system

Abstract

The system according to this embodiment aims to efficiently collect and provide gasoline price information based on user submissions. [Solution] The system according to the embodiment comprises a reception unit, a reading unit, a registration unit, and a provision unit. The reception unit receives submissions of photos and GPS information from users. The reading unit reads the gasoline price from the photos received by the reception unit. The registration unit registers the gasoline price information and GPS information read by the reading unit into a database. The provision unit provides the gasoline price information based on the information registered by the registration unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently collect and share gasoline price information.

[0005] The system according to the embodiment aims to efficiently collect and provide gasoline price information based on posts from users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a reading unit, a registration unit, and a provision unit. The reception unit receives submissions of photos and GPS information from users. The reading unit reads the gasoline price from the photos received by the reception unit. The registration unit registers the gasoline price information and GPS information read by the reading unit into a database. The provision unit provides the gasoline price information based on the information registered by the registration unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and provide gasoline price information based on user submissions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The gasoline price information sharing system according to an embodiment of the present invention is a user-participatory system that allows users to share gasoline price information by posting photos and GPS information, and to create a map. The gasoline price information sharing system allows users to post photos of gas stations and GPS information, enabling other users to check gasoline price information in real time. For example, a user takes a photo of a gas station's price display and posts it to the app along with GPS information. Next, a generating AI reads the gasoline price from the posted photo and registers it in a database along with the GPS information. This allows other users to check gasoline price information in real time through the app. For example, when a user opens the app, price information for gas stations around their current location is displayed on the map. Furthermore, contributors are offered rewards, and viewers can use the service by paying a monthly membership fee. For example, contributors are paid rewards through an electronic payment system, and viewers can view gasoline price information by paying a monthly membership fee of 200 yen. This mechanism allows users to check gasoline price information in real time and respond to fluctuations in gasoline prices. Additionally, contributors receive rewards, creating a win-win situation for both information providers and recipients. For example, by finding gas stations with low prices, users can reduce costs, and those who post the information can earn rewards. This service is extremely useful for users who are sensitive to gasoline prices, such as those who drive cars or motorcycles, or those who use kerosene stoves in winter. For instance, by checking the app before going out, users can find the cheapest gas station and refuel efficiently. In this way, the gasoline price information sharing system allows users to check gasoline prices in real time and respond to fluctuations in gasoline prices.

[0029] The gasoline price information sharing system according to this embodiment comprises a reception unit, a reading unit, a registration unit, and a provision unit. The reception unit receives submissions of photos and GPS information from users. Users, for example, take photos of price display boards at gas stations using their smartphones and submit them along with GPS information. The reception unit can accept photos in image formats such as JPEG or PNG. The reading unit uses a generation AI to read gasoline prices from photos received by the reception unit. The reading unit extracts price information from photos using image recognition technology, for example. For example, the generation AI uses OCR technology to extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from photos. The registration unit registers the gasoline price information and GPS information read by the reading unit into a database. The registration unit can register information in a database such as a relational database or a NoSQL database. The provision unit provides gasoline price information based on the information registered by the registration unit. The provision unit enables users to check gasoline price information in real time through an app, for example. For example, the provision unit displays price information for gas stations near the user's current location on a map. This allows the gasoline price information sharing system according to the embodiment to provide gasoline price information based on photos posted by the user and GPS information. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can provide information using an AI model that takes information registered by the registration unit as input and outputs gasoline price information.

[0030] The reception desk accepts photos and GPS information submissions from users. For example, a user might use their smartphone to take a photo of a gas station price display and submit it along with GPS information. The reception desk can accept photos in various image formats, such as JPEG and PNG. Specifically, when a user launches a dedicated smartphone app and takes a photo of a gas station price display, the app automatically acquires GPS information and sends this information to the server in bulk. The reception desk receives these submissions in real time and performs initial checks to verify data integrity. For example, it checks image resolution and GPS accuracy, and has a function to filter out inappropriate submissions and incomplete data. Furthermore, the reception desk manages the user's submission history and can detect abnormal data by comparing it with past submission data. This allows the reception desk to efficiently collect reliable data and improve the overall accuracy of the system. The reception desk also provides feedback to users on their submissions and notifies them whether their submission has been successfully received. This allows users to confirm that their submissions are reflected in the system, helping them maintain their motivation to submit.

[0031] The reading unit uses a generation AI to read gasoline prices from photos received by the reception unit. The reading unit extracts price information from photos using, for example, image recognition technology. Specifically, the generation AI uses OCR technology to extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from photos. The generation AI first preprocesses the image, improving image quality by removing noise and adjusting contrast. Then, it uses an OCR engine to perform character recognition and extract the price information as text data. Furthermore, the generation AI analyzes the extracted text data and performs a verification process to confirm the accuracy of the price information. For example, it checks whether the price information is numerically valid and falls within a specific range. In addition, the generation AI can extract accurate price information by combining multiple algorithms even when multiple price signs are visible or when price displays are partially hidden. This allows the reading unit to extract gasoline price information from user-submitted photos with high accuracy, improving the overall reliability of the system. Furthermore, the reading unit is equipped with an anomaly detection algorithm to compare the extracted price information with other data and detect abnormal or incorrect information. This allows the reading unit to always provide accurate and reliable price information.

[0032] The registration unit registers the gasoline price information and GPS information read by the reading unit into the database. The registration unit can register information in databases such as relational databases and NoSQL databases. Specifically, the read price information and GPS information are stored in the database as a single record, and each record also includes metadata such as the posting date and time and user ID. This makes it easy to search and filter data, allowing users to quickly obtain the information they need. Furthermore, the registration unit has a transaction management function to maintain data integrity, ensuring data consistency even if errors occur during data registration. The registration unit also implements a normalization process to eliminate data redundancy, supporting efficient database operation. As a result, the registration unit can efficiently manage large amounts of data and improve the overall system performance. In addition, the registration unit has a data backup function, preventing data loss by performing regular data backups. As a result, the registration unit can achieve highly reliable data management and improve the overall system stability.

[0033] The service provider provides gasoline price information based on the information registered by the registration service provider. For example, the service provider allows users to check gasoline price information in real time through an app. Specifically, the service provider has a function to display price information of gas stations around the user's current location on a map. Users can launch the app and easily check price information of gas stations based on their current location. The service provider can prioritize displaying the cheapest gas station or the nearest gas station according to the user's search criteria. The service provider also has a personalization function that displays individual recommended gas stations based on the user's past search history. Furthermore, the service provider can provide information using an AI model that takes the information registered by the registration service provider as input and outputs gasoline price information. For example, the AI ​​model can analyze past price data and trends and predict future price fluctuations. This allows users to predict future price trends and refuel at the optimal time. In addition, the service provider can collect feedback from users and continuously improve the accuracy and reliability of the information it provides. This allows the service provider to always provide users with the latest and most accurate gasoline price information and improve user convenience.

[0034] The reading unit can extract price information from a photograph using image recognition technology. For example, the reading unit can extract price information from a photograph using OCR technology. For example, the reading unit can extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from a photograph. The reading unit can also extract price information from a photograph using deep learning technology. For example, the reading unit can extract price information from a photograph with high accuracy using a deep learning model. This allows for accurate extraction of price information from a photograph using image recognition technology. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input photographic data into a generating AI and have the generating AI perform the extraction of price information.

[0035] The service provider can enable users to check gasoline price information in real time through the app. For example, when a user opens the app, the service provider can display price information for gas stations near the user's current location on a map. For example, the service provider can identify the user's current location based on GPS information and display price information for gas stations in that area. The service provider can also display price information for gas stations in a specific area when the user searches for that area. For example, when a user selects a specific area on the map, the service provider can display price information for gas stations in that area. This allows users to check gasoline price information in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input GPS information and gasoline price information into a generating AI and provide the information in real time.

[0036] The reception system allows users to take photos of gas station price boards using their smartphones and post them along with GPS information. For example, the reception system accepts photos and GPS information taken by users, such as when a user takes a photo of a gas station price board using their smartphone camera and posts it through an app. The reception system also accepts GPS information entered by users. For example, the reception system accepts information when a user manually enters their current location. This allows users to easily post gasoline price information using their smartphones. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the photos and GPS information posted by users into a generating AI and have the generating AI perform the information reception.

[0037] The registration unit can register the extracted price information and GPS information into a database. For example, the registration unit can register the price information and GPS information extracted by the reading unit into a relational database. For example, the registration unit inserts the price information and GPS information into a table. The registration unit can also register information into a NoSQL database. For example, the registration unit saves the price information and GPS information in document format. This ensures that the extracted price information and GPS information is accurately registered in the database. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the extracted price information and GPS information into a generating AI and have the generating AI perform the registration to the database.

[0038] The service provider can display price information for gas stations near the user's current location on a map. For example, when a user opens the app, the service provider can identify the user's current location based on GPS information and display price information for nearby gas stations on the map. For example, the service provider can use an API to display gas station price information on the map. The service provider can also display price information for gas stations in a specific area when the user searches for that area. For example, when a user selects a specific area on the map, the service provider can display price information for gas stations in that area. This allows the user to check gas prices near their current location on the map. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input GPS information and gas price information into a generating AI and display the information on the map.

[0039] The service provider can offer rewards to contributors and allow viewers to use the service by paying a monthly membership fee. For example, the service provider can pay rewards to contributors through an electronic payment system. For example, the service provider can offer points or gift cards to contributors. The service provider can also allow viewers to use the service by having them pay a monthly membership fee. For example, the service provider can have viewers pay a monthly membership fee of 200 yen by credit card or bank transfer. In this way, the service provider can offer rewards to contributors and allow viewers to use the service by paying a monthly membership fee. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the calculation of contributor rewards and the management of viewers' membership fees.

[0040] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, the reception desk can automatically display gas stations that the user has frequently posted about in the past as candidates. For example, the reception desk can prioritize displaying gas stations that have been frequently posted about based on the user's past posting history. The reception desk can also prioritize suggesting posting methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input to post in the past, it will prioritize suggesting voice input. The reception desk can also predict and suggest gas stations that the user will use at specific times of day based on their past posting history. For example, the reception desk can predict which gas stations the user frequently posts about at specific times of day and prioritize displaying them at those times. This allows the reception desk to select the most suitable reception method based on the user's past posting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past posting history data into a generating AI and have the generating AI select the most suitable reception method.

[0041] The reception unit can filter posts based on the user's current location and areas of interest when a post is received. For example, when a user opens the app, the reception unit can automatically obtain the user's current location and prioritize displaying nearby gas stations. For example, the reception unit can identify the user's current location based on GPS information and display gas stations in the vicinity. The reception unit can also filter posts based on the user's areas of interest (for example, a specific brand of gas station). For example, if a user is interested in a specific brand of gas station, the reception unit will prioritize displaying gas stations of that brand. Furthermore, if the user is using the app while on the move, the reception unit can update the user's current location in real time and display the most suitable post candidates. For example, when a user is using the app while on the move, the reception unit updates the user's current location in real time and displays the most suitable post candidates. This allows for filtering posts based on the user's current location and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's location information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0042] The reception system can prioritize accepting posts that are highly relevant, taking into account the user's geographical location information. For example, the reception system can prioritize accepting posts about gas stations near the user's current location. For example, the reception system can identify the user's current location based on GPS information and prioritize accepting posts about gas stations in that area. The reception system can also prioritize accepting posts about a specific region if the user is interested in that region. For example, if the user is interested in a specific region, the reception system can prioritize accepting posts about gas stations in that region. Furthermore, if the user is using the app while on the move, the reception system can update the user's current location in real time and prioritize accepting posts that are highly relevant. For example, when the user is using the app while on the move, the reception system can update the user's current location in real time and prioritize accepting posts that are highly relevant. This allows for the priority acceptance of posts that are highly relevant based on the user's geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception desk can input user location data into a generation AI and have the AI ​​select highly relevant posts.

[0043] The reception unit can analyze a user's social media activity when receiving a submission and accept relevant submissions. For example, the reception unit can prioritize accepting submissions of gas stations that the user frequently mentions on social media. The reception unit can also predict which gas stations the user is interested in based on their social media activity and prioritize accepting submissions of those stations. The reception unit can also prioritize accepting submissions of gas stations that the user follows on social media. This allows the reception unit to accept relevant submissions based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant submissions.

[0044] The reading unit can optimize its reading algorithm based on image quality during reading. For example, in the case of a high-resolution image, the reading unit can use an algorithm that extracts detailed information. For example, the reading unit can take a high-resolution image as input and apply an algorithm that extracts detailed information. The reading unit can also use an algorithm that removes noise and extracts necessary information in the case of a low-resolution image. For example, the reading unit can take a low-resolution image as input and apply an algorithm that performs noise reduction and information extraction. The reading unit can also use an algorithm that adjusts brightness to improve reading accuracy if the image is dark. For example, the reading unit can take a dark image as input and apply an algorithm that adjusts brightness and extracts information. This allows the reading algorithm to be optimized based on image quality. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input image data into a generating AI and have the generating AI optimize the algorithm based on the image quality.

[0045] The reading unit can apply different reading methods depending on the type and location of the gas station. For example, in the case of a large chain gas station, the reading unit can use a method to read standardized price signs. For example, the reading unit can take a standardized price sign from a large chain gas station as input and apply a method to extract the information. The reading unit can also use a method to read handwritten price signs in the case of a small local gas station. For example, the reading unit can take a handwritten price sign from a small local gas station as input and apply a method to extract the information. Furthermore, in the case of an urban gas station, the reading unit can use a rapid reading method to avoid congestion. For example, the reading unit can take a price sign from an urban gas station as input and apply a method to quickly extract the information. This allows different reading methods to be applied depending on the type and location of the gas station. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the type and location information of the gas station into a generating AI and have the generating AI select the optimal reading method.

[0046] The reading unit can determine the reading priority based on the time the posted image was taken. For example, the reading unit can prioritize reading the most recent image to provide real-time information. For example, the reading unit can take the most recent image as input and extract information preferentially. The reading unit can also postpone reading older images and prioritize the latest information. For example, the reading unit can take older images as input and prioritize extracting the latest information. The reading unit can also prioritize reading images taken during a specific time period to understand price fluctuations for each time period. For example, the reading unit can take images taken during a specific time period as input and extract information preferentially. This allows the reading priority to be determined based on the time the posted image was taken. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the image shooting time data into a generating AI and have the generating AI perform the priority determination.

[0047] The reading unit can improve reading accuracy by referring to relevant information about the image during reading. For example, the reading unit can improve reading accuracy by referring to image metadata (such as the date and time of shooting, location information, etc.). For example, the reading unit can take image metadata as input and refer to it when extracting information. The reading unit can also improve reading accuracy by referring to other text information within the image (for example, the name of a gas station). For example, the reading unit can take other text information within the image as input and refer to it when extracting information. The reading unit can also improve reading accuracy by referring to background information of the image (for example, weather and lighting conditions). For example, the reading unit can take background information of the image as input and refer to it when extracting information. This improves reading accuracy by referring to relevant information about the image. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input relevant information about the image to a generating AI and have the generating AI perform the improvement of reading accuracy.

[0048] The registration unit can optimize the registration algorithm by referring to past registration data during registration. For example, the registration unit can select the optimal registration algorithm based on past registration data. For example, the registration unit can analyze past registration data and select the optimal algorithm. The registration unit can also select an algorithm with fewer errors from past registration data. For example, the registration unit can select an algorithm with fewer errors based on past registration data. The registration unit can also analyze past registration data and select the most efficient registration algorithm. For example, the registration unit can select the most efficient algorithm based on past registration data. This allows the registration algorithm to be optimized by referring to past registration data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input past registration data into a generating AI and have the generating AI perform algorithm optimization.

[0049] The registration unit can adjust the database structure considering gasoline price fluctuation patterns during registration. For example, the registration unit can optimize the database index based on gasoline price fluctuation patterns. For example, the registration unit can analyze gasoline price fluctuation patterns and optimize the index. The registration unit can also analyze gasoline price fluctuation patterns and adjust the database table structure. For example, the registration unit can adjust the table structure based on gasoline price fluctuation patterns. The registration unit can also optimize the database caching strategy considering gasoline price fluctuation patterns. For example, the registration unit can optimize the caching strategy based on gasoline price fluctuation patterns. This allows the database structure to be optimized considering gasoline price fluctuation patterns. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input gasoline price fluctuation pattern data into a generating AI and have the generating AI perform the adjustment of the database structure.

[0050] The registration unit can perform registration while considering the geographical distribution of the submitted data. For example, the registration unit can select the optimal registration method based on the geographical distribution of the submitted data. For example, the registration unit can analyze the geographical distribution of the submitted data and select the optimal registration method. The registration unit can also select a registration method to evenly distribute geographically biased data. For example, the registration unit can select a registration method to evenly distribute geographically biased data. The registration unit can also optimize the database index while considering the geographical distribution. For example, the registration unit optimizes the database index based on the geographical distribution. This allows for optimized registration while considering the geographical distribution of the submitted data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input geographical distribution data of the submitted data into a generating AI and have the generating AI select the registration method.

[0051] The registration unit can improve the accuracy of registration by referring to relevant market data during registration. For example, the registration unit can select the optimal registration method based on market data. For example, the registration unit can analyze market data and select the optimal registration method. The registration unit can also select a registration method with fewer errors by referring to market data. For example, the registration unit can select a registration method with fewer errors based on market data. The registration unit can also analyze market data and select the most efficient registration method. For example, the registration unit can select the most efficient registration method based on market data. This improves the accuracy of registration by referring to relevant market data. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input market data into a generating AI and have the generating AI perform the selection of a registration method.

[0052] The information provider can provide optimal information by referring to the user's past browsing history at the time of delivery. For example, the information provider can prioritize displaying information about gas stations that the user has previously viewed. For example, the information provider can prioritize displaying information about gas stations that the user has frequently viewed based on the user's past browsing history. The information provider can also predict and provide information of interest to the user based on the user's past browsing history. For example, the information provider can analyze the user's past browsing history, predict and provide information of interest. The information provider can also provide optimal information based on information that the user has frequently viewed in the past. For example, the information provider can provide optimal information based on the user's past browsing history. This allows the information provider to provide optimal information based on the user's past browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past browsing history data into a generating AI and have the generating AI select the optimal information.

[0053] The service provider can customize information at the time of delivery, taking into account predictions of gasoline price fluctuations. For example, the service provider can suggest the optimal refueling timing based on predictions of gasoline price fluctuations. For example, the service provider can analyze predictions of gasoline price fluctuations and suggest the optimal refueling timing. The service provider can also suggest the cheapest gas station, taking into account predictions of gasoline price fluctuations. For example, the service provider can suggest the cheapest gas station based on predictions of gasoline price fluctuations. The service provider can also customize and provide information tailored to the user based on predictions of gasoline price fluctuations. For example, the service provider can customize and provide information tailored to the user based on predictions of gasoline price fluctuations. This allows for the customization of information based on predictions of gasoline price fluctuations. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input prediction data of gasoline price fluctuations into a generating AI and have the generating AI perform the customization of the information.

[0054] The service provider can provide optimal information by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing information on gas stations near the user's current location. For example, the service provider can identify the user's current location based on GPS information and prioritize providing information on gas stations in that area. The service provider can also prioritize providing information on a specific region if the user is interested in that region. For example, if the service provider is interested in a specific region, it can prioritize providing information on gas stations in that region. Furthermore, if the user is using the app while on the move, the service provider can update the user's current location in real time and provide optimal information. For example, when the user is using the app while on the move, the service provider updates the user's current location in real time and provides optimal information. This allows the service provider to provide optimal information based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select the optimal information.

[0055] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing information about gas stations that the user frequently mentions on social media. For example, the service provider can prioritize providing information about gas stations that are frequently mentioned based on the user's social media activity. The service provider can also predict and provide information of interest based on the user's social media activity. For example, the service provider can analyze the user's social media activity, predict and provide information of interest. The service provider can also prioritize providing information about gas stations that the user follows on social media. For example, the service provider can prioritize providing information about gas stations that the user follows. This allows the service provider to provide relevant information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The gasoline price information sharing system can further analyze the user's past browsing history and provide optimal information. For example, the information provider can prioritize displaying information about gas stations that the user has frequently viewed in the past. It can also predict and provide information of interest based on the user's past browsing history. Furthermore, it can provide information that is optimal for a given time period based on the information the user frequently views during that time. This allows the system to provide optimal information based on the user's past browsing history. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past browsing history data into a generating AI and have the generating AI select the optimal information.

[0058] The gasoline price information sharing system can further provide optimal information by taking into account the user's geographical location. For example, the information provider can prioritize providing information on gas stations near the user's current location. It can also prioritize providing information on a specific region if the user is interested in that region. Furthermore, if the user is using the app while on the move, the system can update the user's current location in real time to provide optimal information. This allows the system to provide optimal information based on the user's geographical location. Some or all of the above processing in the information provider can be performed using AI or not. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal information.

[0059] The gasoline price information sharing system can further analyze users' social media activity and provide relevant information. For example, the information provider can prioritize providing information on gas stations that users frequently mention on social media. It can also predict and provide information of interest based on users' social media activity. Furthermore, it can prioritize providing information on gas stations that users follow on social media. This allows the system to provide relevant information based on users' social media activity. Some or all of the above processing in the information provider can be performed using AI or not. For example, the information provider can input users' social media data into a generating AI and have the generating AI select relevant information.

[0060] The gasoline price information sharing system can further analyze a user's past posting history and select the optimal reception method. For example, the reception unit can automatically display gas stations that the user has frequently posted about in the past as candidates. It can also prioritize suggesting posting methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest gas stations to be used at specific times based on the user's past posting history. This allows for the selection of the optimal reception method based on the user's past posting history. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal reception method.

[0061] The gasoline price information sharing system can also prioritize receiving posts that are highly relevant, taking into account the user's geographical location. For example, the receiving unit can prioritize receiving posts about gas stations near the user's current location. Furthermore, if the user is interested in a specific region, posts about that region can be prioritized. Additionally, if the user is using the app while on the move, their current location can be updated in real time, and highly relevant posts can be prioritized. This allows for the prioritization of highly relevant posts based on the user's geographical location. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can input the user's location data into a generating AI and have the generating AI select highly relevant posts.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk accepts photos and GPS information submissions from users. Users take photos of gas station price displays using their smartphones and submit them along with GPS information. The reception desk can accept photos in image formats such as JPEG and PNG. Step 2: The reading unit uses generation AI to read the gasoline price from the photo received by the reception unit. The reading unit uses image recognition technology to extract price information from the photo. For example, it uses OCR technology to extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from the photo. Step 3: The registration unit registers the gasoline price information and GPS information read by the reading unit into the database. The registration unit can register information into databases such as relational databases and NoSQL databases. Step 4: The service provider provides gasoline price information based on the information registered by the registration provider. The service provider enables users to check gasoline price information in real time through the app. For example, it displays price information for gas stations near the user's current location on a map.

[0064] (Example of form 2) The gasoline price information sharing system according to an embodiment of the present invention is a user-participatory system that allows users to share gasoline price information by posting photos and GPS information, and to create a map. The gasoline price information sharing system allows users to post photos of gas stations and GPS information, enabling other users to check gasoline price information in real time. For example, a user takes a photo of a gas station's price display and posts it to the app along with GPS information. Next, a generating AI reads the gasoline price from the posted photo and registers it in a database along with the GPS information. This allows other users to check gasoline price information in real time through the app. For example, when a user opens the app, price information for gas stations around their current location is displayed on the map. Furthermore, contributors are offered rewards, and viewers can use the service by paying a monthly membership fee. For example, contributors are paid rewards through an electronic payment system, and viewers can view gasoline price information by paying a monthly membership fee of 200 yen. This mechanism allows users to check gasoline price information in real time and respond to fluctuations in gasoline prices. Additionally, contributors receive rewards, creating a win-win situation for both information providers and recipients. For example, by finding gas stations with low prices, users can reduce costs, and those who post the information can earn rewards. This service is extremely useful for users who are sensitive to gasoline prices, such as those who drive cars or motorcycles, or those who use kerosene stoves in winter. For instance, by checking the app before going out, users can find the cheapest gas station and refuel efficiently. In this way, the gasoline price information sharing system allows users to check gasoline prices in real time and respond to fluctuations in gasoline prices.

[0065] The gasoline price information sharing system according to this embodiment comprises a reception unit, a reading unit, a registration unit, and a provision unit. The reception unit receives submissions of photos and GPS information from users. Users, for example, take photos of price display boards at gas stations using their smartphones and submit them along with GPS information. The reception unit can accept photos in image formats such as JPEG or PNG. The reading unit uses a generation AI to read gasoline prices from photos received by the reception unit. The reading unit extracts price information from photos using image recognition technology, for example. For example, the generation AI uses OCR technology to extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from photos. The registration unit registers the gasoline price information and GPS information read by the reading unit into a database. The registration unit can register information in a database such as a relational database or a NoSQL database. The provision unit provides gasoline price information based on the information registered by the registration unit. The provision unit enables users to check gasoline price information in real time through an app, for example. For example, the provision unit displays price information for gas stations near the user's current location on a map. This allows the gasoline price information sharing system according to the embodiment to provide gasoline price information based on photos posted by the user and GPS information. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can provide information using an AI model that takes information registered by the registration unit as input and outputs gasoline price information.

[0066] The reception desk accepts photos and GPS information submissions from users. For example, a user might use their smartphone to take a photo of a gas station price display and submit it along with GPS information. The reception desk can accept photos in various image formats, such as JPEG and PNG. Specifically, when a user launches a dedicated smartphone app and takes a photo of a gas station price display, the app automatically acquires GPS information and sends this information to the server in bulk. The reception desk receives these submissions in real time and performs initial checks to verify data integrity. For example, it checks image resolution and GPS accuracy, and has a function to filter out inappropriate submissions and incomplete data. Furthermore, the reception desk manages the user's submission history and can detect abnormal data by comparing it with past submission data. This allows the reception desk to efficiently collect reliable data and improve the overall accuracy of the system. The reception desk also provides feedback to users on their submissions and notifies them whether their submission has been successfully received. This allows users to confirm that their submissions are reflected in the system, helping them maintain their motivation to submit.

[0067] The reading unit uses a generation AI to read gasoline prices from photos received by the reception unit. The reading unit extracts price information from photos using, for example, image recognition technology. Specifically, the generation AI uses OCR technology to extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from photos. The generation AI first preprocesses the image, improving image quality by removing noise and adjusting contrast. Then, it uses an OCR engine to perform character recognition and extract the price information as text data. Furthermore, the generation AI analyzes the extracted text data and performs a verification process to confirm the accuracy of the price information. For example, it checks whether the price information is numerically valid and falls within a specific range. In addition, the generation AI can extract accurate price information by combining multiple algorithms even when multiple price signs are visible or when price displays are partially hidden. This allows the reading unit to extract gasoline price information from user-submitted photos with high accuracy, improving the overall reliability of the system. Furthermore, the reading unit is equipped with an anomaly detection algorithm to compare the extracted price information with other data and detect abnormal or incorrect information. This allows the reading unit to always provide accurate and reliable price information.

[0068] The registration unit registers the gasoline price information and GPS information read by the reading unit into the database. The registration unit can register information in databases such as relational databases and NoSQL databases. Specifically, the read price information and GPS information are stored in the database as a single record, and each record also includes metadata such as the posting date and time and user ID. This makes it easy to search and filter data, allowing users to quickly obtain the information they need. Furthermore, the registration unit has a transaction management function to maintain data integrity, ensuring data consistency even if errors occur during data registration. The registration unit also implements a normalization process to eliminate data redundancy, supporting efficient database operation. As a result, the registration unit can efficiently manage large amounts of data and improve the overall system performance. In addition, the registration unit has a data backup function, preventing data loss by performing regular data backups. As a result, the registration unit can achieve highly reliable data management and improve the overall system stability.

[0069] The service provider provides gasoline price information based on the information registered by the registration service provider. For example, the service provider allows users to check gasoline price information in real time through an app. Specifically, the service provider has a function to display price information of gas stations around the user's current location on a map. Users can launch the app and easily check price information of gas stations based on their current location. The service provider can prioritize displaying the cheapest gas station or the nearest gas station according to the user's search criteria. The service provider also has a personalization function that displays individual recommended gas stations based on the user's past search history. Furthermore, the service provider can provide information using an AI model that takes the information registered by the registration service provider as input and outputs gasoline price information. For example, the AI ​​model can analyze past price data and trends and predict future price fluctuations. This allows users to predict future price trends and refuel at the optimal time. In addition, the service provider can collect feedback from users and continuously improve the accuracy and reliability of the information it provides. This allows the service provider to always provide users with the latest and most accurate gasoline price information and improve user convenience.

[0070] The reading unit can extract price information from a photograph using image recognition technology. For example, the reading unit can extract price information from a photograph using OCR technology. For example, the reading unit can extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from a photograph. The reading unit can also extract price information from a photograph using deep learning technology. For example, the reading unit can extract price information from a photograph with high accuracy using a deep learning model. This allows for accurate extraction of price information from a photograph using image recognition technology. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input photographic data into a generating AI and have the generating AI perform the extraction of price information.

[0071] The service provider can enable users to check gasoline price information in real time through the app. For example, when a user opens the app, the service provider can display price information for gas stations near the user's current location on a map. For example, the service provider can identify the user's current location based on GPS information and display price information for gas stations in that area. The service provider can also display price information for gas stations in a specific area when the user searches for that area. For example, when a user selects a specific area on the map, the service provider can display price information for gas stations in that area. This allows users to check gasoline price information in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input GPS information and gasoline price information into a generating AI and provide the information in real time.

[0072] The reception system allows users to take photos of gas station price boards using their smartphones and post them along with GPS information. For example, the reception system accepts photos and GPS information taken by users, such as when a user takes a photo of a gas station price board using their smartphone camera and posts it through an app. The reception system also accepts GPS information entered by users. For example, the reception system accepts information when a user manually enters their current location. This allows users to easily post gasoline price information using their smartphones. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the photos and GPS information posted by users into a generating AI and have the generating AI perform the information reception.

[0073] The registration unit can register the extracted price information and GPS information into a database. For example, the registration unit can register the price information and GPS information extracted by the reading unit into a relational database. For example, the registration unit inserts the price information and GPS information into a table. The registration unit can also register information into a NoSQL database. For example, the registration unit saves the price information and GPS information in document format. This ensures that the extracted price information and GPS information is accurately registered in the database. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the extracted price information and GPS information into a generating AI and have the generating AI perform the registration to the database.

[0074] The service provider can display price information for gas stations near the user's current location on a map. For example, when a user opens the app, the service provider can identify the user's current location based on GPS information and display price information for nearby gas stations on the map. For example, the service provider can use an API to display gas station price information on the map. The service provider can also display price information for gas stations in a specific area when the user searches for that area. For example, when a user selects a specific area on the map, the service provider can display price information for gas stations in that area. This allows the user to check gas prices near their current location on the map. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input GPS information and gas price information into a generating AI and display the information on the map.

[0075] The service provider can offer rewards to contributors and allow viewers to use the service by paying a monthly membership fee. For example, the service provider can pay rewards to contributors through an electronic payment system. For example, the service provider can offer points or gift cards to contributors. The service provider can also allow viewers to use the service by having them pay a monthly membership fee. For example, the service provider can have viewers pay a monthly membership fee of 200 yen by credit card or bank transfer. In this way, the service provider can offer rewards to contributors and allow viewers to use the service by paying a monthly membership fee. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the calculation of contributor rewards and the management of viewers' membership fees.

[0076] The reception desk can estimate the user's emotions and adjust the timing of submission based on the estimated emotions. For example, if the user is stressed, the reception desk can quickly process the submission and minimize the effort required. For example, when the user is stressed, the reception desk can provide a simple interface and allow them to complete the submission quickly. If the user is relaxed, the reception desk can also provide detailed submission options and suggest a customizable submission method. For example, when the user is relaxed, the reception desk can provide an option to enter detailed information. If the user is in a hurry, the reception desk can prioritize voice input and allow them to quickly submit photos and GPS information. For example, when the user is in a hurry, the reception desk can use voice input to quickly complete the submission. This allows the timing of submission to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the timing of submission.

[0077] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, the reception desk can automatically display gas stations that the user has frequently posted about in the past as candidates. For example, the reception desk can prioritize displaying gas stations that have been frequently posted about based on the user's past posting history. The reception desk can also prioritize suggesting posting methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input to post in the past, it will prioritize suggesting voice input. The reception desk can also predict and suggest gas stations that the user will use at specific times of day based on their past posting history. For example, the reception desk can predict which gas stations the user frequently posts about at specific times of day and prioritize displaying them at those times. This allows the reception desk to select the most suitable reception method based on the user's past posting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past posting history data into a generating AI and have the generating AI select the most suitable reception method.

[0078] The reception unit can filter posts based on the user's current location and areas of interest when a post is received. For example, when a user opens the app, the reception unit can automatically obtain the user's current location and prioritize displaying nearby gas stations. For example, the reception unit can identify the user's current location based on GPS information and display gas stations in the vicinity. The reception unit can also filter posts based on the user's areas of interest (for example, a specific brand of gas station). For example, if a user is interested in a specific brand of gas station, the reception unit will prioritize displaying gas stations of that brand. Furthermore, if the user is using the app while on the move, the reception unit can update the user's current location in real time and display the most suitable post candidates. For example, when a user is using the app while on the move, the reception unit updates the user's current location in real time and displays the most suitable post candidates. This allows for filtering posts based on the user's current location and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's location information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0079] The reception desk can estimate the user's emotions and determine the priority of posts to accept based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a simple and highly visible interface and prioritize the post. For example, when the user is nervous, the reception desk can provide a simple interface and allow the user to complete the post quickly. Also, if the user is relaxed, the reception desk can prioritize posts containing detailed information. For example, when the user is relaxed, the reception desk can provide an option to enter detailed information. Also, if the user is in a hurry, the reception desk can accept the post quickly and prioritize it. For example, when the user is in a hurry, the reception desk can complete the post quickly. This allows for the prioritization of posts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user sentiment data into a generative AI, which can then perform sentiment estimation and determine the priority of posts.

[0080] The reception system can prioritize accepting posts that are highly relevant, taking into account the user's geographical location information. For example, the reception system can prioritize accepting posts about gas stations near the user's current location. For example, the reception system can identify the user's current location based on GPS information and prioritize accepting posts about gas stations in that area. The reception system can also prioritize accepting posts about a specific region if the user is interested in that region. For example, if the user is interested in a specific region, the reception system can prioritize accepting posts about gas stations in that region. Furthermore, if the user is using the app while on the move, the reception system can update the user's current location in real time and prioritize accepting posts that are highly relevant. For example, when the user is using the app while on the move, the reception system can update the user's current location in real time and prioritize accepting posts that are highly relevant. This allows for the priority acceptance of posts that are highly relevant based on the user's geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception desk can input user location data into a generation AI and have the AI ​​select highly relevant posts.

[0081] The reception unit can analyze a user's social media activity when receiving a submission and accept relevant submissions. For example, the reception unit can prioritize accepting submissions of gas stations that the user frequently mentions on social media. The reception unit can also predict which gas stations the user is interested in based on their social media activity and prioritize accepting submissions of those stations. The reception unit can also prioritize accepting submissions of gas stations that the user follows on social media. This allows the reception unit to accept relevant submissions based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant submissions.

[0082] The reading unit can estimate the user's emotions and adjust the accuracy of the reading based on the estimated emotions. For example, if the user is relaxed, the reading unit can perform a reading that includes detailed information. For example, when the user is relaxed, the reading unit extracts detailed information. The reading unit can also perform a quick reading and extract only the minimum necessary information if the user is in a hurry. For example, when the user is in a hurry, the reading unit extracts information quickly. The reading unit can also provide a reading result with visually stimulating effects if the user is excited. For example, when the user is excited, the reading unit provides a reading result with visually appealing effects. This allows the accuracy of the reading to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the accuracy of the reading.

[0083] The reading unit can optimize its reading algorithm based on image quality during reading. For example, in the case of a high-resolution image, the reading unit can use an algorithm that extracts detailed information. For example, the reading unit can take a high-resolution image as input and apply an algorithm that extracts detailed information. The reading unit can also use an algorithm that removes noise and extracts necessary information in the case of a low-resolution image. For example, the reading unit can take a low-resolution image as input and apply an algorithm that performs noise reduction and information extraction. The reading unit can also use an algorithm that adjusts brightness to improve reading accuracy if the image is dark. For example, the reading unit can take a dark image as input and apply an algorithm that adjusts brightness and extracts information. This allows the reading algorithm to be optimized based on image quality. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input image data into a generating AI and have the generating AI optimize the algorithm based on the image quality.

[0084] The reading unit can apply different reading methods depending on the type and location of the gas station. For example, in the case of a large chain gas station, the reading unit can use a method to read standardized price signs. For example, the reading unit can take a standardized price sign from a large chain gas station as input and apply a method to extract the information. The reading unit can also use a method to read handwritten price signs in the case of a small local gas station. For example, the reading unit can take a handwritten price sign from a small local gas station as input and apply a method to extract the information. Furthermore, in the case of an urban gas station, the reading unit can use a rapid reading method to avoid congestion. For example, the reading unit can take a price sign from an urban gas station as input and apply a method to quickly extract the information. This allows different reading methods to be applied depending on the type and location of the gas station. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the type and location information of the gas station into a generating AI and have the generating AI select the optimal reading method.

[0085] The reading unit can estimate the user's emotions and adjust the display method of the reading results based on the estimated user emotions. For example, if the user is nervous, the reading unit can provide a simple and highly visible display method. For example, if the user is nervous, the reading unit can provide a simple and highly visible display method. The reading unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the reading unit can provide a display method that includes detailed information. The reading unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the reading unit can provide a display method that gets to the point. This allows the display method of the reading results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the display method.

[0086] The reading unit can determine the reading priority based on the time the posted image was taken. For example, the reading unit can prioritize reading the most recent image to provide real-time information. For example, the reading unit can take the most recent image as input and extract information preferentially. The reading unit can also postpone reading older images and prioritize the latest information. For example, the reading unit can take older images as input and prioritize extracting the latest information. The reading unit can also prioritize reading images taken during a specific time period to understand price fluctuations for each time period. For example, the reading unit can take images taken during a specific time period as input and extract information preferentially. This allows the reading priority to be determined based on the time the posted image was taken. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the image shooting time data into a generating AI and have the generating AI perform the priority determination.

[0087] The reading unit can improve reading accuracy by referring to relevant information about the image during reading. For example, the reading unit can improve reading accuracy by referring to image metadata (such as the date and time of shooting, location information, etc.). For example, the reading unit can take image metadata as input and refer to it when extracting information. The reading unit can also improve reading accuracy by referring to other text information within the image (for example, the name of a gas station). For example, the reading unit can take other text information within the image as input and refer to it when extracting information. The reading unit can also improve reading accuracy by referring to background information of the image (for example, weather and lighting conditions). For example, the reading unit can take background information of the image as input and refer to it when extracting information. This improves reading accuracy by referring to relevant information about the image. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input relevant information about the image to a generating AI and have the generating AI perform the improvement of reading accuracy.

[0088] The registration unit can estimate the user's emotions and select registration data based on the estimated emotions. For example, if the user is relaxed, the registration unit can register data containing detailed information. For example, if the user is relaxed, the registration unit can register data containing detailed information. The registration unit can also register only the minimum necessary information if the user is in a hurry. For example, if the user is in a hurry, the registration unit can register only the minimum necessary information. The registration unit can also register data with visually stimulating effects if the user is excited. For example, if the user is excited, the registration unit can register data with visually appealing effects. This allows for the selection of registration data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input user emotion data into a generating AI, which can then perform emotion estimation and select registration data.

[0089] The registration unit can optimize the registration algorithm by referring to past registration data during registration. For example, the registration unit can select the optimal registration algorithm based on past registration data. For example, the registration unit can analyze past registration data and select the optimal algorithm. The registration unit can also select an algorithm with fewer errors from past registration data. For example, the registration unit can select an algorithm with fewer errors based on past registration data. The registration unit can also analyze past registration data and select the most efficient registration algorithm. For example, the registration unit can select the most efficient algorithm based on past registration data. This allows the registration algorithm to be optimized by referring to past registration data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input past registration data into a generating AI and have the generating AI perform algorithm optimization.

[0090] The registration unit can adjust the database structure considering gasoline price fluctuation patterns during registration. For example, the registration unit can optimize the database index based on gasoline price fluctuation patterns. For example, the registration unit can analyze gasoline price fluctuation patterns and optimize the index. The registration unit can also analyze gasoline price fluctuation patterns and adjust the database table structure. For example, the registration unit can adjust the table structure based on gasoline price fluctuation patterns. The registration unit can also optimize the database caching strategy considering gasoline price fluctuation patterns. For example, the registration unit can optimize the caching strategy based on gasoline price fluctuation patterns. This allows the database structure to be optimized considering gasoline price fluctuation patterns. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input gasoline price fluctuation pattern data into a generating AI and have the generating AI perform the adjustment of the database structure.

[0091] The registration unit can estimate the user's emotions and determine the priority of registration data based on the estimated emotions. For example, if the user is nervous, the registration unit will prioritize registering simple and easily visible data. For example, if the user is nervous, the registration unit will prioritize registering simple and easily visible data. The registration unit can also prioritize registering data containing detailed information if the user is relaxed. For example, if the user is relaxed, the registration unit will prioritize registering data containing detailed information. The registration unit can also prioritize registering data that can be registered quickly if the user is in a hurry. For example, if the user is in a hurry, the registration unit will prioritize registering data that can be registered quickly. This allows the priority of registration data to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input user emotion data into a generating AI, which can then perform emotion estimation and determine the priority of the registration data.

[0092] The registration unit can perform registration while considering the geographical distribution of the submitted data. For example, the registration unit can select the optimal registration method based on the geographical distribution of the submitted data. For example, the registration unit can analyze the geographical distribution of the submitted data and select the optimal registration method. The registration unit can also select a registration method to evenly distribute geographically biased data. For example, the registration unit can select a registration method to evenly distribute geographically biased data. The registration unit can also optimize the database index while considering the geographical distribution. For example, the registration unit optimizes the database index based on the geographical distribution. This allows for optimized registration while considering the geographical distribution of the submitted data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input geographical distribution data of the submitted data into a generating AI and have the generating AI select the registration method.

[0093] The registration unit can improve the accuracy of registration by referring to relevant market data during registration. For example, the registration unit can select the optimal registration method based on market data. For example, the registration unit can analyze market data and select the optimal registration method. The registration unit can also select a registration method with fewer errors by referring to market data. For example, the registration unit can select a registration method with fewer errors based on market data. The registration unit can also analyze market data and select the most efficient registration method. For example, the registration unit can select the most efficient registration method based on market data. This improves the accuracy of registration by referring to relevant market data. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input market data into a generating AI and have the generating AI perform the selection of a registration method.

[0094] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. The service provider can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. The service provider can also provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide a display method that gets to the point. This allows the service provider to adjust the way information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation and adjust the display method.

[0095] The information provider can provide optimal information by referring to the user's past browsing history at the time of delivery. For example, the information provider can prioritize displaying information about gas stations that the user has previously viewed. For example, the information provider can prioritize displaying information about gas stations that the user has frequently viewed based on the user's past browsing history. The information provider can also predict and provide information of interest to the user based on the user's past browsing history. For example, the information provider can analyze the user's past browsing history, predict and provide information of interest. The information provider can also provide optimal information based on information that the user has frequently viewed in the past. For example, the information provider can provide optimal information based on the user's past browsing history. This allows the information provider to provide optimal information based on the user's past browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past browsing history data into a generating AI and have the generating AI select the optimal information.

[0096] The service provider can customize information at the time of delivery, taking into account predictions of gasoline price fluctuations. For example, the service provider can suggest the optimal refueling timing based on predictions of gasoline price fluctuations. For example, the service provider can analyze predictions of gasoline price fluctuations and suggest the optimal refueling timing. The service provider can also suggest the cheapest gas station, taking into account predictions of gasoline price fluctuations. For example, the service provider can suggest the cheapest gas station based on predictions of gasoline price fluctuations. The service provider can also customize and provide information tailored to the user based on predictions of gasoline price fluctuations. For example, the service provider can customize and provide information tailored to the user based on predictions of gasoline price fluctuations. This allows for the customization of information based on predictions of gasoline price fluctuations. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input prediction data of gasoline price fluctuations into a generating AI and have the generating AI perform the customization of the information.

[0097] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is nervous, the provider will prioritize providing simple and easily visible information. For example, if the user is nervous, the provider will prioritize providing simple and easily visible information. The provider can also prioritize providing information containing detailed information if the user is relaxed. For example, if the user is relaxed, the provider will prioritize providing information containing detailed information. The provider can also prioritize providing information that can be provided quickly if the user is in a hurry. For example, if the user is in a hurry, the provider will prioritize providing information that can be provided quickly. This allows the information to be prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation and determine the priority of information.

[0098] The service provider can provide optimal information by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing information on gas stations near the user's current location. For example, the service provider can identify the user's current location based on GPS information and prioritize providing information on gas stations in that area. The service provider can also prioritize providing information on a specific region if the user is interested in that region. For example, if the service provider is interested in a specific region, it can prioritize providing information on gas stations in that region. Furthermore, if the user is using the app while on the move, the service provider can update the user's current location in real time and provide optimal information. For example, when the user is using the app while on the move, the service provider updates the user's current location in real time and provides optimal information. This allows the service provider to provide optimal information based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select the optimal information.

[0099] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing information about gas stations that the user frequently mentions on social media. For example, the service provider can prioritize providing information about gas stations that are frequently mentioned based on the user's social media activity. The service provider can also predict and provide information of interest based on the user's social media activity. For example, the service provider can analyze the user's social media activity, predict and provide information of interest. The service provider can also prioritize providing information about gas stations that the user follows on social media. For example, the service provider can prioritize providing information about gas stations that the user follows. This allows the service provider to provide relevant information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI select relevant information.

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

[0101] The gasoline price information sharing system can further estimate the user's emotions and adjust how gasoline price information is displayed based on those emotions. For example, if the user is stressed, the system can provide a simple, highly visible interface and quickly display the necessary information. If the user is relaxed, it can provide an interface with detailed information to help the user understand the information more deeply. Furthermore, if the user is in a hurry, it can prioritize displaying concise information to enable quick decision-making. This allows for optimal information display according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the information display method.

[0102] The gasoline price information sharing system can further analyze the user's past browsing history and provide optimal information. For example, the information provider can prioritize displaying information about gas stations that the user has frequently viewed in the past. It can also predict and provide information of interest based on the user's past browsing history. Furthermore, it can provide information that is optimal for a given time period based on the information the user frequently views during that time. This allows the system to provide optimal information based on the user's past browsing history. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past browsing history data into a generating AI and have the generating AI select the optimal information.

[0103] The gasoline price information sharing system can further estimate the user's emotions and adjust the submission process based on those emotions. For example, if the user is stressed, the submission process can be expedited to minimize effort. If the user is relaxed, it can offer detailed submission options and suggest a customizable submission method. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick submission of photos and GPS information. This allows the submission process to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as text generation AI or multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the submission process may be performed using AI or not. For example, the submission process can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the submission process.

[0104] The gasoline price information sharing system can further provide optimal information by taking into account the user's geographical location. For example, the information provider can prioritize providing information on gas stations near the user's current location. It can also prioritize providing information on a specific region if the user is interested in that region. Furthermore, if the user is using the app while on the move, the system can update the user's current location in real time to provide optimal information. This allows the system to provide optimal information based on the user's geographical location. Some or all of the above processing in the information provider can be performed using AI or not. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal information.

[0105] The gasoline price information sharing system can further estimate the user's emotions and prioritize the information provided based on those emotions. For example, if the user is stressed, the information provider can prioritize providing simple, easily visible information. If the user is relaxed, it can also prioritize providing information containing more details. Furthermore, if the user is in a hurry, it can prioritize providing information that can be delivered quickly. This allows for the prioritization of information according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the processing described above in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and information prioritization.

[0106] The gasoline price information sharing system can further analyze users' social media activity and provide relevant information. For example, the information provider can prioritize providing information on gas stations that users frequently mention on social media. It can also predict and provide information of interest based on users' social media activity. Furthermore, it can prioritize providing information on gas stations that users follow on social media. This allows the system to provide relevant information based on users' social media activity. Some or all of the above processing in the information provider can be performed using AI or not. For example, the information provider can input users' social media data into a generating AI and have the generating AI select relevant information.

[0107] The gasoline price information sharing system can further estimate the user's emotions and adjust the accuracy of the reading based on the estimated emotions. For example, if the user is relaxed, the reading unit can perform a detailed reading. If the user is in a hurry, it can perform a quick reading and extract only the minimum necessary information. Furthermore, if the user is excited, it can provide a reading result with visually stimulating effects. This allows the accuracy of the reading to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of reading accuracy.

[0108] The gasoline price information sharing system can further analyze a user's past posting history and select the optimal reception method. For example, the reception unit can automatically display gas stations that the user has frequently posted about in the past as candidates. It can also prioritize suggesting posting methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest gas stations to be used at specific times based on the user's past posting history. This allows for the selection of the optimal reception method based on the user's past posting history. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal reception method.

[0109] The gasoline price information sharing system can further estimate the user's emotions and select registration data based on those emotions. For example, if the user is relaxed, the registration unit can register data containing detailed information. If the user is in a hurry, it can register only the minimum necessary information. Furthermore, if the user is excited, it can register data with visually stimulating effects. This allows the system to select registration data according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and registration data selection.

[0110] The gasoline price information sharing system can also prioritize receiving posts that are highly relevant, taking into account the user's geographical location. For example, the receiving unit can prioritize receiving posts about gas stations near the user's current location. Furthermore, if the user is interested in a specific region, posts about that region can be prioritized. Additionally, if the user is using the app while on the move, their current location can be updated in real time, and highly relevant posts can be prioritized. This allows for the prioritization of highly relevant posts based on the user's geographical location. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can input the user's location data into a generating AI and have the generating AI select highly relevant posts.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk accepts photos and GPS information submissions from users. Users take photos of gas station price displays using their smartphones and submit them along with GPS information. The reception desk can accept photos in image formats such as JPEG and PNG. Step 2: The reading unit uses generation AI to read the gasoline price from the photo received by the reception unit. The reading unit uses image recognition technology to extract price information from the photo. For example, it uses OCR technology to extract price information such as "Regular: 150 yen" and "Premium: 160 yen" from the photo. Step 3: The registration unit registers the gasoline price information and GPS information read by the reading unit into the database. The registration unit can register information into databases such as relational databases and NoSQL databases. Step 4: The service provider provides gasoline price information based on the information registered by the registration provider. The service provider enables users to check gasoline price information in real time through the app. For example, it displays price information for gas stations near the user's current location on a map.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0116] Each of the multiple elements described above, including the reception unit, reading unit, registration unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts photos and GPS information submissions from users. The reading unit is implemented by the identification processing unit 290 of the data processing unit 12 and reads the gasoline price from the photo using a generation AI. The registration unit is implemented by the identification processing unit 290 of the data processing unit 12 and registers the read gasoline price information and GPS information in the database 24. The provision unit is implemented by the control unit 46A of the smart device 14 and allows users to check gasoline price information in real time through an app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, reading unit, registration unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts photos and GPS information submissions from users. The reading unit is implemented by the identification processing unit 290 of the data processing unit 12 and reads the gasoline price from the photo using a generation AI. The registration unit is implemented by the identification processing unit 290 of the data processing unit 12 and registers the read gasoline price information and GPS information in the database 24. The provision unit is implemented by the control unit 46A of the smart glasses 214 and allows users to check gasoline price information in real time through an app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, reading unit, registration unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives submissions of photos and GPS information from users. The reading unit is implemented by the specific processing unit 290 of the data processing unit 12 and reads the gasoline price from the photo using a generation AI. The registration unit is implemented by the specific processing unit 290 of the data processing unit 12 and registers the read gasoline price information and GPS information in the database 24. The provision unit is implemented by the control unit 46A of the headset terminal 314 and allows users to check gasoline price information in real time through an application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, reading unit, registration unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts submissions of photos and GPS information from users. The reading unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and reads the gasoline price from the photo using a generation AI. The registration unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and registers the read gasoline price information and GPS information in the database 24. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and allows users to check gasoline price information in real time through an app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception area that accepts photos and GPS information submissions from users, A reading unit that reads the gasoline price from the photograph received by the reception unit, A registration unit registers the gasoline price information and GPS information read by the aforementioned reading unit into a database. The system includes a provisioning unit that provides gasoline price information based on the information registered by the registration unit. A system characterized by the following features. (Note 2) The reading unit is Extract price information from a photograph using image recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The app will allow users to check gasoline price information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Users take photos of gas station price displays using their smartphones and post them along with GPS information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned registration unit is The extracted price information and GPS information are registered in the database. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Display gas station price information for your current location on a map. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, The service is made available by offering compensation to contributors and allowing viewers to use it by paying a monthly membership fee. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates user sentiment and adjusts the timing of submissions based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past posting history and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When submitting a post, filtering is performed based on the user's current location and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's sentiment and determines the priority of posts to accept based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When accepting submissions, the system prioritizes submissions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When accepting submissions, the system analyzes the user's social media activity and accepts relevant submissions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The reading unit is It estimates the user's emotions and adjusts the accuracy of the readings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reading unit is During scanning, the scanning algorithm is optimized based on the image quality. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reading unit is When reading the data, different reading methods are applied depending on the type and location of the gas station. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reading unit is It estimates the user's emotions and adjusts how the reading results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reading unit is When reading images, the reading priority is determined based on the time the posted image was taken. The system described in Appendix 1, characterized by the features described herein. (Note 19) The reading unit is During scanning, the system references relevant image information to improve scanning accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned registration unit is The system estimates the user's emotions and selects registration data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned registration unit is During registration, the registration algorithm is optimized by referring to past registration data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned registration unit is During registration, the database structure is adjusted to take into account the fluctuation patterns of gasoline prices. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned registration unit is The system estimates user sentiment and prioritizes registration data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned registration unit is When registering data, the geographical distribution of the submitted data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned registration unit is During registration, we refer to relevant market data to improve the accuracy of the registration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, we refer to the user's past browsing history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the information, we customize it to take into account predictions of fluctuations in gasoline prices. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts photos and GPS information submissions from users, A reading unit that reads the gasoline price from the photograph received by the reception unit, A registration unit registers the gasoline price information and GPS information read by the aforementioned reading unit into a database. The system includes a provisioning unit that provides gasoline price information based on the information registered by the registration unit. A system characterized by the following features.

2. The reading unit is Use image recognition technology to extract price information from photographs. The system according to feature 1.

3. The aforementioned supply unit is, This will allow users to check real-time gasoline price information through the app. The system according to feature 1.

4. The aforementioned reception unit is Users take photos of gas station price boards using their smartphones and post them along with GPS information. The system according to feature 1.

5. The aforementioned registration unit is The extracted price information and GPS information are registered in the database. The system according to feature 1.

6. The aforementioned supply unit is, Display gas station price information for your current location on a map. The system according to feature 1.

7. The aforementioned supply unit is, The service is made available by offering compensation to contributors and allowing viewers to use it by paying a monthly membership fee. The system according to feature 1.

8. The aforementioned reception unit is The system estimates user sentiment and adjusts the timing of submissions based on the estimated sentiment. The system according to feature 1.

9. The aforementioned reception unit is Analyze the user's past posting history and select the most suitable submission method. The system according to feature 1.

10. The aforementioned reception unit is When submitting a post, filtering is performed based on the user's current location and areas of interest. The system according to feature 1.

Images