the user is relaxed, the database can be updated at a normal update frequency. Also, if the user is nervous, important information can be updated with priority. Furthermore, if the user is in a hurry, the database can be updated quickly. This makes it possible to adjust the database update frequency according to the user's emotions, and provide important information at the appropriate time.

[0171] The photographing unit can estimate the user's emotions and adjust the timing of photographing based on the estimated user's emotions. For example, if the user is relaxed, photographing can be performed at the normal photographing timing. If the user is nervous, photographing can be performed frequently so as not to miss important moments. Furthermore, if the user is in a hurry, photographing important information can be prioritized. This makes it possible to adjust the photographing timing according to the user's emotions, and to photograph important moments without missing them.

[0172] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be displayed. If the user is nervous, important information can be highlighted. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed. This makes it possible to adjust the display method of the analysis results according to the user's emotions, and provide more appropriate information.

[0173] The transmitting unit can set multiple destinations for data transmission to ensure backups. For example, multiple destinations for data transmission can be set to ensure backups. Important data can also be sent to multiple servers to ensure data redundancy. Furthermore, regular data can be sent to multiple destinations to improve data reliability. Thus, by setting multiple destinations for data transmission, data redundancy can be ensured.

[0174] The analysis unit can dynamically adjust the frame rate of the video to ensure that important information is not missed. For example, in scenes containing important information, the frame rate can be increased for detailed analysis. In addition, in normal scenes, the frame rate can be set lower for efficient analysis. Furthermore, when an important event occurs, the frame rate can be dynamically adjusted for analysis. This allows for dynamic adjustment of the video frame rate, making it possible to analyze without missing any important information.

[0175] The database construction unit can integrate different data formats. For example, text data and image data can be integrated when building a database. Voice data and text data can also be integrated when building a database. Furthermore, different data formats can be integrated when building a database to enable efficient searches. In this way, integrating different data formats enables efficient searches.

[0176] The communication management unit can automatically detect communication errors and perform retransmission. For example, it can automatically detect communication errors during communication management. It can also automatically perform retransmission if a communication error occurs during communication management. Furthermore, it can also set rules for detecting communication errors and performing retransmission during communication management. This allows for automatic detection of communication errors and retransmission, improving the reliability of communication.

[0177] The reward provision unit can estimate the user's emotions and adjust the reward content based on the estimated user's emotions. For example, if the user is relaxed, a normal reward can be provided. If the user is nervous, the reward content can be enhanced. Furthermore, if the user is in a hurry, the reward can be provided quickly. This makes it possible to adjust the reward content according to the user's emotions, and to provide more appropriate rewards.

[0178] The processing flow of the second embodiment will be briefly explained below.

[0179] Step 1: The camera unit captures the area in front of the vehicle. The camera unit uses a high-resolution camera to clearly capture important information such as road signs and store signs. It also has adjustment functions to capture high-quality images even at night or in bad weather. Step 2: The analysis unit analyzes the video captured by the camera unit and detects specific information. The analysis unit uses image recognition technology to detect store signs and road signs, and uses AI to automatically identify events such as traffic accidents and construction work. Step 3: The transmitter sends the information detected by the analyzer to the server. The transmitter uses a communication protocol to send the data to the server, selecting the optimal transmission method depending on the communication environment to ensure the reliability of the data.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0194] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0210] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0217] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0227] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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."

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] [Explanation of symbols]

[0252] 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 camera unit that takes pictures of the front of the car, an analysis unit that analyzes the video captured by the imaging unit and detects specific information; a transmitting unit that transmits the information detected by the analyzing unit to a server. A system characterized by:

2. The analysis unit Detecting store signs and road signs using image recognition technology 2. The system of claim 1.

3. The transmission unit Send data to the server using a communication protocol 2. The system of claim 1.

4. The server A database construction unit is provided that constructs a database of local information based on the received information.

2. The system of claim 1.

5. The database construction unit Equipped with an update unit that updates the database in real time 5. The system of claim 4.

6. The system comprises: Equipped with a privacy protection unit that protects user privacy 2. The system of claim 1.

7. The system comprises: Equipped with a communications management unit that manages free communication periods 2. The system of claim 1.

8. The system comprises: Has a special offer department that offers special offers in partnership with automobile insurance companies 2. The system of claim 1.

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