System

Abstract

An object of a system according to an embodiment is to centrally manage purchase histories of real shops and online shops and to perform effective suggestion and advertisement distribution.SOLUTION: A system according to an embodiment includes a purchase history management unit, an analysis unit, a suggestion unit, and an advertisement distribution unit. The purchase history management unit centrally manages purchase histories of the real stores and the online stores. The analysis unit analyzes the purchase history managed by the purchase history management unit. The suggestion unit suggests purchase based on the purchase history analyzed by the analysis unit. The advertisement distribution unit distributes an advertisement based on the purchase history.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] With conventional technology, it was difficult to centrally manage purchase history from both physical and online stores, which meant that effective suggestions and ad delivery based on purchase history were not being fully implemented.

[0005] The system according to the embodiment aims to centrally manage purchase histories from brick-and-mortar stores and online stores, and to provide effective suggestions and advertisement delivery. [Means for solving the problem]

[0006] The system according to the embodiment includes a purchase history management unit, an analysis unit, a suggestion unit, and an advertisement distribution unit. The purchase history management unit centrally manages purchase histories from brick-and-mortar stores and online stores. The analysis unit analyzes the purchase history managed by the purchase history management unit. The suggestion unit makes purchase suggestions based on the purchase history analyzed by the analysis unit. The advertisement distribution unit distributes advertisements based on the purchase history. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage purchase histories from brick-and-mortar stores and online stores, and can provide effective suggestions and advertisement delivery. [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) A purchase history management system according to an embodiment of the present invention is a system that centrally manages purchase histories from brick-and-mortar stores and online stores, generates purchase suggestions using a generation AI, and delivers advertisements based on the purchase data. This allows users to easily check their past purchase history and receive purchase suggestions from the AI. Furthermore, the operating company can monetize the advertisements delivered based on the purchase data.

[0029] A purchase history management system according to an embodiment includes a purchase history management unit, an analysis unit, a suggestion unit, and an advertisement distribution unit. The purchase history management unit centrally manages purchase histories from brick-and-mortar stores and online stores. For example, a user can view the history of products purchased at brick-and-mortar stores and online stores on a single screen. The purchase history management unit classifies purchase histories not only chronologically but also based on the purchase location and the buyer's behavioral patterns, allowing users to search the history based on specific criteria. For example, when searching for products purchased at a specific store, a user can specify the purchase location and time period. The analysis unit analyzes the purchase history managed by the purchase history management unit. For example, the analysis unit implements an algorithm that predicts a user's purchasing trends based on the purchase history. The analysis unit also uses an emotion estimation function to record the emotions a user felt at the time of purchase and filter the purchase history based on the emotions. For example, the analysis unit identifies products for which the user felt positive emotions when purchasing and preferentially displays those products. The suggestion unit makes purchase suggestions based on the purchase history analyzed by the analysis unit. For example, the analysis unit predicts when a user will purchase daily necessities and notifies the user of the timing of repurchases. The suggestion unit also suggests unpurchased products that the user may be interested in. For example, it identifies and suggests products that the user may be interested in based on the user's past purchase history and emotional data. The advertisement distribution unit distributes advertisements based on the purchase history. For example, it displays advertisements related to products the user has previously purchased. When distributing advertisements, the advertisement distribution unit also takes into account the user's browsing history and search history in addition to the user's purchase history, thereby distributing more accurate advertisements. For example, it distributes relevant advertisements based on keywords the user has previously searched for and web pages the user has viewed. As a result, the purchase history management system according to the embodiment allows users to easily check their past purchase history and receive purchase suggestions based on AI. Furthermore, the operating company can generate revenue by distributing advertisements based on purchase data.

[0030] The purchase history management unit allows a user to check the history of products purchased at real stores and online stores on one screen. The purchase history management unit allows a user to check the history of products purchased at real stores and online stores on one screen. For example, when a user searches for products purchased at a specific store, the user can specify the purchase location and time period. This allows a user to check the purchase history from real stores and online stores in a unified manner.

[0031] The suggestion unit can predict when the user will purchase daily necessities based on the purchase history and notify the user of the timing for repurchase. For example, the suggestion unit predicts when the user will purchase daily necessities based on the purchase history and notifies the user of the timing for repurchase. For example, if the user tends to purchase a specific product regularly, the suggestion unit automatically adds the product to the next purchase list. This allows the user to avoid missing the timing for repurchasing daily necessities and purchase them at the optimal timing.

[0032] The suggestion unit can suggest unpurchased products that the user may be interested in based on the purchase history. The suggestion unit suggests unpurchased products that the user may be interested in based on, for example, the purchase history. For example, the suggestion unit identifies and suggests products that the user may be interested in based on the user's past purchase history and emotional data. This improves the user's purchasing experience by suggesting new products that the user may be interested in.

[0033] The advertisement distribution unit can display advertisements related to products previously purchased by the user based on the purchase history. For example, the advertisement distribution unit displays advertisements related to products previously purchased by the user based on the purchase history. For example, if the user has purchased cosmetics from a specific brand, new products from that brand are suggested. This allows advertisements that are highly relevant to the user to be displayed, thereby increasing the effectiveness of the advertisements.

[0034] The purchase history management unit classifies purchase histories not only chronologically but also based on the place of purchase and the behavioral patterns of the purchaser, allowing the user to search for the history based on specific conditions. The purchase history management unit, for example, classifies purchase histories not only chronologically but also based on the place of purchase and the behavioral patterns of the purchaser, allowing the user to search for the history based on specific conditions. For example, when a user searches for products purchased at a specific store, the user can specify the place of purchase and the time period of purchase. This allows the user to search for purchase history based on specific conditions.

[0035] The analysis unit can implement an algorithm that predicts a user's purchasing tendencies and next purchasing behavior based on the purchase history. The analysis unit implements, for example, an algorithm that predicts a user's purchasing tendencies based on the purchase history. For example, if a user tends to regularly purchase a specific product, the analysis unit automatically adds that product to a next purchase list. This makes it possible to predict a user's purchasing tendencies and next purchasing behavior.

[0036] The purchase history management unit has a function that allows users to share their purchase history with family and friends, enabling them to jointly make purchasing plans. The purchase history management unit, for example, has a function that allows users to share their purchase history with family and friends, enabling them to jointly make purchasing plans. For example, the purchase history of all family members can be managed on a single platform, and a joint purchasing list can be created. This allows users to share their purchase history with family and friends, enabling them to jointly make purchasing plans.

[0037] The purchase history management unit can integrate purchase history with other life logs to support comprehensive lifestyle management. For example, the purchase history management unit can integrate purchase history with other life logs (e.g., health data and exercise data) to support comprehensive lifestyle management. For example, health data and purchase history can be integrated to provide healthy dietary suggestions. This allows the user to integrate purchase history with other life logs to perform comprehensive lifestyle management.

[0038] The analysis unit can provide more personalized suggestions by taking into account the user's lifestyle and preferences in analyzing the purchase history. For example, the analysis unit can provide more personalized suggestions by taking into account the user's lifestyle and preferences in analyzing the purchase history. For example, if the user is health-conscious, health foods and fitness-related products can be suggested. This makes it possible to provide personalized suggestions based on the user's lifestyle and preferences.

[0039] The analysis unit can suggest new products that go well with products that the user has previously purchased based on the analysis results of the purchase history. For example, the analysis unit can suggest new products that go well with products that the user has previously purchased based on the analysis results of the purchase history. For example, if the user has purchased cosmetics from a specific brand, new products from that brand can be suggested. This makes it possible to suggest new products that go well with products that the user has previously purchased.

[0040] The analysis unit can share the results of the analysis of the purchase history with the user's friends and family, allowing them to jointly make a purchasing plan. For example, the analysis unit can share the results of the analysis of the purchase history with the user's friends and family, allowing them to jointly make a purchasing plan. For example, the purchase history of all family members can be managed on a single platform, and a joint purchasing list can be created. This allows the user to share their purchasing plan with friends and family, allowing them to jointly make a purchasing plan.

[0041] The analysis unit can compare the analysis results of the purchase history with other users to understand trends and popular products. The analysis unit, for example, compares the analysis results of the purchase history with other users to understand trends and popular products. For example, the analysis unit compares the purchase histories of users living in the same area to understand local trends. This allows the user to compare the purchase histories with other users and understand trends and popular products.

[0042] The advertisement delivery unit takes into account not only purchase history but also browsing history and search history when delivering advertisements, enabling more accurate advertisement delivery. For example, the advertisement delivery unit takes into account not only purchase history but also browsing history and search history when delivering advertisements. For example, the advertisement delivery unit delivers relevant advertisements based on keywords searched for by the user in the past and web pages viewed by the user. This allows advertisements to be delivered taking into account not only the user's purchase history but also their browsing history and search history.

[0043] The advertisement delivery unit can implement an algorithm that monitors the effectiveness of advertisement delivery in real time and delivers highly effective advertisements with priority. The advertisement delivery unit, for example, implements an algorithm that monitors the effectiveness of advertisement delivery in real time and delivers highly effective advertisements with priority. For example, the effectiveness of advertisements is evaluated based on click rates and conversion rates, and highly effective advertisements are delivered with priority. This makes it possible to monitor the effectiveness of advertisement delivery in real time and deliver highly effective advertisements with priority.

[0044] The advertisement delivery unit can deliver highly relevant advertisements by taking into account the purchase histories of the user's friends and family when delivering advertisements. For example, the advertisement delivery unit can deliver highly relevant advertisements by taking into account the purchase histories of the user's friends and family when delivering advertisements. For example, based on the purchase histories of all family members, advertisements relevant to all family members can be delivered. This allows advertisements to be delivered by taking into account the purchase histories of the user's friends and family.

[0045] The advertisement delivery unit can deliver personalized advertisements by taking into consideration the user's lifestyle and preferences when delivering advertisements. For example, the advertisement delivery unit delivers personalized advertisements by taking into consideration the user's lifestyle and preferences when delivering advertisements. For example, if the user is health-conscious, advertisements related to health foods and fitness are delivered. This makes it possible to deliver personalized advertisements based on the user's lifestyle and preferences.

[0046] The user interface can add a voice assistant function, allowing users to search and view their purchase history by voice. For example, when a user says, "Tell me what products I purchased last month," the voice assistant will display the corresponding purchase history. This allows users to search and view their purchase history by voice.

[0047] A customization function may be added to the user interface, allowing the user to configure the interface to suit their preferences. For example, a customization function may be added to the user interface, allowing the user to configure the interface to suit their preferences. For example, the user may be able to freely change the order and layout of the information displayed. This allows the user to configure the interface to suit their preferences.

[0048] The user interface may add a function that allows sharing with family and friends, allowing them to jointly make purchasing plans. The user interface may add a function that allows sharing with family and friends, allowing them to jointly make purchasing plans. For example, the purchasing history of all family members may be managed on a single platform, and a joint purchasing list may be created. This allows users to share their purchasing plans with family and friends, allowing them to jointly make purchasing plans.

[0049] The user interface can add a function to display other life logs and support comprehensive lifestyle management. The user interface can add a function to display other life logs (e.g., health data or exercise data) and support comprehensive lifestyle management. For example, health data and purchase history can be integrated to provide healthy eating suggestions. This allows the user to display other life logs and support comprehensive lifestyle management.

[0050] The data security department can introduce blockchain technology to prevent data tampering for data security purposes. The data security department can introduce blockchain technology to prevent data tampering for data security purposes. For example, purchase history data can be recorded on a blockchain, making it impossible to tamper with. This allows the introduction of blockchain technology to prevent data tampering for data security purposes.

[0051] The data security department may provide a dashboard that allows users to check their data usage status in real time to protect their privacy. For example, the data security department may provide a dashboard that allows users to check their data usage status in real time to protect their privacy. For example, the user may check which advertisers their data is being provided to. This allows the user to check their data usage status in real time.

[0052] The data security unit can store user data in distributed storage for data security purposes, thereby increasing data safety. The data security unit can store user data in distributed storage for data security purposes, for example, to increase data safety. For example, purchase history data can be distributed and stored across multiple servers. This allows user data to be stored in distributed storage, thereby increasing data safety.

[0053] The data security unit can provide a function that allows a user to set detailed permission for use of their own data in order to protect privacy. The data security unit, for example, provides a function that allows a user to set detailed permission for use of their own data in order to protect privacy. For example, a user can set whether to allow data to be provided to a specific advertiser. This allows a user to set detailed permission for use of their own data.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The purchase history management system can also have a function that provides recipes and instructions for using products that the user has previously purchased based on the user's purchase history. For example, if a user purchases a specific ingredient, the system can suggest recipes using that ingredient. It can also provide instructions for using and maintaining home appliances that the user has purchased. This allows users to learn how to make the most of the products they have purchased, improving their purchasing experience.

[0056] The purchase history management system can also have a function to display reviews and ratings of products that a user has previously purchased based on the user's purchase history. For example, if a user purchases a specific product, reviews and ratings of that product by other users will be displayed. Users can also post reviews of products they have purchased themselves. This allows users to consider the opinions of other users when making a purchase.

[0057] A purchase history management system can also be equipped with a function to manage the warranty period and maintenance schedule of products purchased by a user in the past based on the user's purchase history. For example, if a user purchases a home appliance, the system can notify the user of the warranty period and maintenance schedule of that product. It can also send a reminder before the warranty period expires. This makes it easier for users to understand the warranty period and maintenance schedule of the products they have purchased.

[0058] The purchase history management system can also have a function that provides recycling and disposal methods for products that a user has previously purchased based on the user's purchase history. For example, if a user purchases a specific home appliance, the system can suggest recycling and disposal methods for that product. It can also display a list of recyclable products. This allows users to make purchasing decisions that are environmentally conscious.

[0059] A purchase history management system can also have a function to track price fluctuations of products purchased by a user in the past based on the user's purchase history. For example, if a user purchases a specific product, the system can notify the user of price fluctuations of that product. It can also send a reminder when the price drops. This allows the user to understand price fluctuations of purchased products and repurchase them at the optimal time.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The purchase history management unit centrally manages purchase histories from brick-and-mortar stores and online stores. For example, a user can check the history of products purchased at brick-and-mortar stores and online stores on a single screen. The purchase history management unit also categorizes purchase histories not only chronologically but also based on the place of purchase and the buyer's behavioral patterns, allowing users to search for history based on specific criteria. For example, when searching for products purchased at a specific store, a user can specify the place of purchase and the time of purchase. Step 2: The analysis unit analyzes the purchase history managed by the purchase history management unit. For example, it introduces an algorithm that predicts the user's purchasing trends based on the purchase history. The analysis unit also uses an emotion estimation function to record the emotions the user felt at the time of purchase and filters the purchase history based on those emotions. For example, it identifies products that the user felt positive about when they purchased them and displays those products preferentially. Step 3: The suggestion unit makes purchasing suggestions based on the purchasing history analyzed by the analysis unit. For example, it predicts when the user will purchase daily necessities and notifies them of the timing of repurchases. The suggestion unit also suggests unpurchased products that the user may be interested in. For example, it identifies and suggests products that the user may be interested in based on the user's past purchasing history and emotional data. Step 4: The ad delivery unit delivers ads based on the user's purchase history. For example, it displays ads related to products the user has previously purchased. When delivering ads, the ad delivery unit also takes into account the user's browsing history and search history in addition to the user's purchase history, thereby delivering more accurate ads. For example, it delivers relevant ads based on keywords the user has previously searched for and web pages they have viewed.

[0062] (Example 2) A purchase history management system according to an embodiment of the present invention is a system that centrally manages purchase histories from brick-and-mortar stores and online stores, generates purchase suggestions using a generation AI, and delivers advertisements based on the purchase data. This allows users to easily check their past purchase history and receive purchase suggestions from the AI. Furthermore, the operating company can monetize the advertisements delivered based on the purchase data.

[0063] A purchase history management system according to an embodiment includes a purchase history management unit, an analysis unit, a suggestion unit, and an advertisement distribution unit. The purchase history management unit centrally manages purchase histories from brick-and-mortar stores and online stores. For example, a user can view the history of products purchased at brick-and-mortar stores and online stores on a single screen. The purchase history management unit classifies purchase histories not only chronologically but also based on the purchase location and the buyer's behavioral patterns, allowing users to search the history based on specific criteria. For example, when searching for products purchased at a specific store, a user can specify the purchase location and time period. The analysis unit analyzes the purchase history managed by the purchase history management unit. For example, the analysis unit implements an algorithm that predicts a user's purchasing trends based on the purchase history. The analysis unit also uses an emotion estimation function to record the emotions a user felt at the time of purchase and filter the purchase history based on the emotions. For example, the analysis unit identifies products for which the user felt positive emotions when purchasing and preferentially displays those products. The suggestion unit makes purchase suggestions based on the purchase history analyzed by the analysis unit. For example, the analysis unit predicts when a user will purchase daily necessities and notifies the user of the timing of repurchases. The suggestion unit also suggests unpurchased products that the user may be interested in. For example, it identifies and suggests products that the user may be interested in based on the user's past purchase history and emotional data. The advertisement distribution unit distributes advertisements based on the purchase history. For example, it displays advertisements related to products the user has previously purchased. When distributing advertisements, the advertisement distribution unit also takes into account the user's browsing history and search history in addition to the user's purchase history, thereby distributing more accurate advertisements. For example, it distributes relevant advertisements based on keywords the user has previously searched for and web pages the user has viewed. As a result, the purchase history management system according to the embodiment allows users to easily check their past purchase history and receive purchase suggestions based on AI. Furthermore, the operating company can generate revenue by distributing advertisements based on purchase data.

[0064] The purchase history management unit allows a user to check the history of products purchased at real stores and online stores on one screen. The purchase history management unit allows a user to check the history of products purchased at real stores and online stores on one screen. For example, when a user searches for products purchased at a specific store, the user can specify the purchase location and time period. This allows a user to check the purchase history from real stores and online stores in a unified manner.

[0065] The suggestion unit can predict when the user will purchase daily necessities based on the purchase history and notify the user of the timing for repurchase. For example, the suggestion unit predicts when the user will purchase daily necessities based on the purchase history and notifies the user of the timing for repurchase. For example, if the user tends to purchase a specific product regularly, the suggestion unit automatically adds the product to the next purchase list. This allows the user to avoid missing the timing for repurchasing daily necessities and purchase them at the optimal timing.

[0066] The suggestion unit can suggest unpurchased products that the user may be interested in based on the purchase history. The suggestion unit suggests unpurchased products that the user may be interested in based on, for example, the purchase history. For example, the suggestion unit identifies and suggests products that the user may be interested in based on the user's past purchase history and emotional data. This improves the user's purchasing experience by suggesting new products that the user may be interested in.

[0067] The advertisement distribution unit can display advertisements related to products previously purchased by the user based on the purchase history. For example, the advertisement distribution unit displays advertisements related to products previously purchased by the user based on the purchase history. For example, if the user has purchased cosmetics from a specific brand, new products from that brand are suggested. This allows advertisements that are highly relevant to the user to be displayed, thereby increasing the effectiveness of the advertisements.

[0068] The purchase history management unit classifies purchase histories not only chronologically but also based on the place of purchase and the behavioral patterns of the purchaser, allowing the user to search for the history based on specific conditions. The purchase history management unit, for example, classifies purchase histories not only chronologically but also based on the place of purchase and the behavioral patterns of the purchaser, allowing the user to search for the history based on specific conditions. For example, when a user searches for products purchased at a specific store, the user can specify the place of purchase and the time period of purchase. This allows the user to search for purchase history based on specific conditions.

[0069] The analysis unit can implement an algorithm that predicts a user's purchasing tendencies and next purchasing behavior based on the purchase history. The analysis unit implements, for example, an algorithm that predicts a user's purchasing tendencies based on the purchase history. For example, if a user tends to regularly purchase a specific product, the analysis unit automatically adds that product to a next purchase list. This makes it possible to predict a user's purchasing tendencies and next purchasing behavior.

[0070] The analysis unit can use the emotion estimation function to record the emotion the user felt at the time of purchase and filter the purchase history based on that emotion. For example, the analysis unit can use the emotion estimation function to record the emotion the user felt at the time of purchase and filter the purchase history based on that emotion. For example, the analysis unit can identify products for which the user felt positive emotions when purchasing and display those products preferentially. This allows the purchase history to be filtered based on the user's emotion.

[0071] The purchase history management unit has a function that allows users to share their purchase history with family and friends, enabling them to jointly make purchasing plans. The purchase history management unit, for example, has a function that allows users to share their purchase history with family and friends, enabling them to jointly make purchasing plans. For example, the purchase history of all family members can be managed on a single platform, and a joint purchasing list can be created. This allows users to share their purchase history with family and friends, enabling them to jointly make purchasing plans.

[0072] The purchase history management unit can integrate purchase history with other life logs to support comprehensive lifestyle management. For example, the purchase history management unit can integrate purchase history with other life logs (e.g., health data and exercise data) to support comprehensive lifestyle management. For example, health data and purchase history can be integrated to provide healthy dietary suggestions. This allows the user to integrate purchase history with other life logs to perform comprehensive lifestyle management.

[0073] The analysis unit can use the emotion estimation function to analyze the emotion a user feels when purchasing a specific product and make a purchasing suggestion to elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotion a user feels when purchasing a specific product and make a purchasing suggestion to elicit positive emotions. For example, the analysis unit can identify a product that the user felt positive emotions about when purchasing and suggest products related to that product. This makes it possible to make a purchasing suggestion to elicit positive emotions from the user.

[0074] The analysis unit can provide more personalized suggestions by taking into account the user's lifestyle and preferences in analyzing the purchase history. For example, the analysis unit can provide more personalized suggestions by taking into account the user's lifestyle and preferences in analyzing the purchase history. For example, if the user is health-conscious, health foods and fitness-related products can be suggested. This makes it possible to provide personalized suggestions based on the user's lifestyle and preferences.

[0075] The analysis unit can suggest new products that go well with products that the user has previously purchased based on the analysis results of the purchase history. For example, the analysis unit can suggest new products that go well with products that the user has previously purchased based on the analysis results of the purchase history. For example, if the user has purchased cosmetics from a specific brand, new products from that brand can be suggested. This makes it possible to suggest new products that go well with products that the user has previously purchased.

[0076] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding products purchased in the past and suggest products that elicit positive emotions. For example, the analysis unit uses the emotion estimation function to analyze the user's emotions regarding products purchased in the past and suggest products that elicit positive emotions. For example, the analysis unit identifies products that the user felt positive about when purchasing and suggests products related to those products. This makes it possible to suggest products that elicit positive emotions from the user.

[0077] The analysis unit can share the results of the analysis of the purchase history with the user's friends and family, allowing them to jointly make a purchasing plan. For example, the analysis unit can share the results of the analysis of the purchase history with the user's friends and family, allowing them to jointly make a purchasing plan. For example, the purchase history of all family members can be managed on a single platform, and a joint purchasing list can be created. This allows the user to share their purchasing plan with friends and family, allowing them to jointly make a purchasing plan.

[0078] The analysis unit can compare the analysis results of the purchase history with other users to understand trends and popular products. The analysis unit, for example, compares the analysis results of the purchase history with other users to understand trends and popular products. For example, the analysis unit compares the purchase histories of users living in the same area to understand local trends. This allows the user to compare the purchase histories with other users and understand trends and popular products.

[0079] The analysis unit can use the emotion estimation function to suggest unpurchased products that the user may be interested in and monitor the emotional reactions to those products in real time. The analysis unit, for example, uses the emotion estimation function to suggest unpurchased products that the user may be interested in and monitor the emotional reactions to those products in real time. For example, the analysis unit identifies and suggests products that the user may be interested in based on the user's past purchase history and emotional data. This makes it possible to suggest unpurchased products that the user may be interested in and monitor the emotional reactions to those products in real time.

[0080] The advertisement delivery unit takes into account not only purchase history but also browsing history and search history when delivering advertisements, enabling more accurate advertisement delivery. For example, the advertisement delivery unit takes into account not only purchase history but also browsing history and search history when delivering advertisements. For example, the advertisement delivery unit delivers relevant advertisements based on keywords searched for by the user in the past and web pages viewed by the user. This allows advertisements to be delivered taking into account not only the user's purchase history but also their browsing history and search history.

[0081] The advertisement delivery unit can implement an algorithm that monitors the effectiveness of advertisement delivery in real time and delivers highly effective advertisements with priority. The advertisement delivery unit, for example, implements an algorithm that monitors the effectiveness of advertisement delivery in real time and delivers highly effective advertisements with priority. For example, the effectiveness of advertisements is evaluated based on click rates and conversion rates, and highly effective advertisements are delivered with priority. This makes it possible to monitor the effectiveness of advertisement delivery in real time and deliver highly effective advertisements with priority.

[0082] The advertisement distribution unit can use the emotion estimation function to analyze the emotion a user feels when viewing an advertisement and select an advertisement that elicits positive emotions. The advertisement distribution unit, for example, uses the emotion estimation function to analyze the emotion a user feels when viewing an advertisement and selects an advertisement that elicits positive emotions. For example, the advertisement distribution unit analyzes the user's facial expressions and voice and selects an advertisement that elicits positive emotions. This makes it possible to analyze the emotion a user feels when viewing an advertisement and select an advertisement that elicits positive emotions.

[0083] The advertisement delivery unit can deliver highly relevant advertisements by taking into account the purchase histories of the user's friends and family when delivering advertisements. For example, the advertisement delivery unit can deliver highly relevant advertisements by taking into account the purchase histories of the user's friends and family when delivering advertisements. For example, based on the purchase histories of all family members, advertisements relevant to all family members can be delivered. This allows advertisements to be delivered by taking into account the purchase histories of the user's friends and family.

[0084] The advertisement delivery unit can deliver personalized advertisements by taking into consideration the user's lifestyle and preferences when delivering advertisements. For example, the advertisement delivery unit delivers personalized advertisements by taking into consideration the user's lifestyle and preferences when delivering advertisements. For example, if the user is health-conscious, advertisements related to health foods and fitness are delivered. This makes it possible to deliver personalized advertisements based on the user's lifestyle and preferences.

[0085] The advertisement delivery unit uses the emotion estimation function to monitor in real time the emotions of users when they view advertisements, and can deliver advertisements based on those emotions. The advertisement delivery unit, for example, uses the emotion estimation function to monitor in real time the emotions of users when they view advertisements. For example, the advertisement delivery unit analyzes the user's facial expressions and voice, and delivers advertisements based on emotion scores. This allows the advertisement delivery unit to monitor in real time the emotions of users when they view advertisements, and deliver advertisements based on those emotions.

[0086] The user interface can add a voice assistant function, allowing users to search and view their purchase history by voice. For example, when a user says, "Tell me what products I purchased last month," the voice assistant will display the corresponding purchase history. This allows users to search and view their purchase history by voice.

[0087] A customization function may be added to the user interface, allowing the user to configure the interface to suit their preferences. For example, a customization function may be added to the user interface, allowing the user to configure the interface to suit their preferences. For example, the user may be able to freely change the order and layout of the information displayed. This allows the user to configure the interface to suit their preferences.

[0088] The user interface can use an emotion estimation function to analyze the emotion a user feels when operating the interface and provide a design that elicits positive emotions. The user interface can, for example, use an emotion estimation function to analyze the emotion a user feels when operating the interface and provide a design that elicits positive emotions. For example, the user's facial expressions and voice can be analyzed to provide a design that elicits positive emotions. This makes it possible to analyze the emotion a user feels when operating the interface and provide a design that elicits positive emotions.

[0089] The user interface may add a function that allows sharing with family and friends, allowing them to jointly make purchasing plans. The user interface may add a function that allows sharing with family and friends, allowing them to jointly make purchasing plans. For example, the purchasing history of all family members may be managed on a single platform, and a joint purchasing list may be created. This allows users to share their purchasing plans with family and friends, allowing them to jointly make purchasing plans.

[0090] The user interface can add a function to display other life logs and support comprehensive lifestyle management. The user interface can add a function to display other life logs (e.g., health data or exercise data) and support comprehensive lifestyle management. For example, health data and purchase history can be integrated to provide healthy eating suggestions. This allows the user to display other life logs and support comprehensive lifestyle management.

[0091] The user interface uses an emotion estimation function to monitor the emotions of the user when operating the interface in real time, and can provide an interface based on the emotions. The user interface, for example, uses an emotion estimation function to monitor the emotions of the user when operating the interface in real time. For example, the user's facial expressions and voice are analyzed, and the interface is adjusted based on the emotion score. This makes it possible to monitor the emotions of the user when operating the interface in real time, and to provide an interface based on the emotions.

[0092] The data security department can introduce blockchain technology to prevent data tampering for data security purposes. The data security department can introduce blockchain technology to prevent data tampering for data security purposes. For example, purchase history data can be recorded on a blockchain, making it impossible to tamper with. This allows the introduction of blockchain technology to prevent data tampering for data security purposes.

[0093] The data security department may provide a dashboard that allows users to check their data usage status in real time to protect their privacy. For example, the data security department may provide a dashboard that allows users to check their data usage status in real time to protect their privacy. For example, the user may check which advertisers their data is being provided to. This allows the user to check their data usage status in real time.

[0094] The data security unit can use the emotion estimation function to analyze the anxiety the user has about data security and propose measures to alleviate the anxiety. The data security unit can, for example, use the emotion estimation function to analyze the anxiety the user has about data security and propose measures to alleviate the anxiety. For example, the data security unit can analyze the user's facial expressions and voice to identify the cause of the anxiety. This makes it possible to analyze the anxiety the user has about data security and propose measures to alleviate the anxiety.

[0095] The data security unit can store user data in distributed storage for data security purposes, thereby increasing data safety. The data security unit can store user data in distributed storage for data security purposes, for example, to increase data safety. For example, purchase history data can be distributed and stored across multiple servers. This allows user data to be stored in distributed storage, thereby increasing data safety.

[0096] The data security unit can provide a function that allows a user to set detailed permission for use of their own data in order to protect privacy. The data security unit, for example, provides a function that allows a user to set detailed permission for use of their own data in order to protect privacy. For example, a user can set whether to allow data to be provided to a specific advertiser. This allows a user to set detailed permission for use of their own data.

[0097] The data security unit uses the emotion estimation function to monitor the emotions that users have about data security in real time, and can provide security measures based on those emotions. The data security unit, for example, uses the emotion estimation function to monitor the emotions that users have about data security in real time. For example, it analyzes the user's facial expressions and voice and adjusts security measures based on the emotion score. This makes it possible to monitor the emotions that users have about data security in real time, and provide security measures based on those emotions.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The purchase history management system can also have a function that provides recipes and instructions for using products that the user has previously purchased based on the user's purchase history. For example, if a user purchases a specific ingredient, the system can suggest recipes using that ingredient. It can also provide instructions for using and maintaining home appliances that the user has purchased. This allows users to learn how to make the most of the products they have purchased, improving their purchasing experience.

[0100] The purchase history management system can also have a function to display reviews and ratings of products that a user has previously purchased based on the user's purchase history. For example, if a user purchases a specific product, reviews and ratings of that product by other users will be displayed. Users can also post reviews of products they have purchased themselves. This allows users to consider the opinions of other users when making a purchase.

[0101] A purchase history management system can also be equipped with a function to manage the warranty period and maintenance schedule of products purchased by a user in the past based on the user's purchase history. For example, if a user purchases a home appliance, the system can notify the user of the warranty period and maintenance schedule of that product. It can also send a reminder before the warranty period expires. This makes it easier for users to understand the warranty period and maintenance schedule of the products they have purchased.

[0102] The purchase history management system can also have a function that provides recycling and disposal methods for products that a user has previously purchased based on the user's purchase history. For example, if a user purchases a specific home appliance, the system can suggest recycling and disposal methods for that product. It can also display a list of recyclable products. This allows users to make purchasing decisions that are environmentally conscious.

[0103] A purchase history management system can also have a function to track price fluctuations of products purchased by a user in the past based on the user's purchase history. For example, if a user purchases a specific product, the system can notify the user of price fluctuations of that product. It can also send a reminder when the price drops. This allows the user to understand price fluctuations of purchased products and repurchase them at the optimal time.

[0104] The purchase history management system can further estimate the user's emotions and, based on the estimated emotions, suggest products that have a relaxing effect when the user is feeling stressed. For example, if the user is feeling stressed, it can suggest aroma oils or massage equipment that have a relaxing effect. It can also provide music or videos that have a relaxing effect when the user feels like relaxing. This allows the user to receive products and services that match their emotions.

[0105] The purchase history management system can further estimate the user's emotions and, based on the estimated emotions, provide special offers or discount coupons when the user is feeling positive emotions. For example, if the user is feeling joy or satisfaction, the system can provide special offers or discount coupons to further enhance those emotions. Also, if the user is feeling positive emotions toward a specific product, the system can suggest offers related to that product. This allows the user to further enhance their positive emotions.

[0106] The purchase history management system can further estimate the user's emotions and, based on the estimated emotions, suggest products and services that will help change the user's mood when the user is feeling negative. For example, if the user is feeling sad or anxious, the system can suggest products and services that will help change the user's mood and alleviate those emotions. It can also suggest products and services that have a relaxing effect when the user is feeling stressed. This allows the user to relieve negative emotions.

[0107] The purchase history management system can further include a function for estimating a user's emotions, analyzing the user's emotions toward a particular product based on the estimated emotions, and rating the product based on those emotions. For example, if a user has positive emotions toward a particular product, the system can record the product as highly rated. On the other hand, if the user has negative emotions toward the product, the system can record the product as poorly rated. This allows users to rate products based on their emotions.

[0108] The purchase history management system can also have a function to estimate a user's emotions and, based on the estimated emotions, display reviews and ratings of a particular product when the user is considering purchasing that product. For example, if a user is interested in a particular product, reviews and ratings of that product by other users can be displayed. Furthermore, when a user is unsure about a purchase, the user can refer to the opinions of other users. This allows the user to make purchasing decisions based on their emotions.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The purchase history management unit centrally manages purchase histories from brick-and-mortar stores and online stores. For example, a user can check the history of products purchased at brick-and-mortar stores and online stores on a single screen. The purchase history management unit also categorizes purchase histories not only chronologically but also based on the place of purchase and the buyer's behavioral patterns, allowing users to search for history based on specific criteria. For example, when searching for products purchased at a specific store, a user can specify the place of purchase and the time of purchase. Step 2: The analysis unit analyzes the purchase history managed by the purchase history management unit. For example, it introduces an algorithm that predicts the user's purchasing trends based on the purchase history. The analysis unit also uses an emotion estimation function to record the emotions the user felt at the time of purchase and filters the purchase history based on those emotions. For example, it identifies products that the user felt positive about when they purchased them and displays those products preferentially. Step 3: The suggestion unit makes purchasing suggestions based on the purchasing history analyzed by the analysis unit. For example, it predicts when the user will purchase daily necessities and notifies them of the timing of repurchases. The suggestion unit also suggests unpurchased products that the user may be interested in. For example, it identifies and suggests products that the user may be interested in based on the user's past purchasing history and emotional data. Step 4: The ad delivery unit delivers ads based on the user's purchase history. For example, it displays ads related to products the user has previously purchased. When delivering ads, the ad delivery unit also takes into account the user's browsing history and search history in addition to the user's purchase history, thereby delivering more accurate ads. For example, it delivers relevant ads based on keywords the user has previously searched for and web pages they have viewed.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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]

[0178] 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 purchasing history management department that centrally manages purchasing history from brick-and-mortar stores and online stores; an analysis unit that analyzes the purchase history managed by the purchase history management unit; a suggestion unit that makes purchase suggestions based on the purchase history analyzed by the analysis unit; an advertisement distribution unit that distributes advertisements based on the purchase history; A system characterized by:

2. The suggestion unit Based on the purchase history, the timing when the user will purchase daily necessities is predicted and the timing of repurchases is notified. The system of claim 1 .

3. The purchase history management unit Integrate the purchase history with other life logs to support comprehensive lifestyle management The system of claim 1 .

4. The advertisement distribution unit When delivering ads, we take into account not only the purchase history but also browsing history and search history to deliver more accurate ads. The system of claim 1 .

5. The user interface is Using emotion estimation functionality, we analyze the emotions users feel when interacting with the interface and provide designs that elicit positive emotions. The system of claim 1 .

6. The analysis unit Using an emotion estimation function, the emotion a user felt when making a purchase is recorded and the purchase history is filtered based on that emotion. The system of claim 1 .

7. The advertisement distribution unit Using emotion estimation, we analyze the emotions users feel when viewing ads and select ads that elicit positive emotions. The system of claim 1 .

8. The Data Security Department Using emotion estimation, we analyze users' concerns about data security and suggest measures to alleviate those concerns. The system of claim 1 .

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