System

Abstract

An object of a system according to an embodiment is to allow a consumer to grasp a consumption speed of an article and find an optimal purchase timing and place.SOLUTION: A system includes a receipt data analysis part, a consumption speed prediction part, a leaflet information collection part, a proposal part, and a notification part. The receipt data analysis unit analyzes the receipt data. The consumption speed prediction unit predicts a consumption speed of the article based on the data analyzed by the receipt data analysis unit. A leaflet information collection part collects leaflet information of a peripheral store. A proposal part proposes optimum purchase timing and place on the basis of the information obtained by the consumption speed prediction part and the leaflet information collection part. The notification unit notifies the consumer of the content proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for consumers to grasp the rate at which they consume goods and find the optimal time and place to purchase.

[0005] The system according to the embodiment aims to help consumers understand the consumption rate of goods and find the optimal timing and place for purchases. [Means for solving the problem]

[0006] The system according to the embodiment includes a receipt data analysis unit, a consumption rate prediction unit, a flyer information collection unit, a suggestion unit, and a notification unit. The receipt data analysis unit analyzes receipt data. The consumption rate prediction unit predicts the consumption rate of an item based on the data analyzed by the receipt data analysis unit. The flyer information collection unit collects flyer information from nearby stores. The suggestion unit suggests the optimal timing and location for purchases based on the information obtained by the consumption rate prediction unit and the flyer information collection unit. The notification unit notifies the consumer of the content suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment allows consumers to understand the consumption rate of goods and find the optimal timing and place for purchase. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The recommendation system according to an embodiment of the present invention is a system that predicts the consumption rate of an item purchased by a consumer and suggests the optimal timing and place of purchase, thereby enabling consumers to purchase items efficiently and reduce wasteful shopping.

[0029] The proposal system according to the embodiment includes a receipt data analysis unit, a consumption rate prediction unit, a flyer information collection unit, a suggestion unit, and a notification unit. The receipt data analysis unit analyzes receipt data for items purchased by a consumer. For example, the receipt data includes the purchase date, the name, quantity, and price of the purchased item. The consumption rate prediction unit predicts the consumption rate of the item based on the data analyzed by the receipt data analysis unit. For example, if a consumer purchases milk once a week, the consumption rate prediction unit predicts that the milk consumption rate will be one week. The flyer information collection unit collects flyer information from nearby stores. For example, the flyer information collection unit collects sale information and price information. The suggestion unit suggests the optimal purchase timing and location based on the information obtained by the consumption rate prediction unit and the flyer information collection unit. For example, the suggestion unit suggests that "the next milk purchase should be made one week later, and nearby store A is having a sale, so it would be optimal to purchase it there." The notification unit notifies the consumer of the content suggested by the suggestion unit. For example, the notification unit can send notifications via smartphone apps, emails, or messaging services, allowing the proposed system to help consumers purchase goods efficiently and reduce wasteful purchases.

[0030] The receipt data analysis unit analyzes the behavioral patterns of a purchaser and can predict fluctuations in consumption speed on specific days of the week or in specific time periods. For example, the receipt data analysis unit analyzes receipt data to identify what types of items a purchaser tends to purchase on specific days of the week or in specific time periods. For example, if a purchaser tends to buy in bulk on weekends, the consumption speed is predicted based on that behavioral pattern. The receipt data analysis unit also analyzes the behavioral patterns of a purchaser and predicts fluctuations in consumption speed during specific time periods. For example, it analyzes the difference in consumption speed between items purchased on weekday evenings and items purchased on weekends. The receipt data analysis unit also predicts what types of items a purchaser will purchase on specific days of the week or in specific time periods based on the receipt data and calculates the consumption speed. For example, it analyzes the difference in consumption speed between items purchased on weekday mornings and items purchased on weekend evenings. This makes it possible to predict consumption speed based on the behavioral patterns of a purchaser.

[0031] The consumption rate prediction unit can reflect seasonal consumption patterns based on the influence of the season and weather. The consumption rate prediction unit, for example, analyzes receipt data to identify seasonal consumption patterns. For example, since the consumption rate of cold drinks tends to be faster in the summer, this pattern is reflected in the prediction. The consumption rate prediction unit also collects weather data and analyzes the impact on the consumption rate of purchased items. For example, since the consumption rate of instant foods tends to be faster on rainy days, the prediction is made based on this data. The consumption rate prediction unit also builds a system that predicts the consumption rate of purchased items, taking into account seasonal and weather fluctuations. For example, since the consumption rate of hot drinks tends to be faster in the winter, the prediction is made based on this data. This makes it possible to predict consumption rates according to the season and weather.

[0032] The receipt data analysis unit integrates inventory data from the smart refrigerator and the smart kitchen to more accurately predict the consumption rate. The receipt data analysis unit, for example, collects inventory data from the smart refrigerator and integrates it with receipt data to predict the consumption rate. For example, it calculates the consumption rate based on the rate at which inventory in the refrigerator is reduced. The receipt data analysis unit also collects inventory data from the smart kitchen and integrates it with receipt data to predict the consumption rate. For example, it calculates the consumption rate based on the rate at which inventory in the kitchen is reduced. The receipt data analysis unit also collects inventory data from smart devices in real time and integrates it with receipt data to build a system that predicts the consumption rate. For example, it calculates the consumption rate based on the rate at which inventory in the refrigerator or kitchen is reduced. This makes it possible to predict the consumption rate by integrating inventory data from smart devices.

[0033] The receipt data analysis unit can suggest healthy consumption patterns based on the nutritional information and health information of purchased items. For example, the receipt data analysis unit analyzes the nutritional information of purchased items contained in receipt data to suggest healthy consumption patterns. For example, it suggests nutritionally balanced meals. The receipt data analysis unit also analyzes the health information of purchased items to build a system that suggests healthy consumption patterns to consumers. For example, it makes suggestions based on the consumption rate of low-calorie foods. The receipt data analysis unit also analyzes the nutritional information and health information of purchased items based on receipt data to suggest healthy consumption patterns to consumers. For example, it makes suggestions based on the intake of vitamins and minerals. This makes it possible to suggest healthy consumption patterns.

[0034] The flyer information collection unit can dynamically adjust the optimal purchase timing based on the expiration date of sale information. The flyer information collection unit, for example, analyzes the expiration date of sale information contained in flyer information and builds a system that dynamically adjusts the optimal purchase timing. For example, it suggests purchase timing based on the end date of the sale. The flyer information collection unit also takes into account the expiration date of the sale information and suggests the optimal purchase timing to consumers. For example, it suggests purchasing before the sale ends. The flyer information collection unit also collects the expiration date of flyer information in real time and develops a system that dynamically adjusts the optimal purchase timing. For example, it recalculates the purchase timing every time the sale information is updated. This makes it possible to suggest the optimal purchase timing taking into account the expiration date of the sale information.

[0035] The flyer information collection unit can collect inventory status of surrounding stores in real time and make suggestions to avoid the risk of running out of stock. The flyer information collection unit, for example, builds a system that collects inventory status of surrounding stores in real time and makes suggestions to avoid the risk of running out of stock. For example, it suggests avoiding stores with low inventory. The flyer information collection unit also suggests to consumers the optimal place to purchase based on the inventory status to avoid the risk of running out of stock. For example, it prioritizes suggestions to stores with abundant inventory. The flyer information collection unit also develops a system that collects inventory data of surrounding stores in real time and makes suggestions to avoid the risk of running out of stock. For example, it suggests avoiding stores with low inventory. This makes it possible to make suggestions to avoid the risk of running out of stock.

[0036] The flyer information collection unit collects online store price information in addition to flyer information, and can suggest the best online and offline purchasing locations. The flyer information collection unit, for example, collects online store price information in addition to flyer information, and builds a system that suggests the best purchasing location. For example, it compares online and offline prices. The flyer information collection unit also suggests the best purchasing location to consumers based on the online store price information. For example, it suggests purchasing online if the online store is cheaper. The flyer information collection unit also integrates flyer information and online store price information to develop a system that suggests the best purchasing location. For example, it compares online and offline prices and makes suggestions. This makes it possible to suggest the best online and offline purchasing locations.

[0037] The flyer information collection unit analyzes flyer information from nearby stores, identifies sale patterns for specific days of the week or time periods, and can make suggestions based on that. The flyer information collection unit, for example, analyzes flyer information from nearby stores and builds a system that identifies sale patterns for specific days of the week or time periods. For example, it identifies stores that have many sales on weekends. The flyer information collection unit also suggests the optimal timing for purchases to consumers based on the sale patterns. For example, it suggests purchasing on days of the week or time periods when there are many sales. The flyer information collection unit also develops a system that identifies sale patterns for specific days of the week or time periods based on flyer information and makes suggestions based on that. For example, it identifies stores that have many sales on weekends. This makes it possible to identify sale patterns for specific days of the week or time periods and make suggestions based on that.

[0038] The notification unit can select the most effective notification method based on the buyer's past responses and behavioral history. The notification unit, for example, analyzes the buyer's past responses and behavioral history and builds a system that selects the most effective notification method. For example, buyers for whom email notifications are effective will be notified by email. The notification unit also suggests the optimal notification method to the buyer based on their behavioral history. For example, if app notifications have been effective in the past, the notification will be sent by app. The notification unit also develops a system that selects the most effective notification method based on the buyer's past response data. For example, if SMS notifications have been effective in the past, the notification will be sent by SMS. This makes it possible to select the most effective notification method based on the buyer's past responses and behavioral history.

[0039] The notification unit can send notifications at the optimal timing based on the purchaser's schedule information. The notification unit, for example, collects the purchaser's schedule information and builds a system that sends notifications at the optimal timing. For example, notifications are sent during times when the purchaser is not busy. The notification unit also suggests the optimal timing for sending notifications to the purchaser based on the schedule information. For example, notifications are sent during times when the purchaser is on break. The notification unit also develops a system that sends notifications at the optimal timing based on the purchaser's schedule data. For example, notifications are sent during times after the purchaser has finished work. This makes it possible to send notifications at the optimal timing based on the purchaser's schedule information.

[0040] The notification unit can utilize a voice assistant to provide voice notification. For example, when notifying the contents of a proposal, the notification unit builds a system that utilizes a voice assistant to provide voice notification. For example, notification is provided through a smart speaker. The notification unit also uses a voice assistant to notify the purchaser of the contents of the proposal by voice. For example, notification is provided through a voice assistant on a smartphone. The notification unit also develops a system that utilizes a voice assistant to provide voice notification of the contents of the proposal. For example, notification is provided through a smart speaker or a voice assistant on a smartphone. This makes it possible to provide voice notification using a voice assistant.

[0041] The notification unit can utilize devices such as a smartwatch and a smart speaker to provide notifications via multiple devices. For example, when notifying the contents of a proposal, the notification unit builds a system that utilizes devices such as a smartwatch and a smart speaker to provide notifications via multiple devices. For example, notifications are provided simultaneously via a smartwatch and a smart speaker. The notification unit also uses multiple devices to notify the purchaser of the contents of the proposal. For example, notifications are provided simultaneously via a smartphone, a smartwatch, and a smart speaker. The notification unit also develops a system that utilizes smart devices to provide notifications via multiple devices. For example, notifications are provided simultaneously via a smartwatch and a smart speaker. This allows notifications to be provided via multiple devices.

[0042] The feedback collection unit diversifies the method of collecting feedback, and can collect not only surveys and reviews, but also social media posts and comments. The feedback collection unit, for example, diversifies the method of collecting feedback and builds a system that collects not only surveys and reviews, but also social media posts and comments. For example, it collects posts from Twitter (registered trademark) and Facebook (registered trademark). The feedback collection unit also collects social media posts and comments and analyzes them as consumer feedback. For example, it collects and analyzes comments from Instagram (registered trademark). The feedback collection unit also diversifies the method of collecting feedback and develops a system that collects social media posts and comments. For example, it collects and analyzes posts from Twitter (registered trademark) and Facebook (registered trademark). This diversifies the method of collecting feedback, and can also collect social media posts and comments.

[0043] The feedback collection unit can analyze the content of the feedback, classify it into positive feedback and negative feedback, and propose improvement measures according to each. The feedback collection unit, for example, analyzes the content of the feedback and builds a system that classifies it into positive feedback and negative feedback. For example, it classifies the feedback using natural language processing technology. The feedback collection unit also proposes improvement measures according to each type of feedback based on the classified feedback. For example, it proposes improvement measures for negative feedback. The feedback collection unit also develops a system that analyzes the content of the feedback, classifies it into positive feedback and negative feedback, and proposes improvement measures according to each type. For example, it uses natural language processing technology to classify the feedback and propose improvement measures. This makes it possible to analyze the content of the feedback, classify it into positive feedback and negative feedback, and propose improvement measures according to each type.

[0044] The feedback collection unit can automate the collection of feedback and introduce a mechanism that allows a purchaser to input simple feedback when scanning a receipt. The feedback collection unit, for example, builds a system that automates the collection of feedback and introduces a mechanism that allows a purchaser to input simple feedback when scanning a receipt. For example, feedback is requested via a pop-up when a receipt is scanned. The feedback collection unit also introduces a mechanism that allows a purchaser to input simple feedback when scanning a receipt and automates the collection of feedback. For example, a simple survey is displayed after scanning. The feedback collection unit also develops a mechanism that automates the collection of feedback and allows a purchaser to input simple feedback when scanning a receipt. For example, feedback is requested via a pop-up when a receipt is scanned. This automates the collection of feedback and introduces a mechanism that allows a purchaser to input simple feedback when scanning a receipt.

[0045] The feedback collection unit can gamify the collection of feedback and introduce a mechanism whereby points and rewards can be earned by providing feedback. The feedback collection unit, for example, builds a system that gamifies the collection of feedback and introduces a mechanism whereby points and rewards can be earned by providing feedback. For example, points can be accumulated by providing feedback. The feedback collection unit also introduces a mechanism whereby points and rewards can be earned by providing feedback to promote the collection of feedback. For example, coupons can be received by providing feedback. The feedback collection unit also develops a mechanism whereby points and rewards can be earned by providing feedback. For example, points can be accumulated by providing feedback. This makes it possible to gamify the collection of feedback and introduce a mechanism whereby points and rewards can be earned by providing feedback.

[0046] The data update unit can dynamically adjust the frequency of data updates and increase the update frequency in accordance with specific events and seasons. The data update unit, for example, builds a system that dynamically adjusts the frequency of data updates and increases the update frequency in accordance with specific events and seasons. For example, the update frequency is increased during specific periods such as the New Year holidays and summer vacation. The data update unit also adjusts the frequency of data updates in accordance with events and seasons to make optimal suggestions to consumers. For example, the update frequency is increased during event periods such as Christmas and Halloween. The data update unit also develops a system that dynamically adjusts the frequency of data updates and increases the update frequency in accordance with specific events and seasons. For example, the update frequency is increased during specific periods such as the New Year holidays and summer vacation. This makes it possible to dynamically adjust the frequency of data updates and increase the update frequency in accordance with specific events and seasons.

[0047] The data updating unit can detect abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies. The data updating unit, for example, builds a system that detects abnormal values ​​and trend changes by comparing with past data when updating data. For example, an anomaly is detected when there is a sudden change in consumption rate. The data updating unit also detects abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies. For example, an anomaly is detected when there is a sudden change in consumption rate for a specific item. The data updating unit also develops a system that detects abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies. For example, an anomaly is detected when there is a sudden change in consumption rate. This makes it possible to detect abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies.

[0048] When updating data, the data updating unit can compare it with data from other consumers, set a benchmark, and perform optimization. The data updating unit, for example, builds a system that compares it with data from other consumers, set a benchmark, and perform optimization when updating data. For example, a benchmark is set based on data from consumers in the same region. The data updating unit also compares it with data from other consumers, set a benchmark, and perform optimization. For example, a benchmark is set based on data from consumers in the same age group. The data updating unit also develops a system that compares it with data from other consumers, set a benchmark, and perform optimization when updating data. For example, a benchmark is set based on data from consumers in the same region. This makes it possible to compare it with data from other consumers, set a benchmark, and perform optimization.

[0049] The data updating unit integrates external market data and economic data when updating data, allowing optimization from a broader perspective. The data updating unit, for example, integrates external market data and economic data when updating data, and builds a system that optimizes from a broader perspective. For example, the latest market trends are reflected. The data updating unit also makes optimal proposals to consumers based on external market data and economic data. For example, it proposes consumption patterns according to economic conditions. The data updating unit also integrates external market data and economic data when updating data, and develops a system that optimizes from a broader perspective. For example, the latest market trends are reflected. This allows external market data and economic data to be integrated and optimization from a broader perspective.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The recommendation system can also learn preferences for specific brands and products based on a user's purchasing history and make personalized suggestions. For example, if a user frequently purchases a particular brand of milk, it can prioritize suggestions about sales on that brand. Or, if a user likes a particular type of bread, it can notify them of new products and sales on that bread. Furthermore, based on a user's purchasing history, it can learn how preferences change with the seasons and make suggestions that are appropriate for the season. For example, in the summer, it can prioritize suggestions about sales on cold drinks and ice cream.

[0052] The suggestion system can also collect the user's health data and make suggestions based on their health status. For example, if the user is on a diet, it will prioritize suggestions for sale information on low-calorie foods and health foods. Also, if the user has a specific allergy, it can notify them of sale information on products that do not contain that allergen. Furthermore, it can make suggestions for nutritionally balanced meals based on the user's health data. For example, if the user is deficient in vitamins or minerals, it will suggest sale information on foods that will supplement them.

[0053] The recommendation system can also collect lifestyle data about the user and make suggestions based on that lifestyle. For example, if the user enjoys outdoor activities, it can prioritize suggestions about special offers on camping equipment and outdoor goods. If the user has a pet, it can notify the user of special offers on pet food and pet supplies. Furthermore, it can make suggestions related to specific events or hobbies based on the user's lifestyle data. For example, if the user enjoys cooking, it can suggest new recipes or special offers on cooking equipment.

[0054] The recommendation system can also collect a user's social media data and make suggestions based on trends on social media. For example, if a user frequently posts about a particular brand or product, it will prioritize suggestions for sale information on that brand or product. It can also make suggestions based on popular products and trends on social media. Furthermore, it can suggest products and sale information purchased by friends and followers based on the user's social media data. For example, if a product purchased by a friend is on sale, it will notify the user of this information.

[0055] The recommendation system can also collect the user's location information and make suggestions based on the user's current location. For example, if the user is near a specific store, the system will prioritize suggesting sale information for that store. Also, if the user is traveling, the system can notify the user of sale information and recommended stores at the user's travel destination. Furthermore, the system can make suggestions related to nearby events and fairs based on the user's location information. For example, if the user is at a specific event venue, the system will suggest sale information for products related to that event.

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

[0057] Step 1: The receipt data analysis unit analyzes the receipt data of the items purchased by the consumer. For example, the receipt data includes the purchase date, the name, quantity, and price of the purchased items. Step 2: The consumption rate prediction unit predicts the consumption rate of the item based on the data analyzed by the receipt data analysis unit. For example, if a consumer purchases milk once a week, the consumption rate prediction unit predicts the consumption rate of milk to be one week. Step 3: The flyer information collection unit collects flyer information from surrounding stores. For example, the flyer information collection unit collects sale information and price information. Step 4: The suggestion unit suggests the best time and place to buy milk based on the information obtained by the consumption speed prediction unit and the flyer information collection unit. For example, the suggestion unit might suggest, "The next time you buy milk, it would be best to wait a week, and nearby store A is having a special sale, so it would be best to buy it there." Step 5: The notification unit notifies the consumer of the content proposed by the suggestion unit. For example, the notification unit may notify the consumer via a smartphone app, email, or messaging service.

[0058] (Example 2) The recommendation system according to an embodiment of the present invention is a system that predicts the consumption rate of an item purchased by a consumer and suggests the optimal timing and place of purchase, thereby enabling consumers to purchase items efficiently and reduce wasteful shopping.

[0059] The proposal system according to the embodiment includes a receipt data analysis unit, a consumption rate prediction unit, a flyer information collection unit, a suggestion unit, and a notification unit. The receipt data analysis unit analyzes receipt data for items purchased by a consumer. For example, the receipt data includes the purchase date, the name, quantity, and price of the purchased item. The consumption rate prediction unit predicts the consumption rate of the item based on the data analyzed by the receipt data analysis unit. For example, if a consumer purchases milk once a week, the consumption rate prediction unit predicts that the milk consumption rate will be one week. The flyer information collection unit collects flyer information from nearby stores. For example, the flyer information collection unit collects sale information and price information. The suggestion unit suggests the optimal purchase timing and location based on the information obtained by the consumption rate prediction unit and the flyer information collection unit. For example, the suggestion unit suggests that "the next milk purchase should be made one week later, and nearby store A is having a sale, so it would be optimal to purchase it there." The notification unit notifies the consumer of the content suggested by the suggestion unit. For example, the notification unit can send notifications via smartphone apps, emails, or messaging services, allowing the proposed system to help consumers purchase goods efficiently and reduce wasteful purchases.

[0060] The receipt data analysis unit analyzes the behavioral patterns of a purchaser and can predict fluctuations in consumption speed on specific days of the week or in specific time periods. For example, the receipt data analysis unit analyzes receipt data to identify what types of items a purchaser tends to purchase on specific days of the week or in specific time periods. For example, if a purchaser tends to buy in bulk on weekends, the consumption speed is predicted based on that behavioral pattern. The receipt data analysis unit also analyzes the behavioral patterns of a purchaser and predicts fluctuations in consumption speed during specific time periods. For example, it analyzes the difference in consumption speed between items purchased on weekday evenings and items purchased on weekends. The receipt data analysis unit also predicts what types of items a purchaser will purchase on specific days of the week or in specific time periods based on the receipt data and calculates the consumption speed. For example, it analyzes the difference in consumption speed between items purchased on weekday mornings and items purchased on weekend evenings. This makes it possible to predict consumption speed based on the behavioral patterns of a purchaser.

[0061] The consumption rate prediction unit can reflect seasonal consumption patterns based on the influence of the season and weather. The consumption rate prediction unit, for example, analyzes receipt data to identify seasonal consumption patterns. For example, since the consumption rate of cold drinks tends to be faster in the summer, this pattern is reflected in the prediction. The consumption rate prediction unit also collects weather data and analyzes the impact on the consumption rate of purchased items. For example, since the consumption rate of instant foods tends to be faster on rainy days, the prediction is made based on this data. The consumption rate prediction unit also builds a system that predicts the consumption rate of purchased items, taking into account seasonal and weather fluctuations. For example, since the consumption rate of hot drinks tends to be faster in the winter, the prediction is made based on this data. This makes it possible to predict consumption rates according to the season and weather.

[0062] The consumption rate prediction unit can use the emotion estimation function to predict fluctuations in consumption rate based on the emotional state of the purchaser. For example, the consumption rate prediction unit uses the emotion estimation function to analyze the emotional state of the purchaser and identify the effect of that emotion on the consumption rate. For example, the consumption rate of a particular item tends to increase during times of high stress. The consumption rate prediction unit also monitors the emotional state of the purchaser in real time and predicts the consumption rate based on that data. For example, the consumption rate of a particular item tends to decrease during times of strong joy. The consumption rate prediction unit also builds a system that predicts fluctuations in consumption rate according to the emotional state of the purchaser based on the emotion estimation data. For example, since the consumption rate of a particular item tends to increase during times of high stress, predictions are made based on that data. This makes it possible to predict the consumption rate based on the emotional state of the purchaser.

[0063] The receipt data analysis unit integrates inventory data from the smart refrigerator and the smart kitchen to more accurately predict the consumption rate. The receipt data analysis unit, for example, collects inventory data from the smart refrigerator and integrates it with receipt data to predict the consumption rate. For example, it calculates the consumption rate based on the rate at which inventory in the refrigerator is reduced. The receipt data analysis unit also collects inventory data from the smart kitchen and integrates it with receipt data to predict the consumption rate. For example, it calculates the consumption rate based on the rate at which inventory in the kitchen is reduced. The receipt data analysis unit also collects inventory data from smart devices in real time and integrates it with receipt data to build a system that predicts the consumption rate. For example, it calculates the consumption rate based on the rate at which inventory in the refrigerator or kitchen is reduced. This makes it possible to predict the consumption rate by integrating inventory data from smart devices.

[0064] The receipt data analysis unit can suggest healthy consumption patterns based on the nutritional information and health information of purchased items. For example, the receipt data analysis unit analyzes the nutritional information of purchased items contained in receipt data to suggest healthy consumption patterns. For example, it suggests nutritionally balanced meals. The receipt data analysis unit also analyzes the health information of purchased items to build a system that suggests healthy consumption patterns to consumers. For example, it makes suggestions based on the consumption rate of low-calorie foods. The receipt data analysis unit also analyzes the nutritional information and health information of purchased items based on receipt data to suggest healthy consumption patterns to consumers. For example, it makes suggestions based on the intake of vitamins and minerals. This makes it possible to suggest healthy consumption patterns.

[0065] The consumption speed prediction unit can use the emotion estimation function to identify items that a purchaser tends to purchase when in a specific emotional state and predict the consumption speed of those items. For example, the consumption speed prediction unit uses the emotion estimation function to identify items that a purchaser tends to purchase when in a specific emotional state. For example, a purchaser tends to purchase specific snacks during times of high stress. The consumption speed prediction unit also monitors the emotional state of the purchaser in real time and, based on that data, identifies items that a purchaser will purchase when in a specific emotional state. For example, a purchaser tends to purchase specific beverages during times of strong joy. The consumption speed prediction unit also builds a system that predicts the consumption speed of items that a purchaser will purchase when in a specific emotional state based on the emotion estimation data. For example, a purchaser tends to consume specific snacks faster during times of high stress. This makes it possible to predict the consumption speed of specific items based on the emotional state of the purchaser.

[0066] The flyer information collection unit can dynamically adjust the optimal purchase timing based on the expiration date of sale information. The flyer information collection unit, for example, analyzes the expiration date of sale information contained in flyer information and builds a system that dynamically adjusts the optimal purchase timing. For example, it suggests purchase timing based on the end date of the sale. The flyer information collection unit also takes into account the expiration date of the sale information and suggests the optimal purchase timing to consumers. For example, it suggests purchasing before the sale ends. The flyer information collection unit also collects the expiration date of flyer information in real time and develops a system that dynamically adjusts the optimal purchase timing. For example, it recalculates the purchase timing every time the sale information is updated. This makes it possible to suggest the optimal purchase timing taking into account the expiration date of the sale information.

[0067] The flyer information collection unit can collect inventory status of surrounding stores in real time and make suggestions to avoid the risk of running out of stock. The flyer information collection unit, for example, builds a system that collects inventory status of surrounding stores in real time and makes suggestions to avoid the risk of running out of stock. For example, it suggests avoiding stores with low inventory. The flyer information collection unit also suggests to consumers the optimal place to purchase based on the inventory status to avoid the risk of running out of stock. For example, it prioritizes suggestions to stores with abundant inventory. The flyer information collection unit also develops a system that collects inventory data of surrounding stores in real time and makes suggestions to avoid the risk of running out of stock. For example, it suggests avoiding stores with low inventory. This makes it possible to make suggestions to avoid the risk of running out of stock.

[0068] The flyer information collection unit can use the emotion estimation function to preferentially suggest sale information that corresponds to the emotional state of the purchaser. The flyer information collection unit, for example, uses the emotion estimation function to analyze the emotional state of the purchaser and build a system that preferentially suggests sale information that corresponds to that emotion. For example, products with a relaxing effect are suggested during times of high stress. The flyer information collection unit also monitors the emotional state of the purchaser in real time and preferentially suggests sale information based on that data. For example, luxury items are suggested during times of strong joy. The flyer information collection unit also develops a system that preferentially suggests sale information that corresponds to the emotional state of the purchaser based on the emotion estimation data. For example, products with a relaxing effect are suggested during times of high stress. This makes it possible to preferentially suggest sale information that corresponds to the emotional state of the purchaser.

[0069] The flyer information collection unit collects online store price information in addition to flyer information, and can suggest the best online and offline purchasing locations. The flyer information collection unit, for example, collects online store price information in addition to flyer information, and builds a system that suggests the best purchasing location. For example, it compares online and offline prices. The flyer information collection unit also suggests the best purchasing location to consumers based on the online store price information. For example, it suggests purchasing online if the online store is cheaper. The flyer information collection unit also integrates flyer information and online store price information to develop a system that suggests the best purchasing location. For example, it compares online and offline prices and makes suggestions. This makes it possible to suggest the best online and offline purchasing locations.

[0070] The flyer information collection unit analyzes flyer information from nearby stores, identifies sale patterns for specific days of the week or time periods, and can make suggestions based on that. The flyer information collection unit, for example, analyzes flyer information from nearby stores and builds a system that identifies sale patterns for specific days of the week or time periods. For example, it identifies stores that have many sales on weekends. The flyer information collection unit also suggests the optimal timing for purchases to consumers based on the sale patterns. For example, it suggests purchasing on days of the week or time periods when there are many sales. The flyer information collection unit also develops a system that identifies sale patterns for specific days of the week or time periods based on flyer information and makes suggestions based on that. For example, it identifies stores that have many sales on weekends. This makes it possible to identify sale patterns for specific days of the week or time periods and make suggestions based on that.

[0071] The flyer information collection unit uses the emotion estimation function to identify stores and brands that a purchaser prefers when they are in a specific emotional state, and can make suggestions based on that information. For example, the flyer information collection unit uses the emotion estimation function to build a system that identifies stores and brands that a purchaser prefers when they are in a specific emotional state. For example, a purchaser tends to prefer certain brands during times of high stress. The flyer information collection unit also monitors the purchaser's emotional state in real time and identifies the preferred stores and brands based on that data. For example, a purchaser tends to prefer certain stores during times of strong joy. The flyer information collection unit also develops a system that identifies stores and brands that a purchaser prefers when they are in a specific emotional state based on the emotion estimation data, and makes suggestions based on that information. For example, a purchaser tends to prefer certain brands during times of high stress. This makes it possible to identify preferred stores and brands based on the purchaser's emotional state, and make suggestions based on that information.

[0072] The notification unit can select the most effective notification method based on the buyer's past responses and behavioral history. The notification unit, for example, analyzes the buyer's past responses and behavioral history and builds a system that selects the most effective notification method. For example, buyers for whom email notifications are effective will be notified by email. The notification unit also suggests the optimal notification method to the buyer based on their behavioral history. For example, if app notifications have been effective in the past, the notification will be sent by app. The notification unit also develops a system that selects the most effective notification method based on the buyer's past response data. For example, if SMS notifications have been effective in the past, the notification will be sent by SMS. This makes it possible to select the most effective notification method based on the buyer's past responses and behavioral history.

[0073] The notification unit can send notifications at the optimal timing based on the purchaser's schedule information. The notification unit, for example, collects the purchaser's schedule information and builds a system that sends notifications at the optimal timing. For example, notifications are sent during times when the purchaser is not busy. The notification unit also suggests the optimal timing for sending notifications to the purchaser based on the schedule information. For example, notifications are sent during times when the purchaser is on break. The notification unit also develops a system that sends notifications at the optimal timing based on the purchaser's schedule data. For example, notifications are sent during times after the purchaser has finished work. This makes it possible to send notifications at the optimal timing based on the purchaser's schedule information.

[0074] The notification unit can use the emotion estimation function to adjust the content and tone of the notification according to the emotional state of the purchaser. For example, the notification unit uses the emotion estimation function to analyze the emotional state of the purchaser and build a system that adjusts the content and tone of the notification according to that emotion. For example, notifications are sent in a gentler tone during times of high stress. The notification unit also monitors the emotional state of the purchaser in real time and adjusts the content and tone of the notification based on that data. For example, notifications are sent in a brighter tone during times of strong joy. The notification unit also develops a system that adjusts the content and tone of the notification according to the emotional state of the purchaser based on the emotion estimation data. For example, notifications are sent in a gentler tone during times of high stress. This makes it possible to adjust the content and tone of the notification according to the emotional state of the purchaser.

[0075] The notification unit can utilize a voice assistant to provide voice notification. For example, when notifying the contents of a proposal, the notification unit builds a system that utilizes a voice assistant to provide voice notification. For example, notification is provided through a smart speaker. The notification unit also uses a voice assistant to notify the purchaser of the contents of the proposal by voice. For example, notification is provided through a voice assistant on a smartphone. The notification unit also develops a system that utilizes a voice assistant to provide voice notification of the contents of the proposal. For example, notification is provided through a smart speaker or a voice assistant on a smartphone. This makes it possible to provide voice notification using a voice assistant.

[0076] The notification unit can utilize devices such as a smartwatch and a smart speaker to provide notifications via multiple devices. For example, when notifying the contents of a proposal, the notification unit builds a system that utilizes devices such as a smartwatch and a smart speaker to provide notifications via multiple devices. For example, notifications are provided simultaneously via a smartwatch and a smart speaker. The notification unit also uses multiple devices to notify the purchaser of the contents of the proposal. For example, notifications are provided simultaneously via a smartphone, a smartwatch, and a smart speaker. The notification unit also develops a system that utilizes smart devices to provide notifications via multiple devices. For example, notifications are provided simultaneously via a smartwatch and a smart speaker. This allows notifications to be provided via multiple devices.

[0077] The notification unit uses the emotion estimation function to identify the notification method that is most acceptable when the purchaser is in a specific emotional state, and can provide notifications using that method. The notification unit, for example, uses the emotion estimation function to build a system that identifies the notification method that is most acceptable when the purchaser is in a specific emotional state. For example, when the purchaser is under high stress, the notification is provided in a gentle tone. The notification unit also monitors the purchaser's emotional state in real time and identifies the notification method that is most acceptable based on that data. For example, when the purchaser is under high stress, the notification is provided in a bright tone. The notification unit also develops a system that identifies the notification method that is most acceptable when the purchaser is in a specific emotional state, based on the emotion estimation data, and provides notifications using that method. For example, when the purchaser is under high stress, the notification is provided in a gentle tone. This makes it possible to identify the notification method that is most acceptable based on the purchaser's emotional state, and provide notifications using that method.

[0078] The feedback collection unit diversifies the method of collecting feedback, and can collect not only surveys and reviews, but also social media posts and comments. The feedback collection unit, for example, diversifies the method of collecting feedback and builds a system that collects not only surveys and reviews, but also social media posts and comments. For example, it collects posts from Twitter (registered trademark) and Facebook (registered trademark). The feedback collection unit also collects social media posts and comments and analyzes them as consumer feedback. For example, it collects and analyzes comments from Instagram (registered trademark). The feedback collection unit also diversifies the method of collecting feedback and develops a system that collects social media posts and comments. For example, it collects and analyzes posts from Twitter (registered trademark) and Facebook (registered trademark). This diversifies the method of collecting feedback, and can also collect social media posts and comments.

[0079] The feedback collection unit can analyze the content of the feedback, classify it into positive feedback and negative feedback, and propose improvement measures according to each. The feedback collection unit, for example, analyzes the content of the feedback and builds a system that classifies it into positive feedback and negative feedback. For example, it classifies the feedback using natural language processing technology. The feedback collection unit also proposes improvement measures according to each type of feedback based on the classified feedback. For example, it proposes improvement measures for negative feedback. The feedback collection unit also develops a system that analyzes the content of the feedback, classifies it into positive feedback and negative feedback, and proposes improvement measures according to each type. For example, it uses natural language processing technology to classify the feedback and propose improvement measures. This makes it possible to analyze the content of the feedback, classify it into positive feedback and negative feedback, and propose improvement measures according to each type.

[0080] The feedback collection unit can use the emotion estimation function to analyze the emotional tone of the feedback and propose improvement measures based on the emotion. The feedback collection unit, for example, uses the emotion estimation function to analyze the emotional tone of the feedback and build a system that proposes improvement measures based on the emotion. For example, a suggestion is made to maintain the feedback as is for feedback with a positive emotion. The feedback collection unit also analyzes the emotional tone of the feedback in real time and proposes improvement measures based on the data. For example, an improvement measure is proposed for feedback with a negative emotion. The feedback collection unit also develops a system that analyzes the emotional tone of the feedback based on the emotion estimation data and proposes improvement measures based on the emotion. For example, a suggestion is made to maintain the feedback as is for feedback with a positive emotion. In this way, the emotional tone of the feedback can be analyzed and improvement measures based on the emotion can be proposed.

[0081] The feedback collection unit can automate the collection of feedback and introduce a mechanism that allows a purchaser to input simple feedback when scanning a receipt. The feedback collection unit, for example, builds a system that automates the collection of feedback and introduces a mechanism that allows a purchaser to input simple feedback when scanning a receipt. For example, feedback is requested via a pop-up when a receipt is scanned. The feedback collection unit also introduces a mechanism that allows a purchaser to input simple feedback when scanning a receipt and automates the collection of feedback. For example, a simple survey is displayed after scanning. The feedback collection unit also develops a mechanism that automates the collection of feedback and allows a purchaser to input simple feedback when scanning a receipt. For example, feedback is requested via a pop-up when a receipt is scanned. This automates the collection of feedback and introduces a mechanism that allows a purchaser to input simple feedback when scanning a receipt.

[0082] The feedback collection unit can gamify the collection of feedback and introduce a mechanism whereby points and rewards can be earned by providing feedback. The feedback collection unit, for example, builds a system that gamifies the collection of feedback and introduces a mechanism whereby points and rewards can be earned by providing feedback. For example, points can be accumulated by providing feedback. The feedback collection unit also introduces a mechanism whereby points and rewards can be earned by providing feedback to promote the collection of feedback. For example, coupons can be received by providing feedback. The feedback collection unit also develops a mechanism whereby points and rewards can be earned by providing feedback. For example, points can be accumulated by providing feedback. This makes it possible to gamify the collection of feedback and introduce a mechanism whereby points and rewards can be earned by providing feedback.

[0083] The feedback collection unit can use the emotion estimation function to analyze the emotional tone of the feedback in real time and instantly propose a countermeasure. The feedback collection unit, for example, uses the emotion estimation function to analyze the emotional tone of the feedback in real time and build a system that instantly proposes a countermeasure based on the data. For example, an improvement measure is instantly proposed for feedback with a negative emotion. The feedback collection unit also analyzes the emotional tone of the feedback in real time and instantly proposes a countermeasure based on the data. For example, a suggestion is made to maintain the feedback with a positive emotion as is. The feedback collection unit also develops a system that analyzes the emotional tone of the feedback in real time based on the emotion estimation data and instantly proposes a countermeasure. For example, an improvement measure is instantly proposed for feedback with a negative emotion. In this way, the emotional tone of the feedback can be analyzed in real time and instantly proposed a countermeasure.

[0084] The data update unit can dynamically adjust the frequency of data updates and increase the update frequency in accordance with specific events and seasons. The data update unit, for example, builds a system that dynamically adjusts the frequency of data updates and increases the update frequency in accordance with specific events and seasons. For example, the update frequency is increased during specific periods such as the New Year holidays and summer vacation. The data update unit also adjusts the frequency of data updates in accordance with events and seasons to make optimal suggestions to consumers. For example, the update frequency is increased during event periods such as Christmas and Halloween. The data update unit also develops a system that dynamically adjusts the frequency of data updates and increases the update frequency in accordance with specific events and seasons. For example, the update frequency is increased during specific periods such as the New Year holidays and summer vacation. This makes it possible to dynamically adjust the frequency of data updates and increase the update frequency in accordance with specific events and seasons.

[0085] The data updating unit can detect abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies. The data updating unit, for example, builds a system that detects abnormal values ​​and trend changes by comparing with past data when updating data. For example, an anomaly is detected when there is a sudden change in consumption rate. The data updating unit also detects abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies. For example, an anomaly is detected when there is a sudden change in consumption rate for a specific item. The data updating unit also develops a system that detects abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies. For example, an anomaly is detected when there is a sudden change in consumption rate. This makes it possible to detect abnormal values ​​and trend changes by comparing with past data when updating data, thereby detecting anomalies.

[0086] The data update unit can use the emotion estimation function to set a priority order for data updates based on the emotional state of the purchaser. The data update unit, for example, uses the emotion estimation function to analyze the emotional state of the purchaser and builds a system that sets a priority order for data updates based on the emotion. For example, during times of high stress, data on products with a relaxing effect is updated preferentially. The data update unit also monitors the emotional state of the purchaser in real time and sets a priority order for data updates based on that data. For example, during times of strong feelings of joy, data on luxury goods is updated preferentially. The data update unit also develops a system that sets a priority order for data updates based on the emotional state of the purchaser based on the emotion estimation data. For example, during times of high stress, data on products with a relaxing effect is updated preferentially. In this way, it is possible to set a priority order for data updates based on the emotional state of the purchaser.

[0087] When updating data, the data updating unit can compare it with data from other consumers, set a benchmark, and perform optimization. The data updating unit, for example, builds a system that compares it with data from other consumers, set a benchmark, and perform optimization when updating data. For example, a benchmark is set based on data from consumers in the same region. The data updating unit also compares it with data from other consumers, set a benchmark, and perform optimization. For example, a benchmark is set based on data from consumers in the same age group. The data updating unit also develops a system that compares it with data from other consumers, set a benchmark, and perform optimization when updating data. For example, a benchmark is set based on data from consumers in the same region. This makes it possible to compare it with data from other consumers, set a benchmark, and perform optimization.

[0088] The data updating unit integrates external market data and economic data when updating data, allowing optimization from a broader perspective. The data updating unit, for example, integrates external market data and economic data when updating data, and builds a system that optimizes from a broader perspective. For example, the latest market trends are reflected. The data updating unit also makes optimal proposals to consumers based on external market data and economic data. For example, it proposes consumption patterns according to economic conditions. The data updating unit also integrates external market data and economic data when updating data, and develops a system that optimizes from a broader perspective. For example, the latest market trends are reflected. This allows external market data and economic data to be integrated and optimization from a broader perspective.

[0089] The data update unit uses the emotion estimation function to adjust the timing of data updates based on the emotional state of the purchaser, thereby enabling updates to be performed at optimal timing. The data update unit, for example, uses the emotion estimation function to analyze the emotional state of the purchaser and build a system that adjusts the timing of data updates based on that emotion. For example, during times of high stress, data on products with a relaxing effect is preferentially updated. The data update unit also monitors the emotional state of the purchaser in real time and adjusts the timing of data updates based on that data. For example, during times of strong feelings of joy, data on luxury goods is preferentially updated. The data update unit also develops a system that adjusts the timing of data updates based on the emotional state of the purchaser based on the emotion estimation data, thereby enabling updates to be performed at optimal timing. For example, during times of high stress, data on products with a relaxing effect is preferentially updated. This allows the timing of data updates to be adjusted based on the emotional state of the purchaser, enabling updates to be performed at optimal timing.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The recommendation system can also learn preferences for specific brands and products based on a user's purchasing history and make personalized suggestions. For example, if a user frequently purchases a particular brand of milk, it can prioritize suggestions about sales on that brand. Or, if a user likes a particular type of bread, it can notify them of new products and sales on that bread. Furthermore, based on a user's purchasing history, it can learn how preferences change with the seasons and make suggestions that are appropriate for the season. For example, in the summer, it can prioritize suggestions about sales on cold drinks and ice cream.

[0092] The suggestion system can also collect the user's health data and make suggestions based on their health status. For example, if the user is on a diet, it will prioritize suggestions for sale information on low-calorie foods and health foods. Also, if the user has a specific allergy, it can notify them of sale information on products that do not contain that allergen. Furthermore, it can make suggestions for nutritionally balanced meals based on the user's health data. For example, if the user is deficient in vitamins or minerals, it will suggest sale information on foods that will supplement them.

[0093] The recommendation system can also collect lifestyle data about the user and make suggestions based on that lifestyle. For example, if the user enjoys outdoor activities, it can prioritize suggestions about special offers on camping equipment and outdoor goods. If the user has a pet, it can notify the user of special offers on pet food and pet supplies. Furthermore, it can make suggestions related to specific events or hobbies based on the user's lifestyle data. For example, if the user enjoys cooking, it can suggest new recipes or special offers on cooking equipment.

[0094] The recommendation system can also collect a user's social media data and make suggestions based on trends on social media. For example, if a user frequently posts about a particular brand or product, it will prioritize suggestions for sale information on that brand or product. It can also make suggestions based on popular products and trends on social media. Furthermore, it can suggest products and sale information purchased by friends and followers based on the user's social media data. For example, if a product purchased by a friend is on sale, it will notify the user of this information.

[0095] The recommendation system can also collect the user's location information and make suggestions based on the user's current location. For example, if the user is near a specific store, the system will prioritize suggesting sale information for that store. Also, if the user is traveling, the system can notify the user of sale information and recommended stores at the user's travel destination. Furthermore, the system can make suggestions related to nearby events and fairs based on the user's location information. For example, if the user is at a specific event venue, the system will suggest sale information for products related to that event.

[0096] The proposed system can also estimate the user's emotional state and predict the consumption rate of specific products based on the estimated emotion. For example, if the user is feeling stressed, the system predicts that the consumption rate of products that help relieve stress will increase, and suggests the timing of purchasing those products. Also, if the user is feeling joyful, the system predicts that the consumption rate of luxury or luxury goods will slow down, and can adjust the timing of purchasing those products. Furthermore, based on the user's emotional state, the system can prioritize and suggest sales information for products related to specific emotions. For example, the system can notify the user of sales information for products that have a relaxing effect.

[0097] The proposed system can also estimate the user's emotional state and predict the consumption rate of specific products based on the estimated emotion. For example, if the user is feeling stressed, the system predicts that the consumption rate of products that help relieve stress will increase, and suggests the timing of purchasing those products. Also, if the user is feeling joyful, the system predicts that the consumption rate of luxury or luxury goods will slow down, and can adjust the timing of purchasing those products. Furthermore, based on the user's emotional state, the system can prioritize and suggest sales information for products related to specific emotions. For example, the system can notify the user of sales information for products that have a relaxing effect.

[0098] The proposed system can also estimate the user's emotional state and predict the consumption rate of specific products based on the estimated emotion. For example, if the user is feeling stressed, the system predicts that the consumption rate of products that help relieve stress will increase, and suggests the timing of purchasing those products. Also, if the user is feeling joyful, the system predicts that the consumption rate of luxury or luxury goods will slow down, and can adjust the timing of purchasing those products. Furthermore, based on the user's emotional state, the system can prioritize and suggest sales information for products related to specific emotions. For example, the system can notify the user of sales information for products that have a relaxing effect.

[0099] The proposed system can also estimate the user's emotional state and predict the consumption rate of specific products based on the estimated emotion. For example, if the user is feeling stressed, the system predicts that the consumption rate of products that help relieve stress will increase, and suggests the timing of purchasing those products. Also, if the user is feeling joyful, the system predicts that the consumption rate of luxury or luxury goods will slow down, and can adjust the timing of purchasing those products. Furthermore, based on the user's emotional state, the system can prioritize and suggest sales information for products related to specific emotions. For example, the system can notify the user of sales information for products that have a relaxing effect.

[0100] The proposed system can also estimate the user's emotional state and predict the consumption rate of specific products based on the estimated emotion. For example, if the user is feeling stressed, the system predicts that the consumption rate of products that help relieve stress will increase, and suggests the timing of purchasing those products. Also, if the user is feeling joyful, the system predicts that the consumption rate of luxury or luxury goods will slow down, and can adjust the timing of purchasing those products. Furthermore, based on the user's emotional state, the system can prioritize and suggest sales information for products related to specific emotions. For example, the system can notify the user of sales information for products that have a relaxing effect.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The receipt data analysis unit analyzes the receipt data of the items purchased by the consumer. For example, the receipt data includes the purchase date, the name, quantity, and price of the purchased items. Step 2: The consumption rate prediction unit predicts the consumption rate of the item based on the data analyzed by the receipt data analysis unit. For example, if a consumer purchases milk once a week, the consumption rate prediction unit predicts the consumption rate of milk to be one week. Step 3: The flyer information collection unit collects flyer information from surrounding stores. For example, the flyer information collection unit collects sale information and price information. Step 4: The suggestion unit suggests the best time and place to buy milk based on the information obtained by the consumption speed prediction unit and the flyer information collection unit. For example, the suggestion unit might suggest, "The next time you buy milk, it would be best to wait a week, and nearby store A is having a special sale, so it would be best to buy it there." Step 5: The notification unit notifies the consumer of the content proposed by the suggestion unit. For example, the notification unit may notify the consumer via a smartphone app, email, or messaging service.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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 AI 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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 AI 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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 AI 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 receipt data analysis unit that analyzes receipt data; a consumption rate prediction unit that predicts the consumption rate of goods based on the data analyzed by the receipt data analysis unit; A flyer information collection department that collects flyer information from surrounding stores; a suggestion unit that suggests optimal purchase timing and place based on information obtained by the consumption speed prediction unit and the flyer information collection unit; a notification unit that notifies the consumer of the content proposed by the proposal unit. A system characterized by:

2. The receipt data analysis unit Analyzing buyer behavior patterns and predicting fluctuations in consumption speed on specific days of the week or at specific times of the day The system of claim 1 .

3. The consumption rate prediction unit Based on seasonal and weather influences to reflect seasonal consumption patterns The system of claim 1 .

4. The consumption rate prediction unit Predicting fluctuations in spending velocity based on buyers' emotional states The system of claim 1 .

5. The receipt data analysis unit Integrate inventory data from smart refrigerators and smart kitchens to more accurately predict consumption rates The system of claim 1 .

Images