System

Abstract

An object of a system according to an embodiment is to efficiently collect price information of a store and notify a customer of the latest price information.SOLUTION: A system includes a photograph acquisition unit, a price analysis unit, a price update unit, and a notification unit. The photograph acquisition unit acquires a photograph of the entire store. The price analysis unit analyzes price information from the photograph acquired by the photograph acquisition unit. The price update unit updates the price information analyzed by the price analysis unit on the service. The notification unit notifies the customer who has set the notification price.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently collect in-store price information and notify customers of the latest price information.

[0005] The system according to the embodiment aims to efficiently collect in-store price information and notify customers of the latest price information. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo acquisition unit, a price analysis unit, a price update unit, and a notification unit. The photo acquisition unit acquires photos of the entire storefront. The price analysis unit analyzes price information from the photos acquired by the photo acquisition unit. The price update unit updates the price information analyzed by the price analysis unit on the service. The notification unit notifies customers who have set the notification price. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect in-store price information and notify customers of the latest price information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The price notification system according to the embodiment of the present invention is a system that can notify customers of the latest prices for each product simply by taking a photo of the entire storefront. This allows the price notification system to automatically update the price information in the storefront and notify customers of the latest prices.

[0029] A price notification system according to an embodiment includes a photo acquisition unit, a price analysis unit, a price update unit, and a notification unit. The photo acquisition unit acquires a photo of the entire storefront. For example, the photo may be taken using a smartphone or digital camera. The photo acquisition unit may also capture a specific area of ​​the store, such as a specific product shelf or display. The price analysis unit analyzes price information from the photo acquired by the photo acquisition unit. For example, the generation AI may analyze price tags in the photo to read the price of each product. The generation AI may also analyze the shape and color of products to improve the reliability of the price information. For example, the shape and color of a tomato may be analyzed to confirm whether the price matches the price on the price tag. The price update unit updates the price information analyzed by the price analysis unit on the service. For example, the price of each product may be automatically updated on the service based on the price information read by the generation AI. The price update unit may also update the prices in the photo to the latest prices based on price information in a store database. For example, if a price card in a store displays "Tomatoes 120 yen," but the store database lists "Tomatoes 100 yen," the generation AI updates the price in the photo to 100 yen. The notification unit notifies customers who have set the notification price. For example, if a customer sets a notification request to "notify me when tomatoes drop below 90 yen," the customer will receive a notification when the generation AI reads the price of tomatoes as below 90 yen. As a result, the price notification system according to the embodiment can notify customers of the latest prices for each product simply by taking a photo of the entire storefront. For example, by constantly keeping in-store price information up-to-date, customers can enjoy shopping with peace of mind. In addition, by utilizing social media, store information can be widely disseminated, increasing customer attraction.

[0030] The price analysis unit can improve the reliability of price information by analyzing not only the price card in the photo but also the shape and color of the product. For example, the generation AI can analyze not only the price card in the photo but also the shape and color of the product itself to improve the reliability of the price information. For example, it can analyze the shape and color of a tomato and check whether it matches the price on the price card. The price analysis unit can also use an algorithm to improve the reliability of price information based on the shape and color of the product. For example, the generation AI can analyze the shape and color of the product and evaluate the reliability of the price information. This can improve the reliability of the price information.

[0031] The price analysis unit can automatically correct lighting conditions and shooting angles in stores to improve the accuracy of price readings. For example, the generation AI analyzes lighting conditions in stores and automatically corrects them to improve the accuracy of price readings. For example, the price analysis unit can read price cards even under dim lighting by adjusting the brightness. The price analysis unit can also use an algorithm to automatically correct the shooting angle. For example, the generation AI analyzes the shooting angle and corrects it to the optimal angle. This can improve the accuracy of price readings.

[0032] The photo acquisition unit can capture a video of the entire storefront, and the price analysis unit can extract price information from multiple frames in the video. The photo acquisition unit, for example, captures a video of the entire storefront. For example, a smartphone or digital camera can be used to capture a video of the entire storefront. The photo acquisition unit can also capture a video of a specific area. For example, a specific product shelf or display can be captured in the video. The price analysis unit, for example, uses a generation AI to extract price information from multiple frames in the video. For example, each frame of the video can be analyzed to accurately read the price on the price card. The price analysis unit can also use an algorithm to select important frames in the video and extract price information. For example, the generation AI can analyze important frames in the video and extract price information. This makes it possible to extract price information from multiple frames in the video.

[0033] The photo acquisition unit simultaneously collects in-store audio information, and the price analysis unit can complement the price information using voice recognition. The photo acquisition unit, for example, simultaneously collects in-store audio information. For example, it collects in-store announcements and customer conversations. The photo acquisition unit can also collect audio information from a specific area. For example, it collects audio information near a specific product shelf or display. The price analysis unit, for example, uses voice recognition by the generation AI to complement the price information. For example, it analyzes the audio of a store clerk verbally explaining the price of a product. The price analysis unit can also use an algorithm to complement the price information based on the voice information. For example, the generation AI analyzes the voice information and complements the price information. This makes it possible to complement the price information using voice recognition.

[0034] The price update unit can analyze the history of price information fluctuations, predict price trends, and display them on the service. For example, the generation AI in the price update unit analyzes the history of price information fluctuations, predicts price trends, and displays them on the service. For example, future price fluctuations are predicted based on past price data. The price update unit can also use an algorithm to display price information based on price trends. For example, the generation AI analyzes price trends and displays them on the service. This makes it possible to predict price trends and display them on the service.

[0035] The price update unit can implement an algorithm that automatically adjusts the prices of related products when updating price information. For example, the price update unit implements an algorithm that automatically adjusts the prices of related products when updating price information. For example, if the price of tomatoes drops, the prices of related vegetables are also adjusted. The price update unit can also use an algorithm that adjusts price information based on the prices of related products. For example, the generation AI analyzes the prices of related products and adjusts the price information. This allows the prices of related products to be automatically adjusted.

[0036] The price update unit can update price information in real time, allowing customers to check the latest prices while they are in the store. For example, the price update unit can update price information in real time, allowing customers to check the latest prices while they are in the store. For example, the latest prices can be displayed on an in-store display. The price update unit can also use an algorithm that updates price information in real time. For example, a generation AI can update price information in real time and display the latest prices. This allows customers to check the latest prices while they are in the store.

[0037] The price update unit can build a system that automatically updates inventory information at the same time as updating price information. The price update unit builds a system that automatically updates inventory information at the same time as updating price information. For example, when the price is changed, the inventory quantity is automatically adjusted. The price update unit can also use an algorithm that updates price information based on inventory information. For example, a generation AI analyzes inventory information and updates the price information. This allows inventory information to be updated automatically.

[0038] The notification unit can have the generation AI analyze the customer's purchase history and automatically suggest the optimal notification price. For example, the notification unit can have the generation AI analyze the customer's purchase history and automatically suggest the optimal notification price. For example, it can suggest prices that the customer is likely to be interested in based on past purchase data. The notification unit can also use an algorithm that suggests a notification price based on the customer's purchase history. For example, the generation AI can analyze the customer's purchase history and suggest the optimal notification price. This makes it possible to automatically suggest the optimal notification price.

[0039] The notification unit can add a function to display a recommended price that takes into account the setting information of other customers when setting a notification price. The notification unit can add a function to display a recommended price that takes into account the setting information of other customers when setting a notification price. For example, the notification price of other customers for the same product can be displayed. The notification unit can also use an algorithm to display a recommended price based on the setting information of other customers. For example, a generation AI can analyze the setting information of other customers and display a recommended price. This makes it possible to add a function to display a recommended price.

[0040] The notification unit can set the notification price by voice input or gesture input. The notification unit can, for example, build a system that allows the notification price to be set by voice input. For example, a customer can set it by voice, saying, "Notify me when tomatoes drop below 90 yen." The notification unit can also build a system that allows the notification price to be set by gesture input. For example, a customer can set the notification price by gesture. This makes it possible to set the notification price by voice input or gesture input.

[0041] The notification unit can add a function to notify discount information for related products at the same time as setting the notification price. For example, when the price of tomatoes drops, the notification unit can also notify discount information for cucumbers. The notification unit can also use an algorithm to customize the content of the notification based on discount information for related products. For example, a generation AI can analyze discount information for related products and customize the content of the notification. This makes it possible to add a function to notify discount information for related products.

[0042] The price update unit allows the generation AI to compare the price information in the store database with actual sales data and optimize pricing. For example, the generation AI compares the price information in the store database with actual sales data and optimizes pricing. For example, it adjusts prices based on sales data. The price update unit can also use an algorithm that optimizes pricing based on the price information and sales data in the store database. For example, the generation AI analyzes the price information and sales data and optimizes pricing. This allows for optimization of pricing.

[0043] The price update unit can compare the price information in the store database with price information from other stores to automatically set competitive prices. The price update unit, for example, builds a system that compares the price information in the store database with price information from other stores to automatically set competitive prices. For example, it adjusts prices based on the prices of nearby stores. The price update unit can also use an algorithm that sets competitive prices based on price information from other stores. For example, a generation AI analyzes price information from other stores and sets competitive prices. This makes it possible to automatically set competitive prices.

[0044] The price update unit can add a function to automatically adjust the price information in the store database by comparing it with the market price for each region. The price update unit, for example, adds a function to automatically adjust the price information in the store database by comparing it with the market price for each region. For example, the price can be set based on the average price for each region. The price update unit can also use an algorithm to adjust the price information based on the market price for each region. For example, the generation AI analyzes the market price for each region and adjusts the price information. This allows automatic adjustment by comparing it with the market price for each region.

[0045] The price update unit can build a system that automatically updates the price information in the store database according to the season or event. The price update unit builds a system that automatically updates the price information in the store database according to the season or event, for example. For example, prices are adjusted to coincide with Christmas or New Year sales. The price update unit can also use an algorithm that updates price information based on the season or event. For example, a generation AI analyzes the season or event and updates the price information. This allows automatic updates according to the season or event.

[0046] The SNS upload unit uses a generation AI to analyze the content of a photo and automatically generate optimal hashtags and captions. For example, the SNS upload unit uses a generation AI to analyze the content of a photo and automatically generate optimal hashtags and captions. For example, hashtags may be suggested based on product and price information in the photo. The SNS upload unit can also use an algorithm to generate hashtags and captions based on the content of the photo. For example, the generation AI analyzes the content of the photo and generates optimal hashtags and captions. This makes it possible to automatically generate optimal hashtags and captions.

[0047] The SNS upload unit can add a function to automatically apply filters and effects to photos when uploading to SNS. The SNS upload unit can add a function to automatically apply filters and effects to photos when uploading to SNS. For example, the SNS upload unit can automatically adjust the color tone and brightness of photos. The SNS upload unit can also use an algorithm to improve the appearance of photos based on filters and effects. For example, a generative AI analyzes the content of the photo and applies the most appropriate filter or effect. This makes it possible to automatically apply filters and effects.

[0048] The SNS upload unit can be equipped with a function that automatically adds store promotional information when uploading photos to SNS. The SNS upload unit can be equipped with a function that automatically adds store promotional information when uploading photos to SNS. For example, sale information or new product introductions can be posted along with the photos. The SNS upload unit can also use an algorithm that customizes the content of the photos based on the promotional information. For example, a generation AI analyzes the promotional information and customizes the content of the photos. This makes it possible to automatically add store promotional information.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The price notification system can further include a voice guidance unit. The voice guidance unit, for example, provides the latest price information by voice through speakers in the store. For example, when a particular product is reduced in price, this information is announced in the store. The voice guidance unit can also respond with the latest price information when a customer asks a question about a particular product by voice. For example, if a customer asks, "What is the price of tomatoes?", the voice guidance unit will respond, "Tomatoes are currently 100 yen." This makes it possible to provide the latest price information not only visually but also aurally.

[0051] The price analysis unit can further use a temperature sensor to analyze the storage conditions of products and improve the reliability of price information. For example, it can analyze the temperature of refrigerated products to check whether they are stored properly. It can also use a temperature sensor to adjust price information according to temperature changes in the store. For example, if the temperature rises due to a refrigerator malfunction, it can discount the price of the product. This makes it possible to improve the reliability of price information while maintaining the quality of the product.

[0052] The price notification system may further include a location information acquisition unit. The location information acquisition unit acquires in-store location information using, for example, the GPS of the customer's smartphone. For example, if a customer is near a specific product shelf, the unit notifies the customer of the latest price of that product. The location information acquisition unit can also analyze the customer's movement path and notify the customer of price information at the optimal time. For example, if a specific product is reduced in price while the customer is on their way to the cash register, the unit notifies the customer of this information. This makes it possible to provide optimal price information based on the customer's location information.

[0053] The price notification system can further include an inventory management unit. The inventory management unit, for example, monitors the inventory status in the store in real time and links it with price information. For example, it can raise the price of products that are low in stock, or lower the price of products that are in abundant stock. The inventory management unit can also analyze product sales and adjust price information. For example, if a particular product remains unsold, it can discount the price of that product. This makes it possible to set optimal prices according to the inventory status.

[0054] The price notification system can further include a review analysis unit. The review analysis unit, for example, analyzes online reviews and posts on social media to understand product ratings and popularity. For example, if a particular product has received high ratings, the price of that product can be adjusted. The review analysis unit can also display price information based on product ratings. For example, for highly rated products, some of the reviews can be displayed along with price information. This allows optimal pricing to be achieved based on customer ratings.

[0055] The price notification system may further include a weather information acquisition unit. The weather information acquisition unit, for example, acquires current weather information and links it with price information. For example, it may lower the price of cold drinks on a hot day, or lower the price of hot drinks on a cold day. The weather information acquisition unit may also adjust price information based on weather forecast information. For example, if rain is forecast for the next day, it may raise the price of umbrellas. This allows optimal pricing to be set according to weather information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The photo acquisition unit acquires a photo of the entire storefront. For example, a smartphone or digital camera can be used to take a photo of the entire storefront. The photo acquisition unit can also capture a photo of a specific area within the store. For example, a specific product shelf or display can be photographed. Step 2: The price analysis unit analyzes the price information from the photos acquired by the photo acquisition unit. For example, the generation AI analyzes the price cards in the photos and reads the price of each product. The generation AI can also analyze the shape and color of the products to improve the reliability of the price information. For example, it can analyze the shape and color of a tomato to confirm that it matches the price on the price card. Step 3: The price update unit updates the price information analyzed by the price analysis unit on the service. For example, the price of each product is automatically updated on the service based on the price information read by the generation AI. The price update unit can also update the price on the photo to the latest based on price information in the store database. For example, if the price card in the store displays "Tomato 120 yen," but the store database lists it as "Tomato 100 yen," the generation AI will update the price on the photo to 100 yen. Step 4: The notification unit notifies the customer who set the notification price. For example, if a customer sets a notification when tomatoes fall below 90 yen, the customer will receive a notification when the AI ​​generator detects that the price of tomatoes falls below 90 yen.

[0058] (Example 2) The price notification system according to the embodiment of the present invention is a system that can notify customers of the latest prices for each product simply by taking a photo of the entire storefront. This allows the price notification system to automatically update the price information in the storefront and notify customers of the latest prices.

[0059] A price notification system according to an embodiment includes a photo acquisition unit, a price analysis unit, a price update unit, and a notification unit. The photo acquisition unit acquires a photo of the entire storefront. For example, the photo may be taken using a smartphone or digital camera. The photo acquisition unit may also capture a specific area of ​​the store, such as a specific product shelf or display. The price analysis unit analyzes price information from the photo acquired by the photo acquisition unit. For example, the generation AI may analyze price tags in the photo to read the price of each product. The generation AI may also analyze the shape and color of products to improve the reliability of the price information. For example, the shape and color of a tomato may be analyzed to confirm whether the price matches the price on the price tag. The price update unit updates the price information analyzed by the price analysis unit on the service. For example, the price of each product may be automatically updated on the service based on the price information read by the generation AI. The price update unit may also update the prices in the photo to the latest prices based on price information in a store database. For example, if a price card in a store displays "Tomatoes 120 yen," but the store database lists "Tomatoes 100 yen," the generation AI updates the price in the photo to 100 yen. The notification unit notifies customers who have set the notification price. For example, if a customer sets a notification request to "notify me when tomatoes drop below 90 yen," the customer will receive a notification when the generation AI reads the price of tomatoes as below 90 yen. As a result, the price notification system according to the embodiment can notify customers of the latest prices for each product simply by taking a photo of the entire storefront. For example, by constantly keeping in-store price information up-to-date, customers can enjoy shopping with peace of mind. In addition, by utilizing social media, store information can be widely disseminated, increasing customer attraction.

[0060] The price analysis unit can improve the reliability of price information by analyzing not only the price card in the photo but also the shape and color of the product. For example, the generation AI can analyze not only the price card in the photo but also the shape and color of the product itself to improve the reliability of the price information. For example, it can analyze the shape and color of a tomato and check whether it matches the price on the price card. The price analysis unit can also use an algorithm to improve the reliability of price information based on the shape and color of the product. For example, the generation AI can analyze the shape and color of the product and evaluate the reliability of the price information. This can improve the reliability of the price information.

[0061] The price analysis unit can automatically correct lighting conditions and shooting angles in stores to improve the accuracy of price readings. For example, the generation AI analyzes lighting conditions in stores and automatically corrects them to improve the accuracy of price readings. For example, the price analysis unit can read price cards even under dim lighting by adjusting the brightness. The price analysis unit can also use an algorithm to automatically correct the shooting angle. For example, the generation AI analyzes the shooting angle and corrects it to the optimal angle. This can improve the accuracy of price readings.

[0062] The price analysis unit can use the emotion estimation function to analyze the emotions of a user who took a photo and provide photography advice to elicit positive emotions. The price analysis unit, for example, can use the emotion estimation function to analyze the emotions of a user who took a photo and provide photography advice to elicit positive emotions. For example, if the user is nervous, the price analysis unit can display advice to relax. The price analysis unit can also customize photography advice based on the user's emotions. For example, the generation AI can analyze the user's emotions and provide optimal photography advice. This makes it possible to provide photography advice to elicit positive emotions.

[0063] The photo acquisition unit can capture a video of the entire storefront, and the price analysis unit can extract price information from multiple frames in the video. The photo acquisition unit, for example, captures a video of the entire storefront. For example, a smartphone or digital camera can be used to capture a video of the entire storefront. The photo acquisition unit can also capture a video of a specific area. For example, a specific product shelf or display can be captured in the video. The price analysis unit, for example, uses a generation AI to extract price information from multiple frames in the video. For example, each frame of the video can be analyzed to accurately read the price on the price card. The price analysis unit can also use an algorithm to select important frames in the video and extract price information. For example, the generation AI can analyze important frames in the video and extract price information. This makes it possible to extract price information from multiple frames in the video.

[0064] The photo acquisition unit simultaneously collects in-store audio information, and the price analysis unit can complement the price information using voice recognition. The photo acquisition unit, for example, simultaneously collects in-store audio information. For example, it collects in-store announcements and customer conversations. The photo acquisition unit can also collect audio information from a specific area. For example, it collects audio information near a specific product shelf or display. The price analysis unit, for example, uses voice recognition by the generation AI to complement the price information. For example, it analyzes the audio of a store clerk verbally explaining the price of a product. The price analysis unit can also use an algorithm to complement the price information based on the voice information. For example, the generation AI analyzes the voice information and complements the price information. This makes it possible to complement the price information using voice recognition.

[0065] The price analysis unit can use the emotion estimation function to analyze the store atmosphere and customer reactions, and use this information to help optimize pricing. For example, the price analysis unit can use the emotion estimation function to analyze the store atmosphere and customer reactions, and use this information to help optimize pricing. For example, if customers are satisfied, the price can be maintained. The price analysis unit can also use an algorithm that optimizes pricing based on the store atmosphere and customer reactions. For example, a generative AI can analyze the store atmosphere and customer reactions to optimize pricing. This can help optimize pricing.

[0066] The price update unit can analyze the history of price information fluctuations, predict price trends, and display them on the service. For example, the generation AI in the price update unit analyzes the history of price information fluctuations, predicts price trends, and displays them on the service. For example, future price fluctuations are predicted based on past price data. The price update unit can also use an algorithm to display price information based on price trends. For example, the generation AI analyzes price trends and displays them on the service. This makes it possible to predict price trends and display them on the service.

[0067] The price update unit can implement an algorithm that automatically adjusts the prices of related products when updating price information. For example, the price update unit implements an algorithm that automatically adjusts the prices of related products when updating price information. For example, if the price of tomatoes drops, the prices of related vegetables are also adjusted. The price update unit can also use an algorithm that adjusts price information based on the prices of related products. For example, the generation AI analyzes the prices of related products and adjusts the price information. This allows the prices of related products to be automatically adjusted.

[0068] The price update unit can use the emotion estimation function to collect customers' emotional reactions to price information updates and use it to improve the pricing strategy. The price update unit, for example, uses the emotion estimation function to collect customers' emotional reactions to price information updates and use it to improve the pricing strategy. For example, it analyzes customers' joy when a price is lowered. The price update unit can also use an algorithm to improve the pricing strategy based on customers' emotional reactions. For example, a generative AI analyzes customers' emotional reactions and improves the pricing strategy. This can be used to improve the pricing strategy.

[0069] The price update unit can update price information in real time, allowing customers to check the latest prices while they are in the store. For example, the price update unit can update price information in real time, allowing customers to check the latest prices while they are in the store. For example, the latest prices can be displayed on an in-store display. The price update unit can also use an algorithm that updates price information in real time. For example, a generation AI can update price information in real time and display the latest prices. This allows customers to check the latest prices while they are in the store.

[0070] The price update unit can build a system that automatically updates inventory information at the same time as updating price information. The price update unit builds a system that automatically updates inventory information at the same time as updating price information. For example, when the price is changed, the inventory quantity is automatically adjusted. The price update unit can also use an algorithm that updates price information based on inventory information. For example, a generation AI analyzes inventory information and updates the price information. This allows inventory information to be updated automatically.

[0071] The notification unit can have the generation AI analyze the customer's purchase history and automatically suggest the optimal notification price. For example, the notification unit can have the generation AI analyze the customer's purchase history and automatically suggest the optimal notification price. For example, it can suggest prices that the customer is likely to be interested in based on past purchase data. The notification unit can also use an algorithm that suggests a notification price based on the customer's purchase history. For example, the generation AI can analyze the customer's purchase history and suggest the optimal notification price. This makes it possible to automatically suggest the optimal notification price.

[0072] The notification unit can add a function to display a recommended price that takes into account the setting information of other customers when setting a notification price. The notification unit can add a function to display a recommended price that takes into account the setting information of other customers when setting a notification price. For example, the notification price of other customers for the same product can be displayed. The notification unit can also use an algorithm to display a recommended price based on the setting information of other customers. For example, a generation AI can analyze the setting information of other customers and display a recommended price. This makes it possible to add a function to display a recommended price.

[0073] The notification unit can use the emotion estimation function to analyze the emotional reactions of customers who receive the notification and use the results to improve the content of the notification. For example, the notification unit can use the emotion estimation function to analyze the emotional reactions of customers who receive the notification and use the results to improve the content of the notification. For example, it can analyze the joy or surprise of the customer when receiving the notification. The notification unit can also use an algorithm to improve the content of the notification based on the emotional reactions of the customer. For example, a generation AI can analyze the emotional reactions of the customer and improve the content of the notification. This can be used to improve the content of the notification.

[0074] The notification unit can set the notification price by voice input or gesture input. The notification unit can, for example, build a system that allows the notification price to be set by voice input. For example, a customer can set it by voice, saying, "Notify me when tomatoes drop below 90 yen." The notification unit can also build a system that allows the notification price to be set by gesture input. For example, a customer can set the notification price by gesture. This makes it possible to set the notification price by voice input or gesture input.

[0075] The notification unit can add a function to notify discount information for related products at the same time as setting the notification price. For example, when the price of tomatoes drops, the notification unit can also notify discount information for cucumbers. The notification unit can also use an algorithm to customize the content of the notification based on discount information for related products. For example, a generation AI can analyze discount information for related products and customize the content of the notification. This makes it possible to add a function to notify discount information for related products.

[0076] The notification unit can use the emotion estimation function to analyze the customer's emotions when setting the notification price and suggest the optimal notification timing. The notification unit can, for example, use the emotion estimation function to analyze the customer's emotions when setting the notification price and suggest the optimal notification timing. For example, sending a notification during a time when the customer is relaxed. The notification unit can also use an algorithm that suggests the notification timing based on the customer's emotions. For example, a generation AI analyzes the customer's emotions and suggests the optimal notification timing. This makes it possible to suggest the optimal notification timing.

[0077] The price update unit allows the generation AI to compare the price information in the store database with actual sales data and optimize pricing. For example, the generation AI compares the price information in the store database with actual sales data and optimizes pricing. For example, it adjusts prices based on sales data. The price update unit can also use an algorithm that optimizes pricing based on the price information and sales data in the store database. For example, the generation AI analyzes the price information and sales data and optimizes pricing. This allows for optimization of pricing.

[0078] The price update unit can compare the price information in the store database with price information from other stores to automatically set competitive prices. The price update unit, for example, builds a system that compares the price information in the store database with price information from other stores to automatically set competitive prices. For example, it adjusts prices based on the prices of nearby stores. The price update unit can also use an algorithm that sets competitive prices based on price information from other stores. For example, a generation AI analyzes price information from other stores and sets competitive prices. This makes it possible to automatically set competitive prices.

[0079] The price update unit can use the emotion estimation function to analyze customers' emotional reactions to price updates and use the results to improve the pricing strategy. The price update unit can, for example, use the emotion estimation function to analyze customers' emotional reactions to price updates and use the results to improve the pricing strategy. For example, it can analyze customers' joy when a price is lowered. The price update unit can also use an algorithm to improve the pricing strategy based on customers' emotional reactions. For example, a generative AI can analyze customers' emotional reactions and use the results to improve the pricing strategy. This can be useful for improving the pricing strategy.

[0080] The price update unit can add a function to automatically adjust the price information in the store database by comparing it with the market price for each region. The price update unit, for example, adds a function to automatically adjust the price information in the store database by comparing it with the market price for each region. For example, the price can be set based on the average price for each region. The price update unit can also use an algorithm to adjust the price information based on the market price for each region. For example, the generation AI analyzes the market price for each region and adjusts the price information. This allows automatic adjustment by comparing it with the market price for each region.

[0081] The price update unit can build a system that automatically updates the price information in the store database according to the season or event. The price update unit builds a system that automatically updates the price information in the store database according to the season or event, for example. For example, prices are adjusted to coincide with Christmas or New Year sales. The price update unit can also use an algorithm that updates price information based on the season or event. For example, a generation AI analyzes the season or event and updates the price information. This allows automatic updates according to the season or event.

[0082] The price update unit can use the emotion estimation function to analyze the emotional reactions of store staff to price updates, which can be useful in improving work efficiency. The price update unit can, for example, use the emotion estimation function to analyze the emotional reactions of store staff to price updates, which can be useful in improving work efficiency. For example, it can reduce the stress of price change work. The price update unit can also use an algorithm that improves work efficiency based on the emotional reactions of store staff. For example, a generation AI can analyze the emotional reactions of store staff and improve work efficiency. This can be useful in improving work efficiency.

[0083] The SNS upload unit uses a generation AI to analyze the content of a photo and automatically generate optimal hashtags and captions. For example, the SNS upload unit uses a generation AI to analyze the content of a photo and automatically generate optimal hashtags and captions. For example, hashtags may be suggested based on product and price information in the photo. The SNS upload unit can also use an algorithm to generate hashtags and captions based on the content of the photo. For example, the generation AI analyzes the content of the photo and generates optimal hashtags and captions. This makes it possible to automatically generate optimal hashtags and captions.

[0084] The SNS upload unit can add a function to automatically apply filters and effects to photos when uploading to SNS. The SNS upload unit can add a function to automatically apply filters and effects to photos when uploading to SNS. For example, the SNS upload unit can automatically adjust the color tone and brightness of photos. The SNS upload unit can also use an algorithm to improve the appearance of photos based on filters and effects. For example, a generative AI analyzes the content of the photo and applies the most appropriate filter or effect. This makes it possible to automatically apply filters and effects.

[0085] The SNS upload unit can use the emotion estimation function to analyze followers' emotional reactions to SNS posts and use the results to improve the content of the posts. The SNS upload unit can, for example, use the emotion estimation function to analyze followers' emotional reactions to SNS posts and use the results to improve the content of the posts. For example, it can analyze followers' joy or surprise at the posts. The SNS upload unit can also use an algorithm to improve the content of the posts based on followers' emotional reactions. For example, a generation AI can analyze followers' emotional reactions and improve the content of the posts. This can be used to improve the content of the posts.

[0086] The SNS upload unit can be equipped with a function that automatically adds store promotional information when uploading photos to SNS. The SNS upload unit can be equipped with a function that automatically adds store promotional information when uploading photos to SNS. For example, sale information or new product introductions can be posted along with the photos. The SNS upload unit can also use an algorithm that customizes the content of the photos based on the promotional information. For example, a generation AI analyzes the promotional information and customizes the content of the photos. This makes it possible to automatically add store promotional information.

[0087] The SNS upload unit can use the emotion estimation function to monitor followers' emotional reactions to SNS posts in real time and optimize the posting strategy. The SNS upload unit can, for example, use the emotion estimation function to monitor followers' emotional reactions to SNS posts in real time and optimize the posting strategy. For example, it can analyze followers' joy or surprise at the posts. The SNS upload unit can also use an algorithm that optimizes the posting strategy based on followers' emotional reactions. For example, a generation AI can analyze followers' emotional reactions and optimize the posting strategy. This can optimize the posting strategy.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The price notification system can further include a voice guidance unit. The voice guidance unit, for example, provides the latest price information by voice through speakers in the store. For example, when a particular product is reduced in price, this information is announced in the store. The voice guidance unit can also respond with the latest price information when a customer asks a question about a particular product by voice. For example, if a customer asks, "What is the price of tomatoes?", the voice guidance unit will respond, "Tomatoes are currently 100 yen." This makes it possible to provide the latest price information not only visually but also aurally.

[0090] The price analysis unit can further use a temperature sensor to analyze the storage conditions of products and improve the reliability of price information. For example, it can analyze the temperature of refrigerated products to check whether they are stored properly. It can also use a temperature sensor to adjust price information according to temperature changes in the store. For example, if the temperature rises due to a refrigerator malfunction, it can discount the price of the product. This makes it possible to improve the reliability of price information while maintaining the quality of the product.

[0091] The price analysis unit can use the emotion estimation function to analyze the customer's facial expression and optimize the way price information is displayed. For example, if a customer looks surprised when they see the price, the unit can provide advice on how to make the price display easier to understand. It can also adjust the font size and color of the price display based on the customer's facial expression. For example, if a customer looks dissatisfied when they see the price, the unit can increase the font size to make it easier to read. This makes it possible to display prices optimally according to the customer's emotions.

[0092] The price notification system may further include a location information acquisition unit. The location information acquisition unit acquires in-store location information using, for example, the GPS of the customer's smartphone. For example, if a customer is near a specific product shelf, the unit notifies the customer of the latest price of that product. The location information acquisition unit can also analyze the customer's movement path and notify the customer of price information at the optimal time. For example, if a specific product is reduced in price while the customer is on their way to the cash register, the unit notifies the customer of this information. This makes it possible to provide optimal price information based on the customer's location information.

[0093] The price analysis unit can use the emotion estimation function to analyze the tone of a customer's voice and improve the reliability of price information. For example, it can analyze the tone of a customer's voice when asking about prices, and provide detailed price information if the customer has doubts or concerns. It can also adjust the way price information is displayed based on the customer's tone of voice. For example, if a customer is dissatisfied with the price, it can display additional information about the price rationale and discount information. This makes it possible to provide optimal price information according to the customer's emotions.

[0094] The price notification system can further include an inventory management unit. The inventory management unit, for example, monitors the inventory status in the store in real time and links it with price information. For example, it can raise the price of products that are low in stock, or lower the price of products that are in abundant stock. The inventory management unit can also analyze product sales and adjust price information. For example, if a particular product remains unsold, it can discount the price of that product. This makes it possible to set optimal prices according to the inventory status.

[0095] The price analysis unit uses the emotion estimation function to analyze customer purchasing intent and can use this information to optimize pricing. For example, if a customer shows interest in a particular product, the price of that product can be adjusted. It is also possible to create a pricing strategy based on the customer's purchasing intent. For example, if a customer shows a high level of purchase intent for a particular product, the price of that product can be slightly increased. This makes it possible to set optimal prices according to the customer's purchasing intent.

[0096] The price notification system can further include a review analysis unit. The review analysis unit, for example, analyzes online reviews and posts on social media to understand product ratings and popularity. For example, if a particular product has received high ratings, the price of that product can be adjusted. The review analysis unit can also display price information based on product ratings. For example, for highly rated products, some of the reviews can be displayed along with price information. This allows optimal pricing to be achieved based on customer ratings.

[0097] The price analysis unit uses the emotion estimation function to analyze a customer's purchasing history and use the information to customize price information. For example, it can provide individually customized price information based on the price information of products the customer has purchased in the past. It can also provide discount information for specific products based on the customer's purchasing history. For example, it can present special discount prices for products that the customer frequently purchases. This makes it possible to provide optimal price information based on the customer's purchasing history.

[0098] The price notification system may further include a weather information acquisition unit. The weather information acquisition unit, for example, acquires current weather information and links it with price information. For example, it may lower the price of cold drinks on a hot day, or lower the price of hot drinks on a cold day. The weather information acquisition unit may also adjust price information based on weather forecast information. For example, if rain is forecast for the next day, it may raise the price of umbrellas. This allows optimal pricing to be set according to weather information.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The photo acquisition unit acquires a photo of the entire storefront. For example, a smartphone or digital camera can be used to take a photo of the entire storefront. The photo acquisition unit can also capture a photo of a specific area within the store. For example, a specific product shelf or display can be photographed. Step 2: The price analysis unit analyzes the price information from the photos acquired by the photo acquisition unit. For example, the generation AI analyzes the price cards in the photos and reads the price of each product. The generation AI can also analyze the shape and color of the products to improve the reliability of the price information. For example, it can analyze the shape and color of a tomato to confirm that it matches the price on the price card. Step 3: The price update unit updates the price information analyzed by the price analysis unit on the service. For example, the price of each product is automatically updated on the service based on the price information read by the generation AI. The price update unit can also update the price on the photo to the latest based on price information in the store database. For example, if the price card in the store displays "Tomato 120 yen," but the store database lists it as "Tomato 100 yen," the generation AI will update the price on the photo to 100 yen. Step 4: The notification unit notifies the customer who set the notification price. For example, if a customer sets a notification when tomatoes fall below 90 yen, the customer will receive a notification when the AI ​​generator detects that the price of tomatoes falls below 90 yen.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo acquisition unit that acquires photos of the entire storefront; a price analysis unit that analyzes price information from the photograph acquired by the photograph acquisition unit; a price update unit that updates the price information analyzed by the price analysis unit on the service; A notification unit that notifies the customer of the set notification price. A system characterized by:

2. The price analysis unit Automatically corrects lighting conditions and shooting angles in stores to improve price reading accuracy The system of claim 1 .

3. The photo acquisition unit We filmed a video of the entire store, The price analysis unit Extracting the price information from a plurality of the frames in the video. The system of claim 1 .

4. The price update unit Analyzing the fluctuation history of the price information, predicting price trends, and displaying them on the service The system of claim 1 .

5. The notification unit Generative AI analyzes customer purchase history and automatically suggests the optimal notification price. The system of claim 1 .

6. The price update unit The generation AI compares the price information in the store database with actual sales data to optimize pricing. The system of claim 1 .

7. The SNS upload unit Generative AI analyzes the content of the photo and automatically generates optimal hashtags and captions. The system of claim 1 .

8. The price analysis unit Using the emotion estimation function, the emotion of the user who took the photo is analyzed, and photography advice is provided to bring out the positive emotion. The system of claim 1 .

Images