system

Abstract

To provide a system that efficiently manages product best-before dates and expiration dates and reduces food loss.SOLUTION: A system according to an embodiment includes an acquisition unit, a management unit, a notification unit, a recommendation unit, and a proposal unit. The acquisition unit acquires information indicating an RFID tag of a product or information indicating a photo of the product. The management unit manages the best-before date or expiration date of the product based on the information acquired by the acquisition unit. The notification unit transmits a notification when the best-before date or expiration date managed by the management unit approaches. The recommendation unit recommends a recipe using products having close best-before dates or expiration dates managed by the management unit. The proposal unit proposes ingredients that are lacking necessary for the recipe recommended by the recommendation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, managing product expiration dates and best-before dates is a hassle, and there is room for improvement in terms of reducing food waste.

[0005] The system according to the embodiment aims to efficiently manage product best-before dates and expiration dates, thereby reducing food waste. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a management unit, a notification unit, a recommendation unit, and a suggestion unit. The acquisition unit acquires information indicating an RFID tag of a product or information indicating a photo of the product. The management unit manages the best-before date or expiration date of the product based on the information acquired by the acquisition unit. The notification unit transmits a notification when the best-before date or expiration date managed by the management unit approaches. The recommendation unit recommends recipes using items with close best-before dates or expiration dates managed by the management unit. The suggestion unit suggests missing ingredients necessary for a recipe recommended by the recommendation unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently manage product best-before dates and expiration dates, thereby reducing food waste. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The food waste reduction system according to the embodiment of the present invention is a system that acquires information indicating a product's RFID tag or information indicating a product's photo, and manages information on the expiration date or best-before date. This system sends a notification when the expiration date or best-before date is approaching, preventing the user from forgetting to use the product. In addition, the system recommends recipes using products with close expiration dates, and suggests ingredients that are missing at that time. The system provides information on where the suggested ingredients can be purchased on an e-commerce site or at a nearby supermarket for cheap. For example, when a user goes shopping, the system reads the RFID tag of the product or takes a photo of the product. This information is input to the acquisition unit. Next, the system manages the expiration date or best-before date of the product based on the acquired information. The management unit analyzes the acquired information and registers the expiration date or best-before date of each product in a database. For example, the system reads the expiration date from the product's RFID tag and stores it in the database. When the expiration date or best-before date is approaching, the notification unit sends a notification to the user. For example, a notification is sent through a smartphone app to inform the user of the product's expiration date. This prevents the user from forgetting to use the product. Furthermore, the system recommends recipes that use items with close expiration dates. The recommendation unit generates recipes by combining items with close expiration dates managed by the management unit. For example, it proposes recipes for dishes using vegetables and meat with close expiration dates. It proposes ingredients that are missing for a recommended recipe. The suggestion unit identifies ingredients that are missing based on the recommended recipe and suggests them to the user. For example, it suggests seasonings and additional ingredients that are necessary for the recipe. It provides information that the suggested ingredients can be purchased on an e-commerce site or can be purchased cheaply at a nearby supermarket. For example, it provides a link to purchase on an e-commerce site or displays flyer information for a nearby supermarket. This reduces food waste and enables the user to use ingredients efficiently. This allows the food waste reduction system to enable the user to use ingredients efficiently.

[0029] The food loss reduction system according to the embodiment includes an acquisition unit, a management unit, a notification unit, a recommendation unit, and a suggestion unit. The acquisition unit acquires information indicating an RFID tag of a product or information indicating a photo of the product. For example, when a user goes shopping, the RFID tag of the product is read or a photo of the product is taken. This information is input to the acquisition unit. The acquisition unit, for example, reads the RFID tag of the product using an RFID reader. The acquisition unit can also take a photo of the product using a camera. For example, the RFID reader can read RFID tags in the UHF band or the HF band. The camera takes a high-resolution photo and acquires product information. The management unit manages the expiration date or best-before date of the product based on the information acquired by the acquisition unit. The management unit analyzes the acquired information and registers the expiration date or best-before date of each product in a database. For example, the expiration date is read from the RFID tag of the product and stored in the database. The management unit manages the expiration date or best-before date based on the information stored in the database. The notification unit transmits a notification when the expiration date or best-before date managed by the management unit is approaching. The notification unit transmits a notification to the user to prevent forgetting to use the product. For example, a notification is sent to inform the user of a product whose expiration date is approaching through a smartphone app. The notification unit can also transmit the notification using email or SMS. The recommendation unit recommends a recipe using products with a close expiration date or best-before date managed by the management unit. The recommendation unit generates a recipe by combining products with a close expiration date managed by the management unit. For example, the recommendation unit proposes a recipe for a dish using vegetables and meat with a close expiration date. The recommendation unit can also generate a recipe using a generation AI. The generation AI generates a recipe using, for example, a text generation AI (for example, LLM). The suggestion unit proposes missing ingredients required for the recommended recipe. The suggestion unit identifies missing ingredients based on the recommended recipe and suggests them to the user. For example, it proposes seasonings and additional ingredients required for the recipe. The suggestion unit can also provide a link to purchase the ingredients on an e-commerce site. For example, it could provide a link to purchase from an e-commerce site or display flyers about a nearby supermarket.As a result, the food waste reduction system according to the embodiment can enable users to use ingredients efficiently.

[0030] The acquisition unit acquires information indicating the RFID tag of a product or information indicating a photo of the product. For example, when a user goes shopping, the RFID tag of the product is read or a photo of the product is taken. This information is input to the acquisition unit. For example, the acquisition unit reads the RFID tag of the product using an RFID reader. The acquisition unit can also take a photo of the product using a camera. For example, the RFID reader can read RFID tags in the UHF band or the HF band. The camera takes a high-resolution photo and acquires the product information. The acquisition unit is designed to link these devices so that the user can easily acquire product information. For example, by using a camera and an RFID reader built into a smartphone, the user can easily acquire product information through an app. Furthermore, the acquisition unit also has a function of transmitting the acquired information to a cloud server and storing it in a central database. This enables consistent information management even if the user uses different devices. The acquisition unit can also acquire more diverse product information by adding a barcode reader or a two-dimensional code (e.g., QR Code (registered trademark)) reader. This enables the acquisition unit to efficiently collect detailed information about products purchased by users and provide it to the management unit or other departments.

[0031] The management unit manages the expiration date or best-before date of the product based on the information acquired by the acquisition unit. The management unit analyzes the acquired information and registers the expiration date or best-before date of each product in a database. For example, the management unit reads the expiration date from the RFID tag of the product and stores it in the database. The management unit manages the expiration date or best-before date based on the information stored in the database. The management unit can use OCR (optical character recognition) technology or natural language processing technology to analyze the acquired information. This allows the information written on the label or package of the product to be automatically read and registered in the database. Furthermore, the management unit periodically updates the information stored in the database to maintain the latest state. For example, when the expiration date of a product is approaching, the management unit transmits the information to the notification unit and notifies the user. In addition, the management unit has a load distribution function for efficiently managing information even when multiple users use the system at the same time. This allows the management unit to accurately manage the expiration dates and best-before dates of products purchased by users and provide a foundation for reducing food waste.

[0032] The notification unit transmits a notification when the expiration date or best-before date managed by the management unit is approaching. The notification unit transmits a notification to the user to prevent forgetting to use the product. For example, a notification is sent through a smartphone app to inform the user of a product whose expiration date is approaching. The notification unit can also transmit the notification using email or SMS. The notification unit can customize the notification method according to the user's preferences. For example, if the user prefers push notifications, the notification is sent in real time through the app. In addition, notifications can be sent periodically to users who prefer email or SMS. Furthermore, the notification unit can customize the notification content according to the user's language or region. In this way, the notification unit supports the user to efficiently use ingredients without overlooking the expiration date or best-before date. The notification unit also has a function of tracking the user's actions after receiving the notification and evaluating the effectiveness of the notification. For example, the notification unit checks whether the user has consumed the product after receiving the notification and analyzes the effectiveness of the notification. In this way, the notification unit can continuously improve the accuracy and effectiveness of the notification and improve the user's convenience.

[0033] The recommendation unit recommends recipes using products with close expiration dates or expiry dates managed by the management unit. The recommendation unit generates recipes by combining products with close expiration dates managed by the management unit. For example, it proposes recipes for dishes using vegetables and meat with close expiration dates. The recommendation unit can also generate recipes using a generation AI. The generation AI generates recipes using, for example, a text generation AI (e.g., LLM). The generation AI can propose optimal recipes taking into account the user's preferences and past usage history. For example, it generates appropriate recipes based on dishes that the user liked to make in the past and allergy information. Furthermore, the recommendation unit can also propose recipes that use seasonal ingredients taking into account the characteristics of the season and region. This allows the user to enjoy delicious dishes without wasting ingredients with close expiration dates. The recommendation unit also has a function of collecting feedback after the user actually tries the recipe and evaluating the accuracy and satisfaction of the recipe. This allows the recommendation unit to continuously provide recipes that match the user's preferences and contribute to reducing food waste.

[0034] The suggestion unit suggests missing ingredients required for the recommended recipe. The suggestion unit identifies missing ingredients based on the recommended recipe and suggests them to the user. For example, it suggests seasonings and additional ingredients required for the recipe. The suggestion unit can also provide a link to purchase at an e-commerce site. For example, it provides a link to purchase at an e-commerce site or displays flyer information of a nearby supermarket. The suggestion unit supports the user to easily purchase missing ingredients. Furthermore, the suggestion unit can also suggest the optimal purchase destination in consideration of the user's purchase history and preferences. For example, it preferentially displays supermarkets and online stores that the user frequently uses. In addition, if a specific ingredient is on sale or if a coupon is available, the suggestion unit also provides that information. This allows the user to purchase ingredients efficiently and economically. The suggestion unit also has a function of collecting feedback after the user purchases the suggested ingredients and evaluating the accuracy and satisfaction of the suggested content. This allows the suggestion unit to continuously improve the user's purchasing experience and contribute to reducing food waste.

[0035] The food loss reduction system includes a reading unit that reads the RFID tag of a product, and an acquisition unit that acquires information indicating the RFID tag of the product read by the reading unit. The reading unit reads the RFID tag of the product. For example, the RFID tag of the product is read using an RFID reader. The RFID reader can read RFID tags in the UHF band or the HF band. The reading unit reads the RFID tag of the product using the RFID reader and transmits the information to the acquisition unit. This makes it possible to acquire information using the RFID tag. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input data acquired by the RFID reader to AI, which analyzes the data to acquire product information.

[0036] The food waste reduction system includes an imaging unit that captures a photo of a product, and an acquisition unit that acquires information about the photo of the product captured by the imaging unit. The imaging unit captures a photo of the product. For example, a camera is used to capture a photo of the product. The camera captures a high-resolution photo and acquires product information. The imaging unit uses a camera to capture a photo of the product and transmits the information to the acquisition unit. This makes it possible to acquire information using the photo. Some or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input image data captured by the camera to AI, and the AI ​​can analyze the image data to acquire product information.

[0037] The acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method. For example, the acquisition unit preferentially acquires information on products that the user frequently purchased in the past. The acquisition unit can also analyze the user's purchase pattern and suggest the optimal timing for acquiring information. Furthermore, the acquisition unit can automatically acquire information on specific products from the user's past purchase history. This enables optimal information acquisition based on the past purchase history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's purchase history data to the generation AI, which can analyze the data and select the optimal acquisition method.

[0038] The acquisition unit can perform filtering based on the user's living situation and areas of interest when acquiring product information. For example, if the user is health-conscious, the acquisition unit preferentially acquires information on health-related products. In addition, if the user is busy, the acquisition unit can also preferentially acquire information on products that can be easily cooked. Furthermore, if the user is on a specific diet, the acquisition unit can also acquire information on products suitable for that diet. This makes it possible to acquire information according to the user's living situation and areas of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's living situation data to the generation AI, which can analyze the data and perform filtering.

[0039] When acquiring product information, the acquisition unit can preferentially acquire highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the acquisition unit preferentially acquires information on products that can be purchased in that area. In addition, when the user is traveling, the acquisition unit can also preferentially acquire information on products that can be used at the travel destination. Furthermore, when the user is at home, the acquisition unit can also preferentially acquire information on products that can be purchased at a nearby supermarket. This makes it possible to acquire highly relevant information based on geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location data to the generation AI, which can analyze the data and preferentially acquire highly relevant information.

[0040] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring product information. For example, the acquisition unit preferentially acquires information related to products shared by the user on social media. The acquisition unit can also acquire information on products introduced by influencers followed by the user. Furthermore, the acquisition unit can also acquire information on products in which the user has shown interest on social media. This makes it possible to acquire related information based on social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's social media data to the generation AI, which then analyzes the data to acquire related information.

[0041] The management unit can adjust the level of detail of management based on the importance of the product when managing the expiration date or best-before date. For example, in the case of expensive products, the management unit performs detailed management and strictly monitors the expiration date or best-before date. In addition, the management unit can perform simplified management for products used daily. Furthermore, for products for a specific event, the management unit can also manage according to the date of the event. This makes it possible to adjust the level of detail of management according to the importance of the product. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can input the importance data of the product to the generation AI, which can analyze the data and adjust the level of detail of management.

[0042] The management unit can apply different management algorithms depending on the product category when managing the expiration date or best-before date. For example, in the case of fresh food, the management unit performs short-term management. In addition, in the case of processed food, the management unit can also perform long-term management. Furthermore, in the case of beverages, the management unit can also perform management after opening. This makes it possible to apply management algorithms depending on the product category. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input product category data to a generation AI, which analyzes the data and applies different management algorithms.

[0043] The management unit can determine the priority of management based on the submission time of the product when managing the expiration date or best-before date. For example, the management unit prioritizes the management of products with a close expiration date. The management unit can also postpone the management of products with a distant expiration date. Furthermore, the management unit can manage products for a specific event according to the date of the event. This makes it possible to determine the priority of management based on the submission time of the product. A part or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the submission time data of the product to the generation AI, and the generation AI can analyze the data and determine the priority of management.

[0044] The management unit can adjust the order of management based on the relevance of products when managing best-before dates and expiration dates. The management unit, for example, manages products in the same category together. The management unit can also prioritize management of frequently used products. Furthermore, the management unit can also manage products used in a specific recipe together. This makes it possible to adjust the order of management based on the relevance of products. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input product relevance data to a generation AI, which analyzes the data and adjusts the order of management.

[0045] The notification unit can adjust the level of detail of the notification based on the importance of the product at the time of notification. For example, in the case of an expensive product, the notification unit transmits a detailed notification. In addition, in the case of a product used daily, the notification unit can also transmit a simplified notification. Furthermore, for a product for a specific event, the notification unit can also transmit a notification that matches the date of the event. This makes it possible to adjust the level of detail of the notification according to the importance of the product. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance data of the product to the generation AI, and the generation AI can analyze the data and adjust the level of detail of the notification.

[0046] The notification unit can apply different notification algorithms depending on the product category at the time of notification. For example, in the case of fresh food, the notification unit transmits a notification when the expiration date approaches. In addition, in the case of processed food, the notification unit can also transmit a notification when the expiration date approaches. Furthermore, in the case of beverages, the notification unit can also transmit a notification when the expiration date after opening approaches. This makes it possible to apply a notification algorithm depending on the product category. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input product category data to a generation AI, which analyzes the data and applies different notification algorithms.

[0047] The notification unit can determine the priority of notifications based on the submission time of the product at the time of notification. For example, the notification unit prioritizes notifications of products with an upcoming expiration date. The notification unit can also postpone notifications of products with a distant expiration date. Furthermore, for products for a specific event, the notification unit can also prioritize notifications that match the date of the event. This makes it possible to determine the priority of notifications based on the submission time of the product. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input data on the submission time of the product to the generation AI, which can analyze the data to determine the priority of notifications.

[0048] The notification unit can adjust the order of notifications based on the relevance of products at the time of notification. For example, the notification unit collectively notifies products in the same category. The notification unit can also prioritize notifications of products that are used frequently. Furthermore, the notification unit can collectively transmit notifications of products used in a specific recipe. This makes it possible to adjust the order of notifications based on the relevance of products. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input product relevance data to a generation AI, which analyzes the data and adjusts the order of notifications.

[0049] The recommendation unit can adjust the level of detail of the recipe based on the importance of the product when recommending the recipe. For example, the recommendation unit provides detailed steps for a recipe that uses expensive ingredients. The recommendation unit can also provide simplified steps for a recipe that uses ingredients that are used on a daily basis. Furthermore, for recipes for a specific event, the recommendation unit can provide steps that match the date of the event. This makes it possible to adjust the level of detail of the recipe according to the importance of the product. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the importance data of the product to the generation AI, which can analyze the data and adjust the level of detail of the recipe.

[0050] The recommendation unit can apply different recommendation algorithms depending on the product category when recommending a recipe. For example, in the case of a recipe using fresh food, the recommendation unit provides a recipe that is assumed to be consumed within a short period of time. In addition, in the case of a recipe using processed food, the recommendation unit can also provide a recipe that can be stored for a long period of time. Furthermore, in the case of a recipe using a beverage, the recommendation unit can also provide a recipe that takes into account the expiration date after opening. This makes it possible to apply a recommendation algorithm depending on the product category. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product category data to the generation AI, which analyzes the data and applies different recommendation algorithms.

[0051] When recommending a recipe, the recommendation unit can determine the priority of the recipe based on the submission time of the product. For example, the recommendation unit prioritizes a recipe for a product with a close expiration date. The recommendation unit can also postpone a recipe for a product with a distant expiration date. Furthermore, for recipes for a specific event, the recommendation unit can also prioritize a recipe that matches the date of the event. This makes it possible to determine the priority of the recipe based on the submission time of the product. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input data on the submission time of the product to the generation AI, which can analyze the data to determine the priority of the recipe.

[0052] The recommendation unit can adjust the order of recipes based on the relevance of products when recommending recipes. For example, the recommendation unit can provide recipes that use products in the same category together. The recommendation unit can also prioritize recipes for products that are used frequently. Furthermore, the recommendation unit can also provide recipes for products used in a specific recipe together. This makes it possible to adjust the order of recipes based on the relevance of products. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input product relevance data to a generation AI, which can analyze the data and adjust the order of recipes.

[0053] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, in the case of an expensive product, the suggestion unit makes a detailed suggestion. In addition, in the case of a product used daily, the suggestion unit can also make a simplified suggestion. Furthermore, for a product for a specific event, the suggestion unit can also make a suggestion that matches the date of the event. This makes it possible to adjust the level of detail of the suggestion according to the importance of the product. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the product to the generation AI, which can analyze the data and adjust the level of detail of the suggestion.

[0054] The suggestion unit can apply different suggestion algorithms depending on the category of the product when making a suggestion. For example, in the case of fresh food, the suggestion unit makes a suggestion based on the assumption that the product will be consumed within a short period of time. In addition, in the case of processed food, the suggestion unit can also make a suggestion that can be stored for a long period of time. Furthermore, in the case of beverages, the suggestion unit can also make a suggestion that takes into account the expiration date after opening. This makes it possible to apply a suggestion algorithm depending on the category of the product. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input product category data to a generation AI, which analyzes the data and applies different suggestion algorithms.

[0055] The suggestion unit can determine the priority of the suggestions based on the submission time of the products at the time of suggestion. For example, the suggestion unit prioritizes the suggestion of a product with a close expiration date. The suggestion unit can also postpone the suggestion of a product with a distant expiration date. Furthermore, for products for a specific event, the suggestion unit can also prioritize a suggestion that matches the date of the event. This makes it possible to determine the priority of the suggestions based on the submission time of the products. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the submission time data of the products to the generation AI, which can analyze the data to determine the priority of the suggestions.

[0056] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit suggests products in the same category together. The suggestion unit can also prioritize suggestions of products that are used frequently. Furthermore, the suggestion unit can also suggest products to be used in a specific recipe all at once. This makes it possible to adjust the order of suggestions based on the relevance of products. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input product relevance data to a generation AI, which then analyzes the data and adjusts the order of suggestions.

[0057] When reading the RFID tag, the reading unit can adjust the level of detail of the read based on the importance of the product. For example, in the case of an expensive product, the reading unit performs a detailed read and strictly checks the information on the RFID tag. In addition, in the case of a product used daily, the reading unit can perform a simplified read. Furthermore, for products for a specific event, the reading unit can perform a read according to the date of the event. This makes it possible to adjust the level of detail of the RFID tag read according to the importance of the product. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the importance data of the product to the generating AI, which can analyze the data and adjust the level of detail of the read.

[0058] When reading the RFID tag, the reading unit can determine the priority of reading based on the submission time of the product. For example, the reading unit prioritizes reading the RFID tag of a product with a close expiration date. The reading unit can also postpone reading the RFID tag of a product with a distant expiration date. Furthermore, for products for a specific event, the reading unit can prioritize reading the RFID tag that matches the date of the event. This makes it possible to determine the priority of reading the RFID tag based on the submission time of the product. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the submission time data of the product to the generating AI, and the generating AI can analyze the data to determine the priority of reading.

[0059] When photographing a product, the imaging unit can adjust the level of detail of the photograph based on the importance of the product. For example, in the case of an expensive product, the imaging unit takes detailed photographs to strictly check the condition of the product. In addition, the imaging unit can also take simplified photographs of products used daily. Furthermore, for products for a specific event, the imaging unit can take photographs according to the date of the event. This makes it possible to adjust the level of detail of the photograph according to the importance of the product. A part or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input the importance data of the product to the generation AI, and the generation AI can analyze the data and adjust the level of detail of the photograph.

[0060] When photographing a product, the imaging unit can determine the priority of photographing based on the submission time of the product. For example, the imaging unit prioritizes photographing a product with a close expiration date. The imaging unit can also postpone photographing a product with a distant expiration date. Furthermore, for products for a specific event, the imaging unit can prioritize photographing according to the date of the event. This makes it possible to determine the priority of photographing based on the submission time of the product. A part or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input the submission time data of the product to the generation AI, and the generation AI can analyze the data and determine the priority of photographing.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The acquisition unit can analyze the user's purchasing history and predict the best-before date or expiration date of products purchased in the past. For example, the acquisition unit can predict the best-before date of products frequently purchased by the user and send a notification when the expiration date approaches. The acquisition unit can also predict the best-before date of products purchased by the user in a specific season and send appropriate notifications for each season. Furthermore, the acquisition unit can analyze the user's purchasing patterns and predict the best-before date of products related to specific events or holidays. This makes it possible to predict best-before dates and expiration dates based on the user's purchasing history.

[0063] The management unit can adjust the management of best-before dates and expiration dates taking into account the health condition of the user. For example, if the user has a specific allergy, the management unit can strictly manage the best-before dates of products that contain that allergen. In addition, if the user is on a diet, the management unit can also prioritize management of the best-before dates of low-calorie foods. Furthermore, if the user has a specific health goal, the management unit can also manage the best-before dates of products that are suitable for that goal. This makes it possible to manage best-before dates and expiration dates according to the user's health condition.

[0064] The notification unit can adjust the timing of notifications taking into account the user's schedule. For example, the notification unit can refrain from sending notifications during times when the user is busy and send notifications during times when the user has more time. In addition, if the user is traveling, the notification unit can also send notifications about products that can be used at the user's travel destination. Furthermore, if the user is participating in a specific event, the notification unit can also preferentially send notifications about products related to the event. This makes it possible to adjust the timing of notifications according to the user's schedule.

[0065] The recommendation unit can customize recipes based on the user's dietary preferences. For example, if the user is a vegetarian, recipes that do not use meat are preferentially recommended. In addition, if the user likes a particular dish, the recommendation unit can also suggest recipes related to that dish. Furthermore, if the user likes a particular ingredient, the recommendation unit can also preferentially recommend recipes that use that ingredient. This makes it possible to customize recipes according to the user's dietary preferences.

[0066] The suggestion unit can adjust the content of the suggestion based on the user's budget. For example, if the user places importance on saving money, the suggestion unit can suggest recipes using low-priced ingredients. Also, if the user wants to shop within a specific budget, the suggestion unit can suggest ingredients that match the budget. Furthermore, if the user wants to purchase a specific expensive ingredient, the suggestion unit can suggest a recipe using that ingredient. This makes it possible to adjust the content of the suggestion according to the user's budget.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The acquisition unit acquires information indicating the RFID tag of a product or information indicating a photo of the product. For example, when a user goes shopping, the RFID tag of the product is read or a photo of the product is taken. This information is input to the acquisition unit. The acquisition unit, for example, reads the RFID tag of the product using an RFID reader. The acquisition unit can also take a photo of the product using a camera. For example, an RFID reader can read RFID tags in the UHF band or HF band. The camera takes a high-resolution photo and acquires the product information. Step 2: The management unit manages the best-before dates or expiration dates of the products based on the information acquired by the acquisition unit. The management unit analyzes the acquired information and registers the best-before dates or expiration dates of each product in a database. For example, the management unit reads the best-before dates from the RFID tags of the products and stores them in the database. The management unit manages the best-before dates or expiration dates based on the information stored in the database. Step 3: The notification unit sends a notification when the expiration date or best-before date managed by the management unit is approaching. The notification unit sends a notification to the user to prevent forgetting to use the product. For example, a notification is sent via a smartphone app to inform the user that a product's expiration date is approaching. The notification unit can also send notifications using email or SMS. Step 4: The recommendation unit recommends recipes that use items with close best-before dates or expiration dates managed by the management unit. The recommendation unit generates recipes by combining items with close best-before dates managed by the management unit. For example, the recommendation unit suggests recipes for dishes that use vegetables and meat with close best-before dates. The recommendation unit can also generate recipes using a generation AI. The generation AI generates recipes using, for example, a text generation AI (e.g., LLM). Step 5: The suggestion unit suggests missing ingredients required for the recommended recipe. The suggestion unit identifies missing ingredients based on the recommended recipe and suggests them to the user. For example, it suggests seasonings or additional ingredients required for the recipe. The suggestion unit can also provide a link to purchase the ingredients on an e-commerce site. For example, it can provide a link to purchase the ingredients on an e-commerce site or display flyer information for a nearby supermarket.

[0069] (Example 2) The food waste reduction system according to the embodiment of the present invention is a system that acquires information indicating a product's RFID tag or information indicating a product's photo, and manages information on the expiration date or best-before date. This system sends a notification when the expiration date or best-before date is approaching, preventing the user from forgetting to use the product. In addition, the system recommends recipes using products with close expiration dates, and suggests ingredients that are missing at that time. The system provides information on where the suggested ingredients can be purchased on an e-commerce site or at a nearby supermarket for cheap. For example, when a user goes shopping, the system reads the RFID tag of the product or takes a photo of the product. This information is input to the acquisition unit. Next, the system manages the expiration date or best-before date of the product based on the acquired information. The management unit analyzes the acquired information and registers the expiration date or best-before date of each product in a database. For example, the system reads the expiration date from the product's RFID tag and stores it in the database. When the expiration date or best-before date is approaching, the notification unit sends a notification to the user. For example, a notification is sent through a smartphone app to inform the user of the product's expiration date. This prevents the user from forgetting to use the product. Furthermore, the system recommends recipes that use items with close expiration dates. The recommendation unit generates recipes by combining items with close expiration dates managed by the management unit. For example, it proposes recipes for dishes using vegetables and meat with close expiration dates. It proposes ingredients that are missing for a recommended recipe. The suggestion unit identifies ingredients that are missing based on the recommended recipe and suggests them to the user. For example, it suggests seasonings and additional ingredients that are necessary for the recipe. It provides information that the suggested ingredients can be purchased on an e-commerce site or can be purchased cheaply at a nearby supermarket. For example, it provides a link to purchase on an e-commerce site or displays flyer information for a nearby supermarket. This reduces food waste and enables the user to use ingredients efficiently. This allows the food waste reduction system to enable the user to use ingredients efficiently.

[0070] The food loss reduction system according to the embodiment includes an acquisition unit, a management unit, a notification unit, a recommendation unit, and a suggestion unit. The acquisition unit acquires information indicating an RFID tag of a product or information indicating a photo of the product. For example, when a user goes shopping, the RFID tag of the product is read or a photo of the product is taken. This information is input to the acquisition unit. The acquisition unit, for example, reads the RFID tag of the product using an RFID reader. The acquisition unit can also take a photo of the product using a camera. For example, the RFID reader can read RFID tags in the UHF band or the HF band. The camera takes a high-resolution photo and acquires product information. The management unit manages the expiration date or best-before date of the product based on the information acquired by the acquisition unit. The management unit analyzes the acquired information and registers the expiration date or best-before date of each product in a database. For example, the expiration date is read from the RFID tag of the product and stored in the database. The management unit manages the expiration date or best-before date based on the information stored in the database. The notification unit transmits a notification when the expiration date or best-before date managed by the management unit is approaching. The notification unit transmits a notification to the user to prevent forgetting to use the product. For example, a notification is sent to inform the user of a product whose expiration date is approaching through a smartphone app. The notification unit can also transmit the notification using email or SMS. The recommendation unit recommends a recipe using products with a close expiration date or best-before date managed by the management unit. The recommendation unit generates a recipe by combining products with a close expiration date managed by the management unit. For example, the recommendation unit proposes a recipe for a dish using vegetables and meat with a close expiration date. The recommendation unit can also generate a recipe using a generation AI. The generation AI generates a recipe using, for example, a text generation AI (for example, LLM). The suggestion unit proposes missing ingredients required for the recommended recipe. The suggestion unit identifies missing ingredients based on the recommended recipe and suggests them to the user. For example, it proposes seasonings and additional ingredients required for the recipe. The suggestion unit can also provide a link to purchase the ingredients on an e-commerce site. For example, it could provide a link to purchase from an e-commerce site or display flyers about a nearby supermarket.As a result, the food waste reduction system according to the embodiment can enable users to use ingredients efficiently.

[0071] The acquisition unit acquires information indicating the RFID tag of a product or information indicating a photo of the product. For example, when a user makes a purchase, the RFID tag of the product is read or a photo of the product is taken. This information is input to the acquisition unit. For example, the acquisition unit reads the RFID tag of the product using an RFID reader. The acquisition unit can also take a photo of the product using a camera. For example, the RFID reader can read RFID tags in the UHF band or the HF band. The camera takes a high-resolution photo and acquires product information. The acquisition unit is designed to link these devices so that users can easily acquire product information. For example, by using a camera and an RFID reader built into a smartphone, users can easily acquire product information through an app. In addition, the acquisition unit also has a function of transmitting the acquired information to a cloud server and storing it in a central database. This enables consistent information management even if users use different devices. The acquisition unit can also acquire information on even more diverse products by adding a barcode reader or a two-dimensional code (e.g., QR code) reader. This allows the acquisition unit to efficiently collect detailed information on products purchased by users and provide it to the management department and other departments.

[0072] The management unit manages the expiration date or best-before date of the product based on the information acquired by the acquisition unit. The management unit analyzes the acquired information and registers the expiration date or best-before date of each product in a database. For example, the management unit reads the expiration date from the RFID tag of the product and stores it in the database. The management unit manages the expiration date or best-before date based on the information stored in the database. The management unit can use OCR (optical character recognition) technology or natural language processing technology to analyze the acquired information. This allows the information written on the label or package of the product to be automatically read and registered in the database. Furthermore, the management unit periodically updates the information stored in the database to maintain the latest state. For example, when the expiration date of a product is approaching, the management unit transmits the information to the notification unit and notifies the user. In addition, the management unit has a load distribution function for efficiently managing information even when multiple users use the system at the same time. This allows the management unit to accurately manage the expiration dates and best-before dates of products purchased by users and provide a foundation for reducing food waste.

[0073] The notification unit transmits a notification when the expiration date or best-before date managed by the management unit is approaching. The notification unit transmits a notification to the user to prevent forgetting to use the product. For example, a notification is sent through a smartphone app to inform the user of a product whose expiration date is approaching. The notification unit can also transmit the notification using email or SMS. The notification unit can customize the notification method according to the user's preferences. For example, if the user prefers push notifications, the notification is sent in real time through the app. In addition, notifications can be sent periodically to users who prefer email or SMS. Furthermore, the notification unit can customize the notification content according to the user's language or region. In this way, the notification unit supports the user to efficiently use ingredients without overlooking the expiration date or best-before date. The notification unit also has a function of tracking the user's actions after receiving the notification and evaluating the effectiveness of the notification. For example, the notification unit checks whether the user has consumed the product after receiving the notification and analyzes the effectiveness of the notification. In this way, the notification unit can continuously improve the accuracy and effectiveness of the notification and improve the user's convenience.

[0074] The recommendation unit recommends recipes using products with close expiration dates or expiry dates managed by the management unit. The recommendation unit generates recipes by combining products with close expiration dates managed by the management unit. For example, it proposes recipes for dishes using vegetables and meat with close expiration dates. The recommendation unit can also generate recipes using a generation AI. The generation AI generates recipes using, for example, a text generation AI (e.g., LLM). The generation AI can propose optimal recipes taking into account the user's preferences and past usage history. For example, it generates appropriate recipes based on dishes that the user liked to make in the past and allergy information. Furthermore, the recommendation unit can also propose recipes that use seasonal ingredients taking into account the characteristics of the season and region. This allows the user to enjoy delicious dishes without wasting ingredients with close expiration dates. The recommendation unit also has a function of collecting feedback after the user actually tries the recipe and evaluating the accuracy and satisfaction of the recipe. This allows the recommendation unit to continuously provide recipes that match the user's preferences and contribute to reducing food waste.

[0075] The suggestion unit suggests missing ingredients required for the recommended recipe. The suggestion unit identifies missing ingredients based on the recommended recipe and suggests them to the user. For example, it suggests seasonings and additional ingredients required for the recipe. The suggestion unit can also provide a link to purchase at an e-commerce site. For example, it provides a link to purchase at an e-commerce site or displays flyer information of a nearby supermarket. The suggestion unit supports the user to easily purchase missing ingredients. Furthermore, the suggestion unit can also suggest the optimal purchase destination in consideration of the user's purchase history and preferences. For example, it preferentially displays supermarkets and online stores that the user frequently uses. In addition, if a specific ingredient is on sale or if a coupon is available, the suggestion unit also provides that information. This allows the user to purchase ingredients efficiently and economically. The suggestion unit also has a function of collecting feedback after the user purchases the suggested ingredients and evaluating the accuracy and satisfaction of the suggested content. This allows the suggestion unit to continuously improve the user's purchasing experience and contribute to reducing food waste.

[0076] The food loss reduction system includes a reading unit that reads the RFID tag of a product, and an acquisition unit that acquires information indicating the RFID tag of the product read by the reading unit. The reading unit reads the RFID tag of the product. For example, the RFID tag of the product is read using an RFID reader. The RFID reader can read RFID tags in the UHF band or the HF band. The reading unit reads the RFID tag of the product using the RFID reader and transmits the information to the acquisition unit. This makes it possible to acquire information using the RFID tag. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input data acquired by the RFID reader to AI, which analyzes the data to acquire product information.

[0077] The food waste reduction system includes an imaging unit that captures a photo of a product, and an acquisition unit that acquires information about the photo of the product captured by the imaging unit. The imaging unit captures a photo of the product. For example, a camera is used to capture a photo of the product. The camera captures a high-resolution photo and acquires product information. The imaging unit uses a camera to capture a photo of the product and transmits the information to the acquisition unit. This makes it possible to acquire information using the photo. Some or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input image data captured by the camera to AI, and the AI ​​can analyze the image data to acquire product information.

[0078] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring product information based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit automates the acquisition of product information to reduce the user's effort. The acquisition unit can also provide an option to manually acquire product information when the user is relaxed. Furthermore, the acquisition unit can provide a simplified procedure for quickly acquiring product information when the user is in a hurry. This makes it possible to adjust the timing of acquiring information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the timing of acquiring information based on the result.

[0079] The acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method. For example, the acquisition unit preferentially acquires information on products that the user frequently purchased in the past. The acquisition unit can also analyze the user's purchase pattern and suggest the optimal timing for acquiring information. Furthermore, the acquisition unit can automatically acquire information on specific products from the user's past purchase history. This enables optimal information acquisition based on the past purchase history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's purchase history data to the generation AI, which can analyze the data and select the optimal acquisition method.

[0080] The acquisition unit can perform filtering based on the user's living situation and areas of interest when acquiring product information. For example, if the user is health-conscious, the acquisition unit preferentially acquires information on health-related products. In addition, if the user is busy, the acquisition unit can also preferentially acquire information on products that can be easily cooked. Furthermore, if the user is on a specific diet, the acquisition unit can also acquire information on products suitable for that diet. This makes it possible to acquire information according to the user's living situation and areas of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's living situation data to the generation AI, which can analyze the data and perform filtering.

[0081] The acquisition unit can estimate the user's emotion and determine the priority order of products to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit preferentially acquires information on products that have a relaxing effect. In addition, when the user is relaxed, the acquisition unit can also preferentially acquire information on new products or interesting products. Furthermore, when the user is in a hurry, the acquisition unit can also preferentially acquire information on products that can be used immediately. This makes it possible to determine the priority order of products according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data to the generation AI, which estimates the emotion, and determine the priority order of products based on the result.

[0082] When acquiring product information, the acquisition unit can preferentially acquire highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the acquisition unit preferentially acquires information on products that can be purchased in that area. In addition, when the user is traveling, the acquisition unit can also preferentially acquire information on products that can be used at the travel destination. Furthermore, when the user is at home, the acquisition unit can also preferentially acquire information on products that can be purchased at a nearby supermarket. This makes it possible to acquire highly relevant information based on geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location data to the generation AI, which can analyze the data and preferentially acquire highly relevant information.

[0083] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring product information. For example, the acquisition unit preferentially acquires information related to products shared by the user on social media. The acquisition unit can also acquire information on products introduced by influencers followed by the user. Furthermore, the acquisition unit can also acquire information on products in which the user has shown interest on social media. This makes it possible to acquire related information based on social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's social media data to the generation AI, which then analyzes the data to acquire related information.

[0084] The management unit can estimate the user's emotions and adjust the management method of the expiration date and the best-before date based on the estimated user's emotions. For example, when the user is stressed, the management unit automates the management of the expiration date and the best-before date, reducing the user's effort. The management unit can also provide an option to manage manually when the user is relaxed. Furthermore, the management unit can provide a simplified procedure for quick management when the user is in a hurry. This makes it possible to adjust the management method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the management method based on the result.

[0085] The management unit can adjust the level of detail of management based on the importance of the product when managing the expiration date or best-before date. For example, in the case of expensive products, the management unit performs detailed management and strictly monitors the expiration date or best-before date. In addition, the management unit can perform simplified management for products used daily. Furthermore, for products for a specific event, the management unit can also manage according to the date of the event. This makes it possible to adjust the level of detail of management according to the importance of the product. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can input the importance data of the product to the generation AI, which can analyze the data and adjust the level of detail of management.

[0086] The management unit can apply different management algorithms depending on the product category when managing the expiration date or best-before date. For example, in the case of fresh food, the management unit performs short-term management. In addition, in the case of processed food, the management unit can also perform long-term management. Furthermore, in the case of beverages, the management unit can also perform management after opening. This makes it possible to apply management algorithms depending on the product category. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input product category data to a generation AI, which analyzes the data and applies different management algorithms.

[0087] The management unit can estimate the user's emotions and determine the priority of management based on the estimated user's emotions. For example, when the user is feeling stressed, the management unit prioritizes the management of important products. In addition, when the user is relaxed, the management unit can also manage all products equally. Furthermore, when the user is in a hurry, the management unit can also prioritize the management of products that need to be consumed immediately. This makes it possible to determine the priority of management according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input the user's facial expression data to the generation AI, which estimates the emotions, and determine the priority of management based on the result.

[0088] The management unit can determine the priority of management based on the submission time of the product when managing the expiration date or best-before date. For example, the management unit prioritizes the management of products with a close expiration date. The management unit can also postpone the management of products with a distant expiration date. Furthermore, the management unit can manage products for a specific event according to the date of the event. This makes it possible to determine the priority of management based on the submission time of the product. A part or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the submission time data of the product to the generation AI, and the generation AI can analyze the data and determine the priority of management.

[0089] The management unit can adjust the order of management based on the relevance of products when managing best-before dates and expiration dates. The management unit, for example, manages products in the same category together. The management unit can also prioritize management of frequently used products. Furthermore, the management unit can also manage products used in a specific recipe together. This makes it possible to adjust the order of management based on the relevance of products. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input product relevance data to a generation AI, which analyzes the data and adjusts the order of management.

[0090] The notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion. For example, when the user is feeling stressed, the notification unit transmits a simple and highly visible notification. In addition, when the user is relaxed, the notification unit can also transmit a notification including detailed information. Furthermore, when the user is in a hurry, the notification unit can also transmit a concise notification that focuses on the main points. This makes it possible to adjust the notification expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit inputs the user's facial expression data into the generation AI, which estimates the emotion, and adjusts the notification expression method based on the result.

[0091] The notification unit can adjust the level of detail of the notification based on the importance of the product at the time of notification. For example, in the case of an expensive product, the notification unit transmits a detailed notification. In addition, in the case of a product used daily, the notification unit can also transmit a simplified notification. Furthermore, for a product for a specific event, the notification unit can also transmit a notification that matches the date of the event. This makes it possible to adjust the level of detail of the notification according to the importance of the product. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance data of the product to the generation AI, and the generation AI can analyze the data and adjust the level of detail of the notification.

[0092] The notification unit can apply different notification algorithms depending on the product category at the time of notification. For example, in the case of fresh food, the notification unit transmits a notification when the expiration date approaches. In addition, in the case of processed food, the notification unit can also transmit a notification when the expiration date approaches. Furthermore, in the case of beverages, the notification unit can also transmit a notification when the expiration date after opening approaches. This makes it possible to apply a notification algorithm depending on the product category. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input product category data to a generation AI, which analyzes the data and applies different notification algorithms.

[0093] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user's emotion. For example, when the user is stressed, the notification unit transmits a short notification that is to the point. Also, when the user is relaxed, the notification unit can transmit a longer notification that includes detailed information. Furthermore, when the user is in a hurry, the notification unit can transmit a short notification that can be quickly understood. This enables the length of the notification to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's facial expression data to the generation AI, which estimates the emotion, and adjust the length of the notification based on the result.

[0094] The notification unit can determine the priority of notifications based on the submission time of the product at the time of notification. For example, the notification unit prioritizes notifications of products with an upcoming expiration date. The notification unit can also postpone notifications of products with a distant expiration date. Furthermore, for products for a specific event, the notification unit can also prioritize notifications that match the date of the event. This makes it possible to determine the priority of notifications based on the submission time of the product. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input data on the submission time of the product to the generation AI, which can analyze the data to determine the priority of notifications.

[0095] The notification unit can adjust the order of notifications based on the relevance of products at the time of notification. For example, the notification unit collectively notifies products in the same category. The notification unit can also prioritize notifications of products that are used frequently. Furthermore, the notification unit can collectively transmit notifications of products used in a specific recipe. This makes it possible to adjust the order of notifications based on the relevance of products. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input product relevance data to a generation AI, which analyzes the data and adjusts the order of notifications.

[0096] The recommendation unit can estimate the user's emotions and adjust the way of expressing the recipe based on the estimated user's emotions. For example, when the user is feeling stressed, the recommendation unit provides a simple and highly visible recipe. In addition, when the user is relaxed, the recommendation unit can also provide a recipe including detailed information. Furthermore, when the user is in a hurry, the recommendation unit can also provide a concise recipe with the main points. This makes it possible to adjust the way of expressing the recipe according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's facial expression data to the generation AI, which can estimate the emotion and adjust the way of expressing the recipe based on the result.

[0097] The recommendation unit can adjust the level of detail of the recipe based on the importance of the product when recommending the recipe. For example, the recommendation unit provides detailed steps for a recipe that uses expensive ingredients. The recommendation unit can also provide simplified steps for a recipe that uses ingredients that are used on a daily basis. Furthermore, for recipes for a specific event, the recommendation unit can provide steps that match the date of the event. This makes it possible to adjust the level of detail of the recipe according to the importance of the product. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the importance data of the product to the generation AI, which can analyze the data and adjust the level of detail of the recipe.

[0098] The recommendation unit can apply different recommendation algorithms depending on the product category when recommending a recipe. For example, in the case of a recipe using fresh food, the recommendation unit provides a recipe that is assumed to be consumed within a short period of time. In addition, in the case of a recipe using processed food, the recommendation unit can also provide a recipe that can be stored for a long period of time. Furthermore, in the case of a recipe using a beverage, the recommendation unit can also provide a recipe that takes into account the expiration date after opening. This makes it possible to apply a recommendation algorithm depending on the product category. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product category data to the generation AI, which analyzes the data and applies different recommendation algorithms.

[0099] The recommendation unit can estimate the user's emotions and adjust the length of the recipe based on the estimated user's emotions. For example, when the user is stressed, the recommendation unit can provide a short recipe that is to the point. In addition, when the user is relaxed, the recommendation unit can also provide a longer recipe that includes detailed information. Furthermore, when the user is in a hurry, the recommendation unit can also provide a short recipe that can be quickly understood. This makes it possible to adjust the length of the recipe according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the length of the recipe based on the result.

[0100] When recommending a recipe, the recommendation unit can determine the priority of the recipe based on the submission time of the product. For example, the recommendation unit prioritizes a recipe for a product with a close expiration date. The recommendation unit can also postpone a recipe for a product with a distant expiration date. Furthermore, for recipes for a specific event, the recommendation unit can also prioritize a recipe that matches the date of the event. This makes it possible to determine the priority of the recipe based on the submission time of the product. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input data on the submission time of the product to the generation AI, which can analyze the data to determine the priority of the recipe.

[0101] The recommendation unit can adjust the order of recipes based on the relevance of products when recommending recipes. For example, the recommendation unit can provide recipes that use products in the same category together. The recommendation unit can also prioritize recipes for products that are used frequently. Furthermore, the recommendation unit can also provide recipes for products used in a specific recipe together. This makes it possible to adjust the order of recipes based on the relevance of products. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input product relevance data to a generation AI, which can analyze the data and adjust the order of recipes.

[0102] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. In addition, when the user is relaxed, the suggestion unit can also make a suggestion including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a concise suggestion that focuses on the main points. This makes it possible to adjust the way of expressing the suggestion according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the way of expressing the suggestion based on the result.

[0103] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, in the case of an expensive product, the suggestion unit makes a detailed suggestion. In addition, in the case of a product used daily, the suggestion unit can also make a simplified suggestion. Furthermore, for a product for a specific event, the suggestion unit can also make a suggestion that matches the date of the event. This makes it possible to adjust the level of detail of the suggestion according to the importance of the product. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the product to the generation AI, which can analyze the data and adjust the level of detail of the suggestion.

[0104] The suggestion unit can apply different suggestion algorithms depending on the category of the product when making a suggestion. For example, in the case of fresh food, the suggestion unit makes a suggestion based on the assumption that the product will be consumed within a short period of time. In addition, in the case of processed food, the suggestion unit can also make a suggestion that can be stored for a long period of time. Furthermore, in the case of beverages, the suggestion unit can also make a suggestion that takes into account the expiration date after opening. This makes it possible to apply a suggestion algorithm depending on the category of the product. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input product category data to a generation AI, which analyzes the data and applies different suggestion algorithms.

[0105] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit makes a short and to the point suggestion. Also, when the user is relaxed, the suggestion unit can make a longer suggestion including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can make a short suggestion that can be quickly understood. This makes it possible to adjust the length of the suggestion according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI, which estimates the emotion, and adjust the length of the suggestion based on the result.

[0106] The suggestion unit can determine the priority of the suggestions based on the submission time of the products at the time of suggestion. For example, the suggestion unit prioritizes the suggestion of a product with a close expiration date. The suggestion unit can also postpone the suggestion of a product with a distant expiration date. Furthermore, for products for a specific event, the suggestion unit can also prioritize a suggestion that matches the date of the event. This makes it possible to determine the priority of the suggestions based on the submission time of the products. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the submission time data of the products to the generation AI, which can analyze the data to determine the priority of the suggestions.

[0107] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit suggests products in the same category together. The suggestion unit can also prioritize suggestions of products that are used frequently. Furthermore, the suggestion unit can also suggest products to be used in a specific recipe all at once. This makes it possible to adjust the order of suggestions based on the relevance of products. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input product relevance data to a generation AI, which then analyzes the data and adjusts the order of suggestions.

[0108] The reading unit can estimate the user's emotion and adjust the RFID tag reading method based on the estimated user's emotion. For example, when the user is stressed, the reading unit automates the reading of the RFID tag to reduce the user's effort. The reading unit can also provide an option to read the RFID tag manually when the user is relaxed. Furthermore, the reading unit can provide a simplified procedure for quickly reading the RFID tag when the user is in a hurry. This enables the RFID tag reading method to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reading unit can input the user's facial expression data to the generation AI, which estimates the emotion, and adjust the RFID tag reading method based on the result.

[0109] When reading the RFID tag, the reading unit can adjust the level of detail of the read based on the importance of the product. For example, in the case of an expensive product, the reading unit performs a detailed read and strictly checks the information on the RFID tag. In addition, in the case of a product used daily, the reading unit can perform a simplified read. Furthermore, for products for a specific event, the reading unit can perform a read according to the date of the event. This makes it possible to adjust the level of detail of the RFID tag read according to the importance of the product. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the importance data of the product to the generating AI, which can analyze the data and adjust the level of detail of the read.

[0110] The reading unit can estimate the user's emotion and determine the priority of reading RFID tags based on the estimated user's emotion. For example, when the user is feeling stressed, the reading unit prioritizes reading RFID tags of important products. In addition, when the user is relaxed, the reading unit can also read RFID tags of all products evenly. Furthermore, when the user is in a hurry, the reading unit can also prioritize reading RFID tags of products that need to be consumed immediately. This makes it possible to determine the priority of reading RFID tags according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reading unit can input the user's facial expression data to the generation AI, which estimates the emotion, and determine the priority of reading RFID tags based on the result.

[0111] When reading the RFID tag, the reading unit can determine the priority of reading based on the submission time of the product. For example, the reading unit prioritizes reading the RFID tag of a product with a close expiration date. The reading unit can also postpone reading the RFID tag of a product with a distant expiration date. Furthermore, for products for a specific event, the reading unit can prioritize reading the RFID tag that matches the date of the event. This makes it possible to determine the priority of reading the RFID tag based on the submission time of the product. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the submission time data of the product to the generating AI, and the generating AI can analyze the data to determine the priority of reading.

[0112] The imaging unit can estimate the user's emotion and adjust the product photography method based on the estimated user's emotion. For example, when the user is stressed, the imaging unit automates the product photography to reduce the user's effort. The imaging unit can also provide an option to manually take a product photo when the user is relaxed. Furthermore, the imaging unit can provide a simplified procedure for quickly taking a product photo when the user is in a hurry. This enables the product photography method to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the imaging unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the imaging unit can input the user's facial expression data to the generation AI, which can estimate the emotion and adjust the product photography method based on the result.

[0113] When photographing a product, the imaging unit can adjust the level of detail of the photograph based on the importance of the product. For example, in the case of an expensive product, the imaging unit takes detailed photographs to strictly check the condition of the product. In addition, the imaging unit can also take simplified photographs of products used daily. Furthermore, for products for a specific event, the imaging unit can take photographs according to the date of the event. This makes it possible to adjust the level of detail of the photograph according to the importance of the product. A part or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input the importance data of the product to the generation AI, and the generation AI can analyze the data and adjust the level of detail of the photograph.

[0114] The imaging unit can estimate the user's emotion and determine the priority of photographing products based on the estimated user's emotion. For example, when the user is stressed, the imaging unit prioritizes photographing important products. In addition, when the user is relaxed, the imaging unit can also take photos of all products evenly. Furthermore, when the user is in a hurry, the imaging unit can also prioritize photographing products that need to be consumed immediately. This makes it possible to determine the priority of photographing products according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the imaging unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the imaging unit can input the user's facial expression data to the generation AI, which estimates the emotion, and determine the priority of photographing products based on the result.

[0115] When photographing a product, the imaging unit can determine the priority of photographing based on the submission time of the product. For example, the imaging unit prioritizes photographing a product with a close expiration date. The imaging unit can also postpone photographing a product with a distant expiration date. Furthermore, for products for a specific event, the imaging unit can prioritize photographing according to the date of the event. This makes it possible to determine the priority of photographing based on the submission time of the product. A part or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input the submission time data of the product to the generation AI, and the generation AI can analyze the data and determine the priority of photographing.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The acquisition unit can analyze the user's purchasing history and predict the best-before date or expiration date of products purchased in the past. For example, the acquisition unit can predict the best-before date of products frequently purchased by the user and send a notification when the expiration date approaches. The acquisition unit can also predict the best-before date of products purchased by the user in a specific season and send appropriate notifications for each season. Furthermore, the acquisition unit can analyze the user's purchasing patterns and predict the best-before date of products related to specific events or holidays. This makes it possible to predict best-before dates and expiration dates based on the user's purchasing history.

[0118] The management unit can adjust the management of best-before dates and expiration dates taking into account the health condition of the user. For example, if the user has a specific allergy, the management unit can strictly manage the best-before dates of products that contain that allergen. In addition, if the user is on a diet, the management unit can also prioritize management of the best-before dates of low-calorie foods. Furthermore, if the user has a specific health goal, the management unit can also manage the best-before dates of products that are suitable for that goal. This makes it possible to manage best-before dates and expiration dates according to the user's health condition.

[0119] The notification unit can adjust the timing of notifications taking into account the user's schedule. For example, the notification unit can refrain from sending notifications during times when the user is busy and send notifications during times when the user has more time. In addition, if the user is traveling, the notification unit can also send notifications about products that can be used at the user's travel destination. Furthermore, if the user is participating in a specific event, the notification unit can also preferentially send notifications about products related to the event. This makes it possible to adjust the timing of notifications according to the user's schedule.

[0120] The recommendation unit can customize recipes based on the user's dietary preferences. For example, if the user is a vegetarian, recipes that do not use meat are preferentially recommended. In addition, if the user likes a particular dish, the recommendation unit can also suggest recipes related to that dish. Furthermore, if the user likes a particular ingredient, the recommendation unit can also preferentially recommend recipes that use that ingredient. This makes it possible to customize recipes according to the user's dietary preferences.

[0121] The suggestion unit can adjust the content of the suggestion based on the user's budget. For example, if the user places importance on saving money, the suggestion unit can suggest recipes using low-priced ingredients. Also, if the user wants to shop within a specific budget, the suggestion unit can suggest ingredients that match the budget. Furthermore, if the user wants to purchase a specific expensive ingredient, the suggestion unit can suggest a recipe using that ingredient. This makes it possible to adjust the content of the suggestion according to the user's budget.

[0122] The acquisition unit can estimate the user's emotions and adjust the product information acquisition method based on the estimated user's emotions. For example, when the user is stressed, the acquisition of product information is automated to reduce the user's effort. The acquisition unit can also provide an option to manually acquire product information when the user is relaxed. Furthermore, the acquisition unit can provide a simplified procedure for quickly acquiring product information when the user is in a hurry. This makes it possible to adjust the information acquisition method according to the user's emotions.

[0123] The management unit can estimate the user's emotions and adjust the management method of best-before dates and expiration dates based on the estimated user's emotions. For example, when the user is stressed, the management of best-before dates and expiration dates is automated to reduce the user's effort. The management unit can also provide an option to manage them manually when the user is relaxed. Furthermore, the management unit can provide a simplified procedure for quick management when the user is in a hurry. This makes it possible to adjust the management method according to the user's emotions.

[0124] The notification unit can estimate the user's emotion and adjust the way in which the notification is expressed based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and highly visible notification is sent. The notification unit can also send a notification including detailed information if the user is relaxed. Furthermore, the notification unit can also send a concise notification that focuses on the main points if the user is in a hurry. This makes it possible to adjust the way in which the notification is expressed according to the user's emotion.

[0125] The recommendation unit can estimate the user's emotions and adjust the way in which a recipe is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible recipe is provided. The recommendation unit can also provide a recipe including detailed information if the user is relaxed. Furthermore, the recommendation unit can also provide a concise recipe that focuses on the main points if the user is in a hurry. This makes it possible to adjust the way in which a recipe is presented according to the user's emotions.

[0126] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit can make a simple and highly visible suggestion. When the user is relaxed, the suggestion unit can also make a suggestion including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a concise suggestion that focuses on the main points. This makes it possible to adjust the way in which suggestions are expressed according to the user's emotions.

[0127] The process flow of the second embodiment will be briefly described below.

[0128] Step 1: The acquisition unit acquires information indicating the RFID tag of a product or information indicating a photo of the product. For example, when a user goes shopping, the RFID tag of the product is read or a photo of the product is taken. This information is input to the acquisition unit. The acquisition unit, for example, reads the RFID tag of the product using an RFID reader. The acquisition unit can also take a photo of the product using a camera. For example, an RFID reader can read RFID tags in the UHF band or HF band. The camera takes a high-resolution photo and acquires the product information. Step 2: The management unit manages the best-before dates or expiration dates of the products based on the information acquired by the acquisition unit. The management unit analyzes the acquired information and registers the best-before dates or expiration dates of each product in a database. For example, the management unit reads the best-before dates from the RFID tags of the products and stores them in the database. The management unit manages the best-before dates or expiration dates based on the information stored in the database. Step 3: The notification unit sends a notification when the expiration date or best-before date managed by the management unit is approaching. The notification unit sends a notification to the user to prevent forgetting to use the product. For example, a notification is sent via a smartphone app to inform the user that a product's expiration date is approaching. The notification unit can also send notifications using email or SMS. Step 4: The recommendation unit recommends recipes that use items with close best-before dates or expiration dates managed by the management unit. The recommendation unit generates recipes by combining items with close best-before dates managed by the management unit. For example, the recommendation unit suggests recipes for dishes that use vegetables and meat with close best-before dates. The recommendation unit can also generate recipes using a generation AI. The generation AI generates recipes using, for example, a text generation AI (e.g., LLM). Step 5: The suggestion unit suggests missing ingredients required for the recommended recipe. The suggestion unit identifies missing ingredients based on the recommended recipe and suggests them to the user. For example, it suggests seasonings or additional ingredients required for the recipe. The suggestion unit can also provide a link to purchase the ingredients on an e-commerce site. For example, it can provide a link to purchase the ingredients on an e-commerce site or display flyer information for a nearby supermarket.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0131] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] For example, the acquisition unit acquires product information using the camera 42 or RFID reader of the smart device 14. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the information transmitted from the acquisition unit, and registers the best-before date and the expiration date in the database 24. For example, the notification unit is realized by the control unit 46A of the smart device 14, and notifies the user of products whose expiration date is approaching. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a recipe combining products whose expiration date is approaching. For example, the suggestion unit is realized by the control unit 46A of the smart device 14, and suggests missing ingredients based on the recommended recipe. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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, the RAM 30, and the 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] Fig. 4 shows an example of 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the acquisition unit acquires product information using the camera 42 or RFID reader of the smart glasses 214. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the information transmitted from the acquisition unit, and registers the best-before date and the expiration date in the database 24. For example, the notification unit is realized by the control unit 46A of the smart glasses 214, and notifies the user of products whose expiration date is approaching. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a recipe that combines products whose expiration date is approaching. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214, and suggests missing ingredients based on the recommended recipe. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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, the RAM 30, and the 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] 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 performed in a secure state.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 identification processing unit 290 using these models.

[0160] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 voice indicating a user input for 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.

[0162] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] For example, the acquisition unit acquires product information using the camera 42 or RFID reader of the headset terminal 314. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the information transmitted from the acquisition unit, and registers the best-before date and the expiration date in the database 24. For example, the notification unit is realized by the control unit 46A of the headset terminal 314, and notifies the user of products whose expiration date is approaching. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a recipe that combines products whose expiration date is approaching. For example, the suggestion unit is realized by the control unit 46A of the headset terminal 314, and suggests ingredients that are missing based on the recommended recipe. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] 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, the RAM 30, and the 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.

[0168] 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.

[0169] 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 the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0171] 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 performed in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0176] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0177] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0178] 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 a voice indicating a user input for 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.

[0179] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0180] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0181] For example, the acquisition unit acquires product information using the camera 42 or RFID reader of the robot 414. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the information transmitted from the acquisition unit, and registers the best-before date and the expiration date in the database 24. For example, the notification unit is realized by the control unit 46A of the robot 414, and notifies the user of products whose expiration date is approaching. For example, the recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a recipe that combines products whose expiration date is approaching. For example, the suggestion unit is realized by the control unit 46A of the robot 414, and suggests missing ingredients based on the recommended recipe. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0183] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0185] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0186] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0187] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0189] 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.

[0190] 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 Universal Serial Bus (USB) 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.

[0191] In addition, 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 upon request from the data processing device 12.

[0192] 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.

[0193] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0194] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0195] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0196] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0198] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0200] (Appendix 1) an acquisition unit that acquires information indicating an RFID tag of a product or information indicating a photo of the product; A management unit that manages the expiration date or best-before date of a product based on the information acquired by the acquisition unit; A notification unit that transmits a notification when a best-before date or a use-by date managed by the management unit is approaching; a recommendation unit that recommends recipes using items that are close to their expiration dates or best-before dates and that are managed by the management unit; A suggestion unit that suggests ingredients that are necessary for the recipe recommended by the recommendation unit and that are lacking. A system characterized by: (Appendix 2) Equipped with a reader that reads the RFID tags of products, The acquisition unit acquires information indicating the RFID tag of the product read by the reading unit. 2. The system of claim 1, (Appendix 3) An imaging unit that captures a photograph of a product, The acquisition unit acquires information about a photo of the product captured by the imaging unit. 2. The system of claim 1, (Appendix 4) The acquisition unit is The system estimates the user's emotions and adjusts the timing of product information acquisition based on the estimated user emotions. 2. The system of claim 1, (Appendix 5) The acquisition unit is Analyze the user's past purchase history and select the appropriate acquisition method 2. The system of claim 1, (Appendix 6) The acquisition unit is When retrieving product information, filter it based on the user's lifestyle and interests. 2. The system of claim 1, (Appendix 7) The acquisition unit is Estimate user sentiment and prioritize products to be acquired based on the estimated user sentiment 2. The system of claim 1, (Appendix 8) The acquisition unit is When retrieving product information, the system prioritizes retrieving relevant information by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 9) The acquisition unit is When acquiring product information, the user's social media activity is analyzed to acquire related information. 2. The system of claim 1, (Appendix 10) The management unit Inferring user emotions and adjusting expiration date and best-before date management methods based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The management unit When managing expiration dates and best before dates, adjust the level of detail based on the importance of the product. 2. The system of claim 1, (Appendix 12) The management unit When managing expiration dates and expiry dates, apply different management algorithms depending on the product category. 2. The system of claim 1, (Appendix 13) The management unit To estimate a user's emotion and determine a management priority based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The management unit When managing expiration dates and best before dates, determine management priorities based on when the product was submitted 2. The system of claim 1, (Appendix 15) The management unit When managing expiration dates and best before dates, adjust the order of management based on product relevance 2. The system of claim 1, (Appendix 16) The notification unit is Infer user emotions and adjust notification presentation based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The notification unit is When sending notifications, adjust the level of detail based on the importance of the product 2. The system of claim 1, (Appendix 18) The notification unit is During notifications, apply different notification algorithms depending on the product category 2. The system of claim 1, (Appendix 19) The notification unit is Infer user emotion and adjust notification length based on the estimated user emotion 2. The system of claim 1, (Appendix 20) The notification unit is At the time of notification, we will prioritize notifications based on when the product was submitted 2. The system of claim 1, (Appendix 21) The notification unit is During notifications, adjust the order of notifications based on product relevance 2. The system of claim 1, (Appendix 22) The recommendation unit is Infer user emotions and adjust recipe presentation based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The recommendation unit is When recommending recipes, adjust the recipe details based on the importance of the product 2. The system of claim 1, (Appendix 24) The recommendation unit is When recommending recipes, apply different recommendation algorithms depending on the product category. 2. The system of claim 1, (Appendix 25) The recommendation unit is Infer user sentiment and adjust recipe length based on the inferred sentiment 2. The system of claim 1, (Appendix 26) The recommendation unit is When recommending recipes, prioritize recipes based on when they were submitted 2. The system of claim 1, (Appendix 27) The recommendation unit is When recommending recipes, adjust the order of recipes based on product relevance 2. The system of claim 1, (Appendix 28) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The suggestion unit, When making a proposal, adjust the detail of the proposal based on the importance of the product 2. The system of claim 1, (Appendix 30) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the product category 2. The system of claim 1, (Appendix 31) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 32) The suggestion unit, At the time of proposal, prioritize proposals based on when the product was submitted 2. The system of claim 1, (Appendix 33) The suggestion unit, When making suggestions, adjust the order of suggestions based on product relevance 2. The system of claim 1, (Appendix 34) The reading unit is Inferring user emotions and adjusting the RFID tag reading method based on the estimated user emotions 3. The system of claim 2, (Appendix 35) The reading unit is When reading RFID tags, adjust the reading detail based on the importance of the product 3. The system of claim 2, (Appendix 36) The reading unit is A method for predicting user emotions and prioritizing reading of RFID tags based on the user emotions. 3. The system of claim 2, (Appendix 37) The reading unit is When reading RFID tags, prioritize reading based on when the item was submitted 3. The system of claim 2, (Appendix 38) The imaging unit includes: To estimate a user's emotion and adjust a method of photographing a product based on the estimated user's emotion 4. The system of claim 3. (Appendix 39) The imaging unit includes: When taking product photos, adjust the level of detail in your shots based on the importance of the product. 4. The system of claim 3. (Appendix 40) The imaging unit includes: Estimating user emotions and prioritizing product photography based on the estimated user emotions 4. The system of claim 3. (Appendix 41) The imaging unit includes: When taking product photos, prioritize photoshoots based on when products are submitted 4. The system of claim 3. [Explanation of symbols]

[0201] 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. An acquisition unit that acquires information indicating an RFID tag of a product or information indicating a photo of the product; A management unit that manages the expiration date or best-before date of the product based on the information acquired by the acquisition unit; A notification unit that transmits a notification when a best-before date or a use-by date managed by the management unit is approaching; a recommendation unit that recommends recipes using items that are close to their expiration dates or best-before dates and that are managed by the management unit; A suggestion unit that suggests ingredients that are necessary for the recipe recommended by the recommendation unit and that are lacking. A system characterized by:

2. A reading unit for reading the RFID tag of the product, The acquisition unit is Acquire information indicating the RFID tag of the product read by the reading unit.

2. The system of claim 1 .

3. An imaging unit that captures a photo of the product, The acquisition unit is Acquire information about a photo of the product captured by the imaging unit 2. The system of claim 1 .

4. The acquisition unit is The method comprises: estimating a user's emotion; and adjusting a timing for acquiring information on the product based on the estimated user's emotion.

2. The system of claim 1 .

5. The acquisition unit is Analyze the user's past purchase history and select the appropriate acquisition method 2. The system of claim 1 .

6. The acquisition unit is When acquiring information on the said products, filtering is performed based on the user's living situation and areas of interest.

2. The system of claim 1 .

7. The acquisition unit is A user's emotion is estimated, and a priority order of the products to be acquired is determined based on the estimated user's emotion.

2. The system of claim 1 .

8. The acquisition unit is When acquiring information on the product, the user's geographic location information is taken into consideration to acquire related information preferentially.

2. The system of claim 1 .

9. The acquisition unit is When acquiring information about the product, the user's social media activity is analyzed to acquire related information.

2. The system of claim 1 .

10. The management unit Inferring user emotions and adjusting expiration date and best-before date management methods based on the estimated user emotions 2. The system of claim 1 .

Images