System

Abstract

An object of a system according to an embodiment is to provide information for a user to make an optimal selection in daily shopping.SOLUTION: A system according to an embodiment includes a shopping list generation unit, a barcode scanning unit, and a AI chat unit. The shopping list generation unit analyzes the past purchase history and preferences of the user and automatically generates an optimal shopping list. The barcode scanning unit scans the product and performs a price comparison. The AI chat part provides detailed information about specific products.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 techniques have had the problem that it is difficult for users to efficiently obtain information that will allow them to make optimal choices in their daily shopping.

[0005] The system according to the embodiment aims to provide information that allows users to make optimal choices in their daily shopping. [Means for solving the problem]

[0006] The system according to the embodiment includes a shopping list generation unit, a barcode scanning unit, and an AI chat unit. The shopping list generation unit analyzes a user's past purchase history and preferences to automatically generate an optimal shopping list. The barcode scanning unit scans products and compares prices. The AI ​​chat unit provides detailed information about specific products. [Effects of the Invention]

[0007] The system according to the embodiment can provide information to help users make optimal choices in their daily shopping. [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 lifestyle shopping advisor according to an embodiment of the present invention is a system that automatically reads answers written by users, summarizes them using a generation AI, calculates the similarity to model answers, and scores them. This allows the lifestyle shopping advisor to efficiently support users in their daily shopping and provide them with information and convenience to make better choices.

[0029] A lifestyle shopping advisor according to an embodiment includes a shopping list generation unit, a barcode scanning unit, and an AI chat unit. The shopping list generation unit analyzes a user's past purchase history and preferences to automatically generate an optimal shopping list. For example, the generation AI adds frequently purchased groceries and daily necessities to the list, enabling the user to manage all necessary items without missing anything. The generation AI also generates a shopping list based on data related to the user's purchase history and preferences. The barcode scanning unit scans products and performs price comparisons. For example, the generation AI compares the prices of the same product at different stores based on the barcode information of the scanned product and presents the most cost-effective option. The AI ​​chat unit provides detailed information about specific products. For example, the generation AI can provide product ingredients, usage instructions, reviews from other users, and other information based on questions about information the user wants to know, which can serve as a reference for purchasing. This allows the lifestyle shopping advisor according to an embodiment to efficiently support users' shopping.

[0030] The shopping list generation unit can propose the optimal ingredient list by taking into consideration the user's health condition and nutritional balance. For example, the generation AI of the shopping list generation unit proposes the optimal ingredient list by taking into consideration the user's health condition and nutritional balance. For example, if the user has diabetes, low-carbohydrate ingredients will be added to the list preferentially. The generation AI also selects appropriate ingredients based on the user's health goals (e.g., weight loss or muscle building). For example, low-calorie ingredients will be proposed to a user aiming to lose weight. The generation AI also considers the user's allergy information and creates an ingredient list that does not contain allergens. For example, it will propose nut-free ingredients to a user with a nut allergy. This can support the user's health.

[0031] The shopping list generation unit can automatically generate a special shopping list based on the user's life events. In the shopping list generation unit, the generation AI automatically generates a special shopping list based on life events such as the user's birthday or anniversary. For example, birthday cakes and gift candidates are added to the list. The generation AI also takes into account the birthdays of the user's family and friends and suggests gift ideas. For example, for a family member's birthday, ingredients for a special dinner are added to the list. Furthermore, based on the user's travel plans, the generation AI adds items needed at the travel destination to the list. For example, sunscreen and a swimsuit are suggested for a trip to a beach resort. This makes it possible to support the user's special events.

[0032] The barcode scanning unit can display the eco-footprint of the scanned product and suggest environmentally friendly options. The barcode scanning unit, for example, displays the eco-footprint of the scanned product and suggests environmentally friendly options. For example, it may prioritize products with a lower environmental impact even if they are the same product. It may also analyze the environmental impact of the product's manufacturing process and transportation and suggest environmentally friendly options to the user. For example, it may prioritize locally produced products. It may also enable the user to make environmentally friendly choices based on the eco-footprint data. For example, it may suggest products that are manufactured using renewable energy. This can support environmentally conscious shopping.

[0033] The barcode scanning unit can provide detailed information about the manufacturer and place of production of the scanned product. The barcode scanning unit provides detailed information about the manufacturer and place of production of the scanned product, for example. For example, the history of the product's manufacturer and the characteristics of the place of production are displayed. In addition, information about the product's manufacturing process and place of production is analyzed and provided to the user. For example, information about the product's quality and safety is displayed. In addition, the data about the manufacturer and place of production can be used to enable the user to make more informed choices. For example, locally produced products can be preferentially displayed. This can enable the user to make more informed choices.

[0034] The AI ​​chat unit can provide more personalized advice based on the user's past question history. The AI ​​chat unit can provide more personalized advice based on the user's past question history, for example. For example, it can prioritize providing information related to questions asked in the past. It can also analyze the question history and provide advice based on the user's interests and preferences. For example, it can prioritize providing information about specific brands or products. It can also build a system that provides related information and advice based on the user's question history. For example, it can generate answers to new questions based on past questions. This makes it possible to provide more appropriate advice to the user.

[0035] The AI ​​chat unit can provide video tutorials on how to use and maintain a product. The AI ​​chat unit can provide, for example, video tutorials on how to use and maintain a product, making it visually easy to understand. For example, it can provide a video showing how to assemble and use a product. It can also use video tutorials to make it easy for users to understand how to use a product. For example, it can provide a video showing maintenance procedures for a product. It can also provide video tutorials on how to use and maintain a product, providing information that is visually easy for users to understand. For example, it can provide a video showing how to clean a product. This makes it visually easy for users to understand how to use and maintain a product.

[0036] The AI ​​chat unit can add voice input to the chat function, allowing users to input questions by voice. The AI ​​chat unit, for example, adds voice input to the chat function, allowing users to input questions by voice. For example, the user inputs a question by voice using a microphone. Furthermore, using voice recognition technology, the user's voice input is converted into text, and the generation AI generates an answer. For example, the user inputs a question by voice, and the answer to that question is displayed in text. Furthermore, using the voice input function, a system is built that allows users to input questions by voice. For example, the user inputs a question by voice, and the answer to that question is provided by voice. This allows users to input questions by voice.

[0037] The AI ​​chat unit can make the chat function multilingual, allowing it to accommodate users who speak different languages. The AI ​​chat unit, for example, makes the chat function multilingual, allowing it to accommodate users who speak different languages. For example, it can accommodate multiple languages ​​such as English, French, and Chinese. In addition, it uses automatic translation technology to translate a user's question into a different language, and a generation AI generates an answer. For example, a user can input a question in Japanese, and an answer to that question is provided in English. In addition, a system can be built that uses a multilingual chat function to accommodate users who speak different languages. For example, a user can input a question in their native language, and an answer to that question is provided in their native language. This makes it possible to accommodate users who speak different languages.

[0038] Based on receipts, monthly and yearly expenditure analysis can be performed and money-saving advice can be provided. Based on receipts, monthly and yearly expenditure analysis can be performed and money-saving advice can be provided. For example, if expenditure in a particular category is high, money-saving suggestions can be made. Receipt data can also be analyzed to understand the user's spending patterns. For example, monthly expenditure can be displayed in a graph and key savings points can be shown. Furthermore, a system can be built that analyzes a user's spending and provides money-saving advice based on digitized receipts. For example, specific suggestions can be made to reduce wasteful spending. This makes it possible to analyze a user's spending and provide money-saving advice.

[0039] The contents of receipts can be automatically classified and expenditures by category can be visualized. The contents of receipts can be automatically classified and expenditures by category can be visualized. For example, by categorizing receipts into categories such as groceries, daily necessities, and hobby items. In addition, receipt data can be analyzed and expenditures by category can be displayed in graphs and charts. For example, monthly expenditures can be shown in a pie chart. Furthermore, using the automatic classification function, a system can be built that classifies the contents of receipts by category and visualizes expenditures. For example, an alert can be displayed if expenditures in a particular category are high. This makes it possible to visualize a user's expenditures by category.

[0040] Receipts can be stored in the cloud so that they can be accessed from multiple devices. Receipts can be stored in the cloud so that they can be accessed from multiple devices, for example, receipt data can be accessed from a smartphone, tablet, or PC. Cloud storage can also be used to safely store digitized receipts. For example, data backup and restore functions can be provided. A cloud-based receipt management system can also be built to enable access from multiple devices. For example, a system can be built that allows users to check receipt data from any device. This allows users to access receipt data from multiple devices.

[0041] It is possible to automatically manage warranty periods and return deadlines based on the contents of receipts. Add a function to automatically manage warranty periods and return deadlines, for example, based on the contents of receipts. For example, display an alert before a product's warranty period expires. Also, build a system that analyzes receipt data and automatically tracks the warranty period and return deadline for each product. For example, list products whose return deadlines are approaching. Also, use the automatic management function to manage warranty periods and return deadlines based on the contents of receipts. For example, display the warranty period for products purchased by the user on a calendar. This makes it easier for users to manage warranty periods and return deadlines.

[0042] It is possible to suggest optimal products based on the user's lifestyle and health condition. For example, optimal products are suggested based on the user's lifestyle and health condition. For example, if the user is health-conscious, organic foods and low-calorie products are suggested. Also, appropriate products are selected based on the user's health goals (for example, weight loss or muscle building). For example, low-calorie foods and exercise equipment are suggested for a user who is trying to lose weight. Also, taking into account the user's allergy information, products that do not contain allergens are suggested. For example, foods that do not contain nuts are suggested for a user who is allergic to nuts. In this way, it is possible to suggest products that suit the user's lifestyle and health condition.

[0043] It is possible to suggest recommended products for each season based on purchase history. Based on purchase history, for example, recommended products for each season are suggested. For example, cooling products and sunscreen are suggested in the summer. In addition, purchase history is analyzed and recommended products for each season are suggested based on products the user has previously purchased. For example, heating appliances and moisturizing cream are suggested in the winter. In addition, a system can be built that suggests trendy products for each season based on the user's purchase history. For example, pollen prevention products and new lifestyle items are suggested in the spring. This makes it possible to suggest recommended products for each season based on the user's past purchase history.

[0044] A gift suggestion function that takes into account the preferences of the user's friends and family can be added to the purchase suggestion function. For example, a gift suggestion function that takes into account the preferences of the user's friends and family can be added to the purchase suggestion function. For example, gift ideas suitable for a friend's birthday can be suggested. Also, a system can be built that analyzes the preference data of the user's friends and family and suggests appropriate gifts. For example, gift ideas that match the preferences of family members can be suggested. Also, the gift suggestion function can be used to suggest gifts that take into account the preferences of the user's friends and family. For example, gift ideas that match the hobbies of a friend can be suggested. This makes it possible to suggest gifts that match the preferences of the user's friends and family.

[0045] The purchase suggestion function can be customized based on the user's hobbies and interests. The purchase suggestion function can be customized based on the user's hobbies and interests, for example. For example, gardening supplies can be suggested to a user whose hobby is gardening. Also, a system can be built that analyzes data related to a user's hobbies and interests and suggests appropriate products. For example, camping supplies can be suggested to a user who enjoys the outdoors. Also, the purchase suggestion function based on hobbies and interests can be used to suggest the most suitable products to the user. For example, cooking utensils and recipe books can be suggested to a user whose hobby is cooking. This makes it possible to suggest products that match the user's hobbies and interests.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The lifestyle shopping advisor analyzes a user's past purchase history and preferences to automatically generate an optimal shopping list. For example, the generation AI can add frequently purchased groceries and daily necessities to the list, allowing the user to manage all necessary items without missing anything. The generation AI also generates a shopping list based on data related to the user's purchase history and preferences. The barcode scanning unit scans products and performs price comparisons. For example, the generation AI compares the prices of the same product at different stores based on the barcode information of the scanned product and presents the most cost-effective option. The AI ​​chat unit provides detailed information about specific products. For example, the generation AI can provide product ingredients, usage instructions, and reviews from other users based on questions about information the user wants to know, which can serve as a reference for purchasing. This allows the lifestyle shopping advisor according to the embodiment to efficiently support users in their shopping.

[0048] The shopping list generation unit can propose an optimal ingredient list taking into account the user's health condition and nutritional balance. For example, if the user has diabetes, low-carbohydrate ingredients will be added to the list first. The generation AI also selects appropriate ingredients based on the user's health goals (e.g., weight loss or muscle building). For example, low-calorie ingredients will be proposed to a user aiming to lose weight. The generation AI also takes into account the user's allergy information and creates an ingredient list that does not contain allergens. For example, it will propose nut-free ingredients to a user with a nut allergy. This can support the user's health.

[0049] The shopping list generation unit can automatically generate special shopping lists based on the user's life events. For example, the generation AI automatically generates special shopping lists based on life events such as the user's birthday or anniversary. For example, birthday cakes and gift candidates are added to the list. The generation AI also takes into account the birthdays of the user's family and friends and suggests gift ideas. For example, for a family member's birthday, ingredients for a special dinner are added to the list. Furthermore, based on the user's travel plans, the generation AI adds items needed at the travel destination to the list. For example, sunscreen and a swimsuit are suggested for a trip to a beach resort. This supports the user's special events.

[0050] The barcode scanning unit can display the eco-footprint of the scanned product and suggest environmentally friendly options. For example, it can prioritize products with a lower environmental impact even if they are the same product. It can also analyze the environmental impact of the product's manufacturing process and transportation and suggest environmentally friendly options to the user. For example, it can prioritize locally produced products. It can also help users make environmentally friendly choices based on the eco-footprint data. For example, it can suggest products that are manufactured using renewable energy. This can support environmentally conscious shopping.

[0051] The barcode scanning unit can provide detailed information about the manufacturer and place of production of the scanned product. For example, it can display the history of the product's manufacturer and the characteristics of the place of production. It can also analyze information about the product's manufacturing process and place of production and provide it to the user. For example, it can display information about the product's quality and safety. It can also help the user make more informed choices based on data about the manufacturer and place of production. For example, it can prioritize locally produced products. This can help the user make more informed choices.

[0052] The AI ​​chat unit can provide more personalized advice based on the user's past question history. For example, it can prioritize providing information related to questions previously asked. It can also analyze the question history and provide advice based on the user's interests and preferences. For example, it can prioritize providing information about specific brands or products. It can also build a system that provides related information and advice based on the user's question history. For example, it can generate answers to new questions based on past questions. This makes it possible to provide more appropriate advice to the user.

[0053] The AI ​​chat unit can provide video tutorials on how to use and maintain a product. For example, it can provide videos showing how to assemble and use a product. It can also use video tutorials to make it easy for users to understand how to use a product. For example, it can provide videos showing maintenance procedures for a product. It can also provide video tutorials on how to use and maintain a product, providing information that is visually easy for users to understand. For example, it can provide a video showing how to clean a product. This makes it easy for users to visually understand how to use and maintain a product.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The shopping list generator analyzes the user's past purchase history and preferences and automatically generates an optimal shopping list. For example, the generation AI can add the groceries and daily necessities that the user frequently purchases to the list, ensuring that all necessary items are managed properly. The generation AI also generates the shopping list based on data related to the user's purchase history and preferences. Step 2: The barcode scanning unit scans the product and performs price comparisons. For example, the generation AI uses the barcode information of the scanned product to compare the prices at which the same product is sold at different stores and presents the best value option. Step 3: The AI ​​chat section provides detailed information about a specific product. For example, based on questions about the information the user wants to know, the AI ​​can provide information about the product's ingredients, how to use it, and reviews from other users, which can be used as a reference for purchasing.

[0056] (Example 2) The lifestyle shopping advisor according to an embodiment of the present invention is a system that automatically reads answers written by users, summarizes them using a generation AI, calculates the similarity to model answers, and scores them. This allows the lifestyle shopping advisor to efficiently support users in their daily shopping and provide them with information and convenience to make better choices.

[0057] A lifestyle shopping advisor according to an embodiment includes a shopping list generation unit, a barcode scanning unit, and an AI chat unit. The shopping list generation unit analyzes a user's past purchase history and preferences to automatically generate an optimal shopping list. For example, the generation AI adds frequently purchased groceries and daily necessities to the list, enabling the user to manage all necessary items without missing anything. The generation AI also generates a shopping list based on data related to the user's purchase history and preferences. The barcode scanning unit scans products and performs price comparisons. For example, the generation AI compares the prices of the same product at different stores based on the barcode information of the scanned product and presents the most cost-effective option. The AI ​​chat unit provides detailed information about specific products. For example, the generation AI can provide product ingredients, usage instructions, reviews from other users, and other information based on questions about information the user wants to know, which can serve as a reference for purchasing. This allows the lifestyle shopping advisor according to an embodiment to efficiently support users' shopping.

[0058] The shopping list generation unit can propose the optimal ingredient list by taking into consideration the user's health condition and nutritional balance. For example, the generation AI of the shopping list generation unit proposes the optimal ingredient list by taking into consideration the user's health condition and nutritional balance. For example, if the user has diabetes, low-carbohydrate ingredients will be added to the list preferentially. The generation AI also selects appropriate ingredients based on the user's health goals (e.g., weight loss or muscle building). For example, low-calorie ingredients will be proposed to a user aiming to lose weight. The generation AI also considers the user's allergy information and creates an ingredient list that does not contain allergens. For example, it will propose nut-free ingredients to a user with a nut allergy. This can support the user's health.

[0059] The shopping list generation unit can automatically generate a special shopping list based on the user's life events. In the shopping list generation unit, the generation AI automatically generates a special shopping list based on life events such as the user's birthday or anniversary. For example, birthday cakes and gift candidates are added to the list. The generation AI also takes into account the birthdays of the user's family and friends and suggests gift ideas. For example, for a family member's birthday, ingredients for a special dinner are added to the list. Furthermore, based on the user's travel plans, the generation AI adds items needed at the travel destination to the list. For example, sunscreen and a swimsuit are suggested for a trip to a beach resort. This makes it possible to support the user's special events.

[0060] The shopping list generation unit can use the emotion estimation function to generate a shopping list that matches the user's mood. The shopping list generation unit, for example, uses the emotion estimation function to generate a shopping list that matches the user's mood. For example, if the user is feeling stressed, items that have a relaxing effect are added to the list. The shopping list generation unit also analyzes the user's emotional state in real time and suggests ingredients and items that are useful for improving the mood. For example, if the user is tired, ingredients that are suitable for replenishing energy are added to the list. The shopping list generation unit also generates a shopping list that matches the user's mood based on the emotion estimation data and suggests items that elicit positive emotions. For example, if the user is feeling down, items that are useful for changing mood are added to the list. This makes it possible to support shopping that matches the user's mood.

[0061] The barcode scanning unit can display the eco-footprint of the scanned product and suggest environmentally friendly options. The barcode scanning unit, for example, displays the eco-footprint of the scanned product and suggests environmentally friendly options. For example, it may prioritize products with a lower environmental impact even if they are the same product. It may also analyze the environmental impact of the product's manufacturing process and transportation and suggest environmentally friendly options to the user. For example, it may prioritize locally produced products. It may also enable the user to make environmentally friendly choices based on the eco-footprint data. For example, it may suggest products that are manufactured using renewable energy. This can support environmentally conscious shopping.

[0062] The barcode scanning unit can provide detailed information about the manufacturer and place of production of the scanned product. The barcode scanning unit provides detailed information about the manufacturer and place of production of the scanned product, for example. For example, the history of the product's manufacturer and the characteristics of the place of production are displayed. In addition, information about the product's manufacturing process and place of production is analyzed and provided to the user. For example, information about the product's quality and safety is displayed. In addition, the data about the manufacturer and place of production can be used to enable the user to make more informed choices. For example, locally produced products can be preferentially displayed. This can enable the user to make more informed choices.

[0063] The barcode scanning unit can use the emotion estimation function to analyze the emotion a user feels when making price comparisons and provide advice to reduce stress. The barcode scanning unit, for example, uses the emotion estimation function to analyze the emotion a user feels when making price comparisons in real time. For example, if the user is feeling stressed, it provides advice that has a relaxing effect. It also analyzes the user's emotional state and provides specific advice to reduce stress. For example, it suggests an environment where the user can relax when making price comparisons. It also provides advice to reduce stress a user feels when making price comparisons based on the emotion estimation data. For example, it suggests taking a break if the user is tired. This can reduce the user's stress.

[0064] The AI ​​chat unit can provide more personalized advice based on the user's past question history. The AI ​​chat unit can provide more personalized advice based on the user's past question history, for example. For example, it can prioritize providing information related to questions asked in the past. It can also analyze the question history and provide advice based on the user's interests and preferences. For example, it can prioritize providing information about specific brands or products. It can also build a system that provides related information and advice based on the user's question history. For example, it can generate answers to new questions based on past questions. This makes it possible to provide more appropriate advice to the user.

[0065] The AI ​​chat unit can provide video tutorials on how to use and maintain a product. The AI ​​chat unit can provide, for example, video tutorials on how to use and maintain a product, making it visually easy to understand. For example, it can provide a video showing how to assemble and use a product. It can also use video tutorials to make it easy for users to understand how to use a product. For example, it can provide a video showing maintenance procedures for a product. It can also provide video tutorials on how to use and maintain a product, providing information that is visually easy for users to understand. For example, it can provide a video showing how to clean a product. This makes it visually easy for users to understand how to use and maintain a product.

[0066] The AI ​​chat unit can use the emotion estimation function to analyze the emotional reactions to the user's questions and generate more empathetic answers. For example, the AI ​​chat unit uses the emotion estimation function to analyze the emotional reactions to the user's questions in real time and generate empathetic answers. For example, if the user is feeling anxious, it provides an answer that gives a sense of security. In addition, a system is built that analyzes the user's emotional state and generates empathetic answers. For example, if the user is in trouble, it provides an answer that includes words of encouragement. In addition, based on the emotion estimation data, it analyzes the emotional reactions to the user's questions and generates empathetic answers. For example, if the user is happy, it provides an answer that includes words of congratulations. This allows for a more empathetic response to the user.

[0067] The AI ​​chat unit can add voice input to the chat function, allowing users to input questions by voice. The AI ​​chat unit, for example, adds voice input to the chat function, allowing users to input questions by voice. For example, the user inputs a question by voice using a microphone. Furthermore, using voice recognition technology, the user's voice input is converted into text, and the generation AI generates an answer. For example, the user inputs a question by voice, and the answer to that question is displayed in text. Furthermore, using the voice input function, a system is built that allows users to input questions by voice. For example, the user inputs a question by voice, and the answer to that question is provided by voice. This allows users to input questions by voice.

[0068] The AI ​​chat unit can make the chat function multilingual, allowing it to accommodate users who speak different languages. The AI ​​chat unit, for example, makes the chat function multilingual, allowing it to accommodate users who speak different languages. For example, it can accommodate multiple languages ​​such as English, French, and Chinese. In addition, it uses automatic translation technology to translate a user's question into a different language, and a generation AI generates an answer. For example, a user can input a question in Japanese, and an answer to that question is provided in English. In addition, a system can be built that uses a multilingual chat function to accommodate users who speak different languages. For example, a user can input a question in their native language, and an answer to that question is provided in their native language. This makes it possible to accommodate users who speak different languages.

[0069] The AI ​​chat unit uses the emotion estimation function to monitor the emotions of users when using chat in real time and provide optimal advice. The AI ​​chat unit, for example, uses the emotion estimation function to monitor the emotions of users when using chat in real time. For example, if the user is feeling stressed, it will provide advice that has a relaxing effect. It also builds a system that analyzes the user's emotional state and provides optimal advice. For example, if the user is tired, it will suggest taking a break. It also monitors the emotions of users when using chat in real time based on the emotion estimation data and provides optimal advice. For example, if the user is unsure, it will suggest that they refer to the evaluations of other users. This makes it possible to provide optimal advice according to the user's emotions.

[0070] Based on receipts, monthly and yearly expenditure analysis can be performed and money-saving advice can be provided. Based on receipts, monthly and yearly expenditure analysis can be performed and money-saving advice can be provided. For example, if expenditure in a particular category is high, money-saving suggestions can be made. Receipt data can also be analyzed to understand the user's spending patterns. For example, monthly expenditure can be displayed in a graph and key savings points can be shown. Furthermore, a system can be built that analyzes a user's spending and provides money-saving advice based on digitized receipts. For example, specific suggestions can be made to reduce wasteful spending. This makes it possible to analyze a user's spending and provide money-saving advice.

[0071] The contents of receipts can be automatically classified and expenditures by category can be visualized. The contents of receipts can be automatically classified and expenditures by category can be visualized. For example, by categorizing receipts into categories such as groceries, daily necessities, and hobby items. In addition, receipt data can be analyzed and expenditures by category can be displayed in graphs and charts. For example, monthly expenditures can be shown in a pie chart. Furthermore, using the automatic classification function, a system can be built that classifies the contents of receipts by category and visualizes expenditures. For example, an alert can be displayed if expenditures in a particular category are high. This makes it possible to visualize a user's expenditures by category.

[0072] It is possible to analyze emotions when managing receipts and make suggestions to reduce stress. Emotions when managing receipts are analyzed, for example, using an emotion estimation function to analyze the emotions a user has when managing receipts in real time. For example, if the user is feeling stressed, suggestions that have a relaxing effect are made. A system is also constructed that analyzes the user's emotional state and makes specific suggestions to reduce stress. For example, an environment that allows for relaxation when managing receipts is suggested. Furthermore, based on the emotion estimation data, the emotions a user has when managing receipts are analyzed and suggestions to reduce stress are made. For example, if the user is tired, a break is suggested. This makes it possible to reduce the user's stress.

[0073] Receipts can be stored in the cloud so that they can be accessed from multiple devices. Receipts can be stored in the cloud so that they can be accessed from multiple devices, for example, receipt data can be accessed from a smartphone, tablet, or PC. Cloud storage can also be used to safely store digitized receipts. For example, data backup and restore functions can be provided. A cloud-based receipt management system can also be built to enable access from multiple devices. For example, a system can be built that allows users to check receipt data from any device. This allows users to access receipt data from multiple devices.

[0074] It is possible to automatically manage warranty periods and return deadlines based on the contents of receipts. Add a function to automatically manage warranty periods and return deadlines, for example, based on the contents of receipts. For example, display an alert before a product's warranty period expires. Also, build a system that analyzes receipt data and automatically tracks the warranty period and return deadline for each product. For example, list products whose return deadlines are approaching. Also, use the automatic management function to manage warranty periods and return deadlines based on the contents of receipts. For example, display the warranty period for products purchased by the user on a calendar. This makes it easier for users to manage warranty periods and return deadlines.

[0075] It is possible to monitor emotions when managing receipts in real time and suggest the optimal management method. Emotions when managing receipts are monitored in real time, for example, using an emotion estimation function to monitor the emotions of users when managing receipts in real time. For example, if a user is feeling stressed, a management method that has a relaxing effect is suggested. It is also possible to build a system that analyzes the user's emotional state and suggests the optimal management method. For example, if a user is tired, an easy management method is suggested. It is also possible to monitor the emotions of users when managing receipts in real time based on emotion estimation data and suggest the optimal management method. For example, if a user is unsure, it is suggested that they refer to the reviews of other users. This makes it possible to suggest the optimal management method according to the user's emotions.

[0076] It is possible to suggest optimal products based on the user's lifestyle and health condition. For example, optimal products are suggested based on the user's lifestyle and health condition. For example, if the user is health-conscious, organic foods and low-calorie products are suggested. Also, appropriate products are selected based on the user's health goals (for example, weight loss or muscle building). For example, low-calorie foods and exercise equipment are suggested for a user who is trying to lose weight. Also, taking into account the user's allergy information, products that do not contain allergens are suggested. For example, foods that do not contain nuts are suggested for a user who is allergic to nuts. In this way, it is possible to suggest products that suit the user's lifestyle and health condition.

[0077] It is possible to suggest recommended products for each season based on purchase history. Based on purchase history, for example, recommended products for each season are suggested. For example, cooling products and sunscreen are suggested in the summer. In addition, purchase history is analyzed and recommended products for each season are suggested based on products the user has previously purchased. For example, heating appliances and moisturizing cream are suggested in the winter. In addition, a system can be built that suggests trendy products for each season based on the user's purchase history. For example, pollen prevention products and new lifestyle items are suggested in the spring. This makes it possible to suggest recommended products for each season based on the user's past purchase history.

[0078] The emotion estimation function can be used to suggest products that match the user's mood, increasing their desire to buy. The emotion estimation function can be used to suggest products that match the user's mood, for example. For example, if the user is feeling stressed, products with a relaxing effect can be suggested. The user's emotional state can also be analyzed in real time to suggest products that help improve their mood. For example, if the user is tired, products that are suitable for replenishing energy can be suggested. The emotion estimation data can also be used to suggest products that match the user's mood, increasing their desire to buy. For example, if the user is feeling down, products that help change their mood can be suggested. In this way, products that match the user's mood can be suggested, increasing their desire to buy.

[0079] A gift suggestion function that takes into account the preferences of the user's friends and family can be added to the purchase suggestion function. For example, a gift suggestion function that takes into account the preferences of the user's friends and family can be added to the purchase suggestion function. For example, gift ideas suitable for a friend's birthday can be suggested. Also, a system can be built that analyzes the preference data of the user's friends and family and suggests appropriate gifts. For example, gift ideas that match the preferences of family members can be suggested. Also, the gift suggestion function can be used to suggest gifts that take into account the preferences of the user's friends and family. For example, gift ideas that match the hobbies of a friend can be suggested. This makes it possible to suggest gifts that match the preferences of the user's friends and family.

[0080] The purchase suggestion function can be customized based on the user's hobbies and interests. The purchase suggestion function can be customized based on the user's hobbies and interests, for example. For example, gardening supplies can be suggested to a user whose hobby is gardening. Also, a system can be built that analyzes data related to a user's hobbies and interests and suggests appropriate products. For example, camping supplies can be suggested to a user who enjoys the outdoors. Also, the purchase suggestion function based on hobbies and interests can be used to suggest the most suitable products to the user. For example, cooking utensils and recipe books can be suggested to a user whose hobby is cooking. This makes it possible to suggest products that match the user's hobbies and interests.

[0081] Using the emotion estimation function, it is possible to monitor the emotions of a user when receiving a purchase suggestion in real time and make the most appropriate suggestion. Using the emotion estimation function, for example, the emotions of a user when receiving a purchase suggestion can be monitored in real time. For example, if the user is feeling stressed, products with a relaxing effect can be suggested. Also, a system can be built that analyzes the user's emotional state and makes the most appropriate suggestion. For example, if the user is tired, products suitable for replenishing energy can be suggested. Furthermore, based on the emotion estimation data, it is possible to monitor the emotions of a user when receiving a purchase suggestion in real time and make the most appropriate suggestion. For example, if the user is unsure, it can be suggested that they refer to the reviews of other users. This makes it possible to make the most appropriate suggestion according to the user's emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The lifestyle shopping advisor analyzes a user's past purchase history and preferences to automatically generate an optimal shopping list. For example, the generation AI can add frequently purchased groceries and daily necessities to the list, allowing the user to manage all necessary items without missing anything. The generation AI also generates a shopping list based on data related to the user's purchase history and preferences. The barcode scanning unit scans products and performs price comparisons. For example, the generation AI compares the prices of the same product at different stores based on the barcode information of the scanned product and presents the most cost-effective option. The AI ​​chat unit provides detailed information about specific products. For example, the generation AI can provide product ingredients, usage instructions, and reviews from other users based on questions about information the user wants to know, which can serve as a reference for purchasing. This allows the lifestyle shopping advisor according to the embodiment to efficiently support users in their shopping.

[0084] The shopping list generation unit can propose an optimal ingredient list taking into account the user's health condition and nutritional balance. For example, if the user has diabetes, low-carbohydrate ingredients will be added to the list first. The generation AI also selects appropriate ingredients based on the user's health goals (e.g., weight loss or muscle building). For example, low-calorie ingredients will be proposed to a user aiming to lose weight. The generation AI also takes into account the user's allergy information and creates an ingredient list that does not contain allergens. For example, it will propose nut-free ingredients to a user with a nut allergy. This can support the user's health.

[0085] The shopping list generation unit can automatically generate special shopping lists based on the user's life events. For example, the generation AI automatically generates special shopping lists based on life events such as the user's birthday or anniversary. For example, birthday cakes and gift candidates are added to the list. The generation AI also takes into account the birthdays of the user's family and friends and suggests gift ideas. For example, for a family member's birthday, ingredients for a special dinner are added to the list. Furthermore, based on the user's travel plans, the generation AI adds items needed at the travel destination to the list. For example, sunscreen and a swimsuit are suggested for a trip to a beach resort. This supports the user's special events.

[0086] The shopping list generation unit can use the emotion estimation function to generate a shopping list that matches the user's mood. For example, if the user is feeling stressed, items with a relaxing effect are added to the list. The shopping list generation unit can also analyze the user's emotional state in real time and suggest ingredients and items that will help improve the mood. For example, if the user is tired, ingredients that are suitable for replenishing energy are added to the list. The shopping list generation unit can also generate a shopping list that matches the user's mood based on the emotion estimation data and suggest items that will elicit positive emotions. For example, if the user is feeling down, items that will help change their mood are added to the list. This makes it possible to support shopping that suits the user's mood.

[0087] The barcode scanning unit can display the eco-footprint of the scanned product and suggest environmentally friendly options. For example, it can prioritize products with a lower environmental impact even if they are the same product. It can also analyze the environmental impact of the product's manufacturing process and transportation and suggest environmentally friendly options to the user. For example, it can prioritize locally produced products. It can also help users make environmentally friendly choices based on the eco-footprint data. For example, it can suggest products that are manufactured using renewable energy. This can support environmentally conscious shopping.

[0088] The barcode scanning unit can provide detailed information about the manufacturer and place of production of the scanned product. For example, it can display the history of the product's manufacturer and the characteristics of the place of production. It can also analyze information about the product's manufacturing process and place of production and provide it to the user. For example, it can display information about the product's quality and safety. It can also help the user make more informed choices based on data about the manufacturer and place of production. For example, it can prioritize locally produced products. This can help the user make more informed choices.

[0089] The barcode scanning unit can use the emotion estimation function to analyze the emotions of the user when making price comparisons and provide advice to reduce stress. For example, if the user is feeling stressed, it can provide advice that has a relaxing effect. It can also analyze the user's emotional state and provide specific advice to reduce stress. For example, it can suggest an environment where the user can relax when making price comparisons. It can also provide advice to reduce stress when the user makes price comparisons based on the emotion estimation data. For example, it can suggest taking a break if the user is tired. This can reduce the user's stress.

[0090] The AI ​​chat unit can provide more personalized advice based on the user's past question history. For example, it can prioritize providing information related to questions previously asked. It can also analyze the question history and provide advice based on the user's interests and preferences. For example, it can prioritize providing information about specific brands or products. It can also build a system that provides related information and advice based on the user's question history. For example, it can generate answers to new questions based on past questions. This makes it possible to provide more appropriate advice to the user.

[0091] The AI ​​chat unit can provide video tutorials on how to use and maintain a product. For example, it can provide videos showing how to assemble and use a product. It can also use video tutorials to make it easy for users to understand how to use a product. For example, it can provide videos showing maintenance procedures for a product. It can also provide video tutorials on how to use and maintain a product, providing information that is visually easy for users to understand. For example, it can provide a video showing how to clean a product. This makes it easy for users to visually understand how to use and maintain a product.

[0092] The AI ​​chat unit uses the emotion estimation function to analyze the emotional response to the user's question and generate a more empathetic response. For example, if the user is feeling anxious, it will provide an answer that gives a sense of security. We have also built a system that analyzes the user's emotional state and generates empathetic answers. For example, if the user is in trouble, it will provide an answer that includes words of encouragement. We have also analyzed the emotional response to the user's question based on the emotion estimation data and generated an empathetic answer. For example, if the user is happy, it will provide an answer that includes words of congratulations. This allows us to respond to the user in a more empathetic way.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The shopping list generator analyzes the user's past purchase history and preferences and automatically generates an optimal shopping list. For example, the generation AI can add the groceries and daily necessities that the user frequently purchases to the list, ensuring that all necessary items are managed properly. The generation AI also generates the shopping list based on data related to the user's purchase history and preferences. Step 2: The barcode scanning unit scans the product and performs price comparisons. For example, the generation AI uses the barcode information of the scanned product to compare the prices at which the same product is sold at different stores and presents the best value option. Step 3: The AI ​​chat section provides detailed information about a specific product. For example, based on questions about the information the user wants to know, the AI ​​can provide information about the product's ingredients, how to use it, and reviews from other users, which can be used as a reference for purchasing.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0139] 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 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.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 shopping list generation unit that analyzes a user's past purchase history and preferences and automatically generates an optimal shopping list; A barcode scanning section that scans products and compares prices; An AI chat section that provides detailed information about specific products. A system characterized by:

2. The shopping list generation unit It proposes optimal food lists based on the user's health condition and nutritional balance.

2. The system of claim 1.

3. The shopping list generation unit Auto-generate special shopping lists based on user life events 2. The system of claim 1.

4. The shopping list generation unit Generate a shopping list tailored to the user's mood 2. The system of claim 1.

5. The barcode scanning unit Shows the eco-footprint of scanned items and suggests eco-friendly options 2. The system of claim 1.

Images