system

Abstract

An object of a system according to an embodiment is to efficiently acquire purchase details in a real store.SOLUTION: A system according to an embodiment includes an OCR reading unit. The OCR reading unit accurately extracts information on the product name, the price, and the purchase date and time described on the receipt.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 obtain purchase details from physical stores.

[0005] The system according to the embodiment aims to efficiently obtain purchase details from a physical store. [Means for solving the problem]

[0006] The system according to the embodiment includes an OCR reader that accurately extracts information about the product name, price, and purchase date and time from the receipt. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently obtain purchase details from a physical store. [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 electronic payment system according to an embodiment of the present invention allows users to register their purchase details at brick-and-mortar stores and easily read receipts using OCR technology. This allows the electronic payment system to efficiently collect purchase data and effectively utilize receipts that would normally be discarded.

[0029] The electronic payment system according to the embodiment includes a receipt registration unit and an OCR reader. The receipt registration unit allows users to register purchase details from brick-and-mortar stores. For example, the app allows users to take a photo of their receipt after a purchase and upload it. The receipt registration unit can also provide points or cashback rewards each time a user registers a receipt. For example, the app provides a system where points are awarded each time a user registers a receipt. The OCR reader uses OCR technology to read receipts registered by the receipt registration unit. For example, the OCR reader accurately extracts information such as the product name, price, and purchase date and time from the receipt. The OCR reader can also automatically correct physical defects such as stains and creases on receipts. For example, image processing technology can be used to remove stains and correct creases. This allows the electronic payment system to efficiently collect purchase data and effectively utilize receipts that would normally be discarded. For example, users can earn points by registering receipts and receive personalized services based on their purchase data. Furthermore, brick-and-mortar store managers can use purchase data to increase sales and improve their operations.

[0030] The receipt registration unit allows users to take a photo of a receipt after a purchase and upload it within the app. The receipt registration unit provides a function that allows users to take a photo of a receipt with their smartphone camera after a purchase and upload it within the app. For example, the app can automatically correct the image and perform character recognition. In addition, when a user registers a receipt, the receipt registration unit can use an emotion estimation function to estimate the user's emotion and provide an interface to elicit positive emotions. For example, the receipt registration unit can be equipped with a function that analyzes the user's facial expressions and voice and estimates their emotion in real time. This allows users to easily provide purchase data.

[0031] The OCR reader can accurately extract information such as the product name, price, and purchase date and time from a receipt. For example, the OCR reader can use OCR technology to accurately extract information such as the product name, price, and purchase date and time from a receipt. For example, it can use OCR technology to analyze the letters and numbers on the receipt and convert them into digital data. The OCR reader can also provide a function that allows users to simultaneously input reviews and ratings of the purchased product when registering the receipt. For example, it can provide a form where users can input reviews and ratings of the purchased product. This allows receipt information to be accurately converted into digital data.

[0032] The receipt registration unit can provide points or cash back each time a user registers a receipt. For example, the receipt registration unit can provide points or cash back each time a user registers a receipt. For example, it can provide a system whereby points are awarded each time a user registers a receipt. The receipt registration unit can also provide a function that allows a user to share a receipt with other users after registering the receipt. For example, it can provide a share button so that the receipt can be easily shared on social media or a messaging app. This can encourage users to actively register receipts.

[0033] The OCR reader can analyze purchase data to understand a user's purchasing trends and preferences. For example, the OCR reader can analyze purchase data based on receipt information read using OCR technology to understand a user's purchasing trends and preferences. For example, it can analyze the frequency of purchases and purchasing trends by category. The OCR reader can also provide a function that allows receipt information to be registered using voice input when registering a receipt. For example, the contents of a receipt can be input by voice using a microphone and converted into text. This allows for personalized advertisements and promotions.

[0034] The OCR reader can anonymize or encrypt data to prevent unauthorized access to third parties. For example, the OCR reader can anonymize or encrypt data to prevent unauthorized access to third parties. For example, it can delete personal information and mask the data. The OCR reader can also provide a function that automatically provides related information (e.g., recipes and usage instructions) about products purchased by a user when registering a receipt. For example, it can suggest recipes based on the ingredients purchased. This protects the user's privacy.

[0035] The receipt registration unit can provide a function that allows users to simultaneously input reviews or ratings of purchased products. The receipt registration unit, for example, provides a form that allows users to input reviews and ratings of purchased products. For example, it provides fields for inputting product names, rating scores, and comments. It also provides a function that allows users to post reviews of purchased products at the same time as registering receipts. For example, it provides a review posting button to allow users to easily enter feedback. This allows for centralized management of purchase data and user feedback.

[0036] The receipt registration unit can provide a function that allows a user to share a receipt with other users after registering it. For example, the receipt registration unit provides a function that allows a user to share a receipt with other users after registering it. For example, a share button can be provided to enable easy sharing via social media or messaging apps. The receipt registration unit also provides a function that allows receipt information to be shared within a community and receives comments and ratings from other users. For example, comments can be posted on shared receipts. This can promote information exchange between users.

[0037] The receipt registration unit can provide a function that allows receipt information to be registered using voice input. For example, the receipt registration unit provides a function that allows receipt information to be registered using voice input. For example, the contents of the receipt can be input by voice using a microphone and converted into text. Also, a system can be built using voice recognition technology that allows users to input receipt information by voice. For example, product names and prices can be input using voice commands. This improves user convenience.

[0038] The receipt registration unit can provide a function to automatically provide related information about products purchased by a user. The receipt registration unit provides a function to automatically provide related information about products purchased by a user. For example, it can suggest recipes based on the ingredients purchased. It can also analyze receipt information and build a system that provides usage and maintenance information for purchased products. For example, it can display a usage guide for a home appliance. This improves user convenience.

[0039] The OCR reading unit can automatically classify receipt information read using OCR technology using generation AI to organize the user's purchase history. The OCR reading unit, for example, automatically classifies receipt information read using OCR technology to build a system that organizes the user's purchase history. For example, it can classify by product category. In addition, it uses generation AI to analyze the receipt information read using OCR technology and automatically organize the purchase history. For example, it can classify based on purchase frequency or amount. This allows the purchase history to be organized efficiently.

[0040] The OCR reading unit improves OCR technology to automatically correct physical defects such as stains or creases on receipts. For example, the OCR reading unit improves OCR technology to add a function to automatically correct physical defects such as stains or creases on receipts. For example, it removes stains using image processing technology. It also develops an algorithm to correct physical defects on receipts and incorporates it into the OCR technology. For example, it detects and corrects creases and wrinkles. This makes it possible to correct physical defects on receipts.

[0041] The OCR reading unit can analyze a user's purchasing patterns based on receipt information read using OCR technology and predict future purchases. The OCR reading unit, for example, builds a system that analyzes a user's purchasing patterns based on receipt information read using OCR technology. For example, it analyzes purchase frequency and trends in purchased products. It also adds a function to analyze purchasing patterns and predict future purchases. For example, it predicts the next purchase time and purchased products based on past purchase data. This makes it possible to predict future purchases.

[0042] The OCR reading unit can automatically translate receipt information read using OCR technology into different languages, enabling data analysis from an international perspective. For example, the OCR reading unit can build a system that automatically translates receipt information read using OCR technology into different languages. For example, it can translate into multiple languages ​​such as English and Chinese. It can also perform data analysis from an international perspective based on the automatically translated receipt information. For example, it can integrate receipt information in different languages ​​to analyze global purchasing trends. This makes data analysis possible from an international perspective.

[0043] The OCR reader converts the receipt information scanned using OCR technology into a visual note or mind map, making it easier to understand visually. The OCR reader, for example, builds a system that converts the receipt information scanned using OCR technology into a visual note. For example, it displays important information using diagrams and icons. It also converts receipt information into a mind map format, adding a function for visually organizing it. For example, it visually displays related keywords and concepts. This makes the receipt information easier to understand visually.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The receipt registration unit can also automatically provide related information about purchased products when a user registers a receipt. For example, it can add a function to suggest recipes based on the ingredients purchased. It can also provide usage and maintenance information for purchased products. For example, it can display a usage guide or maintenance schedule for a home appliance. It can also provide a function to share reviews and ratings of purchased products with other users. For example, it can provide a review posting button to make it easy to enter feedback. This can improve the user's purchasing experience.

[0046] The OCR reader can provide a function to automatically translate receipt information scanned using OCR technology into different languages. For example, it can translate into multiple languages, such as English and Chinese. It can also perform data analysis from an international perspective based on the automatically translated receipt information. For example, it can integrate receipt information in different languages ​​to analyze global purchasing trends. It can also provide a function to share translated receipt information with other users. This enables data analysis from an international perspective.

[0047] The OCR reader can provide a function to convert the receipt information scanned using OCR technology into a visual note or mind map. For example, it can display important information using diagrams or icons. It can also add a function to convert the receipt information into a mind map format and visually organize it. For example, it can visually display related keywords and concepts. It can also provide a function to share the visual note or mind map with other users. This makes the receipt information easier to understand visually.

[0048] The OCR reader can provide a function to automatically classify receipt information read using OCR technology using generation AI and organize a user's purchase history. For example, by product category. The generation AI can also analyze receipt information read using OCR technology and automatically organize purchase history. For example, by purchase frequency or amount. Furthermore, it can provide a function to share purchase history with other users. This allows for efficient organization of purchase history.

[0049] The OCR reader can provide a function to automatically translate receipt information scanned using OCR technology into different languages. For example, it can translate into multiple languages, such as English and Chinese. It can also perform data analysis from an international perspective based on the automatically translated receipt information. For example, it can integrate receipt information in different languages ​​to analyze global purchasing trends. It can also provide a function to share translated receipt information with other users. This enables data analysis from an international perspective.

[0050] The OCR reader can provide a function to convert the receipt information scanned using OCR technology into a visual note or mind map. For example, it can display important information using diagrams or icons. It can also add a function to convert the receipt information into a mind map format and visually organize it. For example, it can visually display related keywords and concepts. It can also provide a function to share the visual note or mind map with other users. This makes the receipt information easier to understand visually.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The OCR reader uses OCR technology to read the receipt registered by the receipt registration unit. Specifically, it accurately extracts information such as the product name, price, and purchase date and time written on the receipt. The OCR reader can also automatically correct physical defects on the receipt, such as stains or creases. For example, it can use image processing technology to remove stains and straighten creases.

[0053] (Example 2) The electronic payment system according to an embodiment of the present invention allows users to register their purchase details at brick-and-mortar stores and easily read receipts using OCR technology. This allows the electronic payment system to efficiently collect purchase data and effectively utilize receipts that would normally be discarded.

[0054] The electronic payment system according to the embodiment includes a receipt registration unit and an OCR reader. The receipt registration unit allows users to register purchase details from brick-and-mortar stores. For example, the app allows users to take a photo of their receipt after a purchase and upload it. The receipt registration unit can also provide points or cashback rewards each time a user registers a receipt. For example, the app provides a system where points are awarded each time a user registers a receipt. The OCR reader uses OCR technology to read receipts registered by the receipt registration unit. For example, the OCR reader accurately extracts information such as the product name, price, and purchase date and time from the receipt. The OCR reader can also automatically correct physical defects such as stains and creases on receipts. For example, image processing technology can be used to remove stains and correct creases. This allows the electronic payment system to efficiently collect purchase data and effectively utilize receipts that would normally be discarded. For example, users can earn points by registering receipts and receive personalized services based on their purchase data. Furthermore, brick-and-mortar store managers can use purchase data to increase sales and improve their operations.

[0055] The receipt registration unit allows users to take a photo of a receipt after a purchase and upload it within the app. The receipt registration unit provides a function that allows users to take a photo of a receipt with their smartphone camera after a purchase and upload it within the app. For example, the app can automatically correct the image and perform character recognition. In addition, when a user registers a receipt, the receipt registration unit can use an emotion estimation function to estimate the user's emotion and provide an interface to elicit positive emotions. For example, the receipt registration unit can be equipped with a function that analyzes the user's facial expressions and voice and estimates their emotion in real time. This allows users to easily provide purchase data.

[0056] The OCR reader can accurately extract information such as the product name, price, and purchase date and time from a receipt. For example, the OCR reader can use OCR technology to accurately extract information such as the product name, price, and purchase date and time from a receipt. For example, it can use OCR technology to analyze the letters and numbers on the receipt and convert them into digital data. The OCR reader can also provide a function that allows users to simultaneously input reviews and ratings of the purchased product when registering the receipt. For example, it can provide a form where users can input reviews and ratings of the purchased product. This allows receipt information to be accurately converted into digital data.

[0057] The receipt registration unit can provide points or cash back each time a user registers a receipt. For example, the receipt registration unit can provide points or cash back each time a user registers a receipt. For example, it can provide a system whereby points are awarded each time a user registers a receipt. The receipt registration unit can also provide a function that allows a user to share a receipt with other users after registering the receipt. For example, it can provide a share button so that the receipt can be easily shared on social media or a messaging app. This can encourage users to actively register receipts.

[0058] The OCR reader can analyze purchase data to understand a user's purchasing trends and preferences. For example, the OCR reader can analyze purchase data based on receipt information read using OCR technology to understand a user's purchasing trends and preferences. For example, it can analyze the frequency of purchases and purchasing trends by category. The OCR reader can also provide a function that allows receipt information to be registered using voice input when registering a receipt. For example, the contents of a receipt can be input by voice using a microphone and converted into text. This allows for personalized advertisements and promotions.

[0059] The OCR reader can anonymize or encrypt data to prevent unauthorized access to third parties. For example, the OCR reader can anonymize or encrypt data to prevent unauthorized access to third parties. For example, it can delete personal information and mask the data. The OCR reader can also provide a function that automatically provides related information (e.g., recipes and usage instructions) about products purchased by a user when registering a receipt. For example, it can suggest recipes based on the ingredients purchased. This protects the user's privacy.

[0060] The receipt registration unit can use generative AI to estimate the user's emotions and provide an interface for eliciting positive emotions. The receipt registration unit can, for example, use generative AI to estimate the user's emotions and provide an interface for eliciting positive emotions. For example, it can be equipped with a function for analyzing the user's facial expressions and voice and estimating emotions in real time. It can also use the emotion estimation function to provide an interface for eliciting positive emotions when the user registers a receipt. For example, it can present encouraging messages or success stories. This can elicit positive emotions from the user.

[0061] The receipt registration unit can provide a function that allows users to simultaneously input reviews or ratings of purchased products. The receipt registration unit, for example, provides a form that allows users to input reviews and ratings of purchased products. For example, it provides fields for inputting product names, rating scores, and comments. It also provides a function that allows users to post reviews of purchased products at the same time as registering receipts. For example, it provides a review posting button to allow users to easily enter feedback. This allows for centralized management of purchase data and user feedback.

[0062] The receipt registration unit can provide a function that allows a user to share a receipt with other users after registering it. For example, the receipt registration unit provides a function that allows a user to share a receipt with other users after registering it. For example, a share button can be provided to enable easy sharing via social media or messaging apps. The receipt registration unit also provides a function that allows receipt information to be shared within a community and receives comments and ratings from other users. For example, comments can be posted on shared receipts. This can promote information exchange between users.

[0063] The receipt registration unit can provide a function that allows receipt information to be registered using voice input. For example, the receipt registration unit provides a function that allows receipt information to be registered using voice input. For example, the contents of the receipt can be input by voice using a microphone and converted into text. Also, a system can be built using voice recognition technology that allows users to input receipt information by voice. For example, product names and prices can be input using voice commands. This improves user convenience.

[0064] The receipt registration unit can provide a function to automatically provide related information about products purchased by a user. The receipt registration unit provides a function to automatically provide related information about products purchased by a user. For example, it can suggest recipes based on the ingredients purchased. It can also analyze receipt information and build a system that provides usage and maintenance information for purchased products. For example, it can display a usage guide for a home appliance. This improves user convenience.

[0065] The receipt registration unit can use the emotion estimation function to analyze the emotion a user has when registering a receipt and provide incentives to elicit positive emotions. The receipt registration unit, for example, uses the emotion estimation function to analyze the emotion a user has when registering a receipt in real time. For example, it analyzes facial expressions and voice and calculates an emotion score. In addition, a system is constructed that analyzes the user's emotion and provides incentives to elicit positive emotions. For example, bonus points are awarded if the emotion score is high. This makes it possible to elicit positive emotions from the user.

[0066] The OCR reading unit can automatically classify receipt information read using OCR technology using generation AI to organize the user's purchase history. The OCR reading unit, for example, automatically classifies receipt information read using OCR technology to build a system that organizes the user's purchase history. For example, it can classify by product category. In addition, it uses generation AI to analyze the receipt information read using OCR technology and automatically organize the purchase history. For example, it can classify based on purchase frequency or amount. This allows the purchase history to be organized efficiently.

[0067] The OCR reading unit improves OCR technology to automatically correct physical defects such as stains or creases on receipts. For example, the OCR reading unit improves OCR technology to add a function to automatically correct physical defects such as stains or creases on receipts. For example, it removes stains using image processing technology. It also develops an algorithm to correct physical defects on receipts and incorporates it into the OCR technology. For example, it detects and corrects creases and wrinkles. This makes it possible to correct physical defects on receipts.

[0068] The OCR reading unit can analyze a user's purchasing patterns based on receipt information read using OCR technology and predict future purchases. The OCR reading unit, for example, builds a system that analyzes a user's purchasing patterns based on receipt information read using OCR technology. For example, it analyzes purchase frequency and trends in purchased products. It also adds a function to analyze purchasing patterns and predict future purchases. For example, it predicts the next purchase time and purchased products based on past purchase data. This makes it possible to predict future purchases.

[0069] The OCR reading unit can automatically translate receipt information read using OCR technology into different languages, enabling data analysis from an international perspective. For example, the OCR reading unit can build a system that automatically translates receipt information read using OCR technology into different languages. For example, it can translate into multiple languages ​​such as English and Chinese. It can also perform data analysis from an international perspective based on the automatically translated receipt information. For example, it can integrate receipt information in different languages ​​to analyze global purchasing trends. This makes data analysis possible from an international perspective.

[0070] The OCR reader converts the receipt information scanned using OCR technology into a visual note or mind map, making it easier to understand visually. The OCR reader, for example, builds a system that converts the receipt information scanned using OCR technology into a visual note. For example, it displays important information using diagrams and icons. It also converts receipt information into a mind map format, adding a function for visually organizing it. For example, it visually displays related keywords and concepts. This makes the receipt information easier to understand visually.

[0071] The OCR reading unit uses the emotion estimation function to collect users' emotional responses to receipt information read by OCR technology, and can use this data to improve the accuracy of the OCR technology. For example, the OCR reading unit uses the emotion estimation function to build a system that collects users' emotional responses to receipt information read by OCR technology. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. In addition, an algorithm is developed to improve the accuracy of the OCR technology based on the user's emotional response data. For example, if the emotion score is low, the OCR technology is improved. This can improve the accuracy of the OCR technology.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The receipt registration unit can also automatically provide related information about purchased products when a user registers a receipt. For example, it can add a function to suggest recipes based on the ingredients purchased. It can also provide usage and maintenance information for purchased products. For example, it can display a usage guide or maintenance schedule for a home appliance. It can also provide a function to share reviews and ratings of purchased products with other users. For example, it can provide a review posting button to make it easy to enter feedback. This can improve the user's purchasing experience.

[0074] The OCR reader can provide a function to automatically translate receipt information scanned using OCR technology into different languages. For example, it can translate into multiple languages, such as English and Chinese. It can also perform data analysis from an international perspective based on the automatically translated receipt information. For example, it can integrate receipt information in different languages ​​to analyze global purchasing trends. It can also provide a function to share translated receipt information with other users. This enables data analysis from an international perspective.

[0075] The receipt registration unit can use an emotion estimation function to estimate the user's emotion when the user registers a receipt, and provide an interface to elicit positive emotions. For example, it can be equipped with a function to analyze the user's facial expressions and voice and estimate emotions in real time. The emotion estimation function can also be used to provide incentives to elicit positive emotions when the user registers a receipt. For example, bonus points can be awarded if the emotion score is high. This can elicit positive emotions from the user.

[0076] The OCR reader can provide a function to convert the receipt information scanned using OCR technology into a visual note or mind map. For example, it can display important information using diagrams or icons. It can also add a function to convert the receipt information into a mind map format and visually organize it. For example, it can visually display related keywords and concepts. It can also provide a function to share the visual note or mind map with other users. This makes the receipt information easier to understand visually.

[0077] The receipt registration unit can use an emotion estimation function to estimate the user's emotion when the user registers a receipt, and provide an interface to elicit positive emotions. For example, it can be equipped with a function to analyze the user's facial expressions and voice and estimate emotions in real time. The emotion estimation function can also be used to provide incentives to elicit positive emotions when the user registers a receipt. For example, bonus points can be awarded if the emotion score is high. This can elicit positive emotions from the user.

[0078] The OCR reader can provide a function to automatically classify receipt information read using OCR technology using generation AI and organize a user's purchase history. For example, by product category. The generation AI can also analyze receipt information read using OCR technology and automatically organize purchase history. For example, by purchase frequency or amount. Furthermore, it can provide a function to share purchase history with other users. This allows for efficient organization of purchase history.

[0079] The receipt registration unit can use an emotion estimation function to estimate the user's emotion when the user registers a receipt, and provide an interface to elicit positive emotions. For example, it can be equipped with a function to analyze the user's facial expressions and voice and estimate emotions in real time. The emotion estimation function can also be used to provide incentives to elicit positive emotions when the user registers a receipt. For example, bonus points can be awarded if the emotion score is high. This can elicit positive emotions from the user.

[0080] The OCR reader can provide a function to automatically translate receipt information scanned using OCR technology into different languages. For example, it can translate into multiple languages, such as English and Chinese. It can also perform data analysis from an international perspective based on the automatically translated receipt information. For example, it can integrate receipt information in different languages ​​to analyze global purchasing trends. It can also provide a function to share translated receipt information with other users. This enables data analysis from an international perspective.

[0081] The receipt registration unit can use an emotion estimation function to estimate the user's emotion when the user registers a receipt, and provide an interface to elicit positive emotions. For example, it can be equipped with a function to analyze the user's facial expressions and voice and estimate emotions in real time. The emotion estimation function can also be used to provide incentives to elicit positive emotions when the user registers a receipt. For example, bonus points can be awarded if the emotion score is high. This can elicit positive emotions from the user.

[0082] The OCR reader can provide a function to convert the receipt information scanned using OCR technology into a visual note or mind map. For example, it can display important information using diagrams or icons. It can also add a function to convert the receipt information into a mind map format and visually organize it. For example, it can visually display related keywords and concepts. It can also provide a function to share the visual note or mind map with other users. This makes the receipt information easier to understand visually.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The OCR reader uses OCR technology to read the receipt registered by the receipt registration unit. Specifically, it accurately extracts information such as the product name, price, and purchase date and time written on the receipt. The OCR reader can also automatically correct physical defects on the receipt, such as stains or creases. For example, it can use image processing technology to remove stains and straighten creases.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[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 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.

[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 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.

[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 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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, in order to avoid confusion and to 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.

[0151] 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]

[0152] 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. The OCR reading unit Accurately extract the product name, price, and purchase date and time information written on the receipt 2. The system of claim 1.

2. The receipt registration unit After making a purchase, the user takes a photo of the receipt and uploads it within the app.

2. The system of claim 1.

3. The OCR reading unit Analyze the purchasing data to understand users' purchasing trends and preferences 2. The system of claim 1.

4. The OCR reading unit The receipt information read using OCR technology is automatically classified using AI generation, providing a function to organize the user's purchase history.

2. The system of claim 1.

5. The receipt registration unit Provide a function that uses generative AI to estimate the user's emotions and provide an interface to elicit positive emotions.

2. The system of claim 1.

6. The receipt registration unit Using an emotion estimation function, the emotion of the user when registering a receipt is analyzed, and a function is provided to provide incentives to elicit positive emotions.

2. The system of claim 1.

7. The OCR reading unit Provides a function that uses OCR technology to automatically translate the receipt information into different languages, enabling data analysis from an international perspective.

2. The system of claim 1.

8. The OCR reading unit Using an emotion estimation function, the system collects the user's emotional response to the receipt information read by OCR technology, and provides a function to improve the accuracy of the OCR technology based on that data.

2. The system of claim 1.

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