system

Abstract

The system according to this embodiment aims to keep track of the inventory of household items simply by taking a photograph. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a display unit. The reception unit takes a photograph. The analysis unit analyzes the image taken by the reception unit, identifies the items, and counts them. The display unit displays the inventory status based on the information analyzed by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is necessary to manually check the inventory of daily necessities in the house, which has the problem of low efficiency.

[0005] The system according to the embodiment aims to grasp the inventory of daily necessities in the house just by taking a photo.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a display unit. The reception unit takes a photo. The analysis unit analyzes the image captured by the reception unit, discriminates and counts the items. The display unit displays the inventory status based on the information analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment allows you to keep track of the inventory of everyday items in your home simply by taking a picture. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The household goods inventory management tool according to an embodiment of the present invention is a system that allows users to keep track of their household goods inventory simply by taking a picture. This system works by taking a picture of the area to be managed, such as a refrigerator or shelves. The AI ​​object detection function identifies the items in the image and counts their quantities. This allows the inventory status to be displayed in a list on the app. Furthermore, by setting a desired inventory level in advance, the system can calculate the number of items needed based on the current inventory and create a shopping list. For example, it is possible to register items from packages that have been photographed and identified in the past. Additionally, users can search for and purchase products with the same (or similar) packaging from various shopping sites based on the image. This allows users to easily purchase necessary items. This mechanism allows users to easily keep track of their inventory in refrigerators and shelves and efficiently purchase necessary items. For example, simply taking a picture of the inside of a refrigerator displays the current inventory status in a list on the app, and missing items are automatically added to the shopping list. Furthermore, since users can search for and purchase products with the same (or similar) packaging from various shopping sites based on the image, the effort of shopping is reduced. As a result, the household goods inventory management tool allows users to easily keep track of and efficiently manage their household goods inventory.

[0029] The daily necessities inventory management tool according to this embodiment comprises a reception unit, an analysis unit, and a display unit. The reception unit provides an interface for the user to take photos. For example, the reception unit can take photos of the inside of a refrigerator or shelves using a smartphone camera. The reception unit also has a function to send the photos taken by the user to the system. For example, the reception unit uploads the taken photos to a cloud server and sends them to the analysis unit. The analysis unit uses AI object detection functionality to identify items in the captured image and count their quantities. For example, the analysis unit uses an image analysis algorithm to identify items based on their shape and color. The analysis unit can also identify the type of item by comparing the characteristics of the items with a database. For example, the analysis unit reads the labels of items in the image to identify the type of item. The display unit displays the inventory status in a list on the app based on the information analyzed by the analysis unit. For example, the display unit displays the quantity and type of inventory in graph or list format. The display unit can also compare the inventory quantity set by the user with the current inventory quantity and highlight the items that are lacking. For example, the display unit shows items with low stock in red to alert the user. This allows the household goods inventory management tool according to this embodiment to easily grasp and efficiently manage the inventory of household goods in the home.

[0030] The reception unit provides an interface for users to take photos. For example, the reception unit allows users to take pictures of refrigerators and shelves using their smartphone cameras. Specifically, when a user launches the app and selects the camera function, guidelines are displayed for photographing items inside the refrigerator or shelves. These guidelines guide the user to properly frame the items, improving the accuracy of the photograph. The reception unit also has a function to send the photos taken by the user to the system. For example, the reception unit uploads the photographed photos to a cloud server and sends them to the analysis unit. During uploading, the resolution and file size of the photos are automatically adjusted, allowing for quick and efficient data transmission. Furthermore, the reception unit also provides a function that allows users to take multiple photos at once and send them all together. This allows users to photograph the inventory status of multiple locations at once and manage them efficiently. The reception unit also has features such as voice guidance and vibration feedback to simplify user operation, and is designed for intuitive operation. As a result, the reception unit makes it possible for users to easily photograph the inventory status of daily necessities and send them to the system.

[0031] The analysis unit uses AI object detection capabilities to identify and count items in captured images. Specifically, the analysis unit uses image analysis algorithms to identify items based on their shape and color. For example, it uses a deep learning-based object detection model to recognize each item in the image with high accuracy. This model is pre-trained on a large dataset of item images and can accurately identify items even under various angles and lighting conditions. The analysis unit can also identify the type of item by comparing its features with a database. For example, it can read the labels on items in the image to identify the type of item. By using OCR (Optical Character Recognition) technology to extract text information from the labels and comparing it with a database, it obtains detailed information about the item. Furthermore, the analysis unit uses a duplicate detection algorithm for identical items to count the number of items. This allows for accurate inventory counts even if the same item is photographed multiple times. The analysis unit performs these processes in real time and can quickly provide analysis results for photos taken by the user. This allows the analysis unit to accurately and quickly extract inventory information from images taken by the user, improving the overall efficiency of the system.

[0032] The display unit displays inventory status in a list format on the app based on information analyzed by the analysis unit. Specifically, the display unit shows the quantity and type of inventory in graph and list formats. For example, it uses bar graphs and pie charts to visually display the inventory quantity of each item in an easy-to-understand way. In addition, the list format displays detailed information such as the item name, inventory quantity, and last updated date and time, allowing users to easily check the inventory status. Furthermore, the display unit can compare the inventory quantity set by the user with the current inventory quantity and highlight items that are low. For example, the display unit can display items with low inventory in red to alert the user. This allows users to quickly identify items that are running low and replenish them promptly. The display unit also provides a customizable filter function, allowing users to narrow down inventory information based on specific categories or importance. For example, it can display inventory status by category such as food, daily necessities, and consumables, allowing users to quickly access the information they need. The display unit also has a function to display inventory fluctuation history, allowing users to understand consumption trends by displaying past inventory status in graph form. This allows the display unit to provide users with a powerful tool for gaining a detailed understanding of inventory status and managing it efficiently.

[0033] The list creation unit calculates the number of items that are missing based on a pre-set inventory level and creates a shopping list. The list creation unit compares the inventory level set by the user with the current inventory level and automatically lists the missing items. For example, if the inventory level set by the user is 10 and the current inventory level is 5, the list creation unit calculates that 5 items are missing. The list creation unit also has a function to add missing items to the shopping list. For example, the list creation unit adds missing items to the list and notifies the user. Furthermore, the list creation unit can also send the shopping list to the user's smartphone. For example, the list creation unit displays the shopping list on the app so that the user can check it. In this way, the list creation unit can provide support for the user to efficiently purchase the items they need. Some or all of the above processing in the list creation unit may be performed using AI, for example, or not using AI. For example, the list creation unit can calculate the number of items that are missing using an AI model that takes the inventory level set by the user and the current inventory level as input and outputs the number of items that are missing.

[0034] The search unit searches for products with the same packaging from an image on various shopping sites and makes a purchase. The search unit identifies products with the same (or similar) packaging from a captured image and searches for them on various shopping sites. For example, the search unit uses image recognition technology to extract packaging features and compare them with a database. The search unit can also search for the identified products on various shopping sites and suggest the best place to buy them. For example, the search unit searches for products on shopping sites such as Amazon® and Yahoo® and compares prices and availability. Furthermore, the search unit can provide links for users to purchase the products they have selected. For example, the search unit adds the selected products to the cart and supports the purchase process. In this way, the search unit can provide support for users to efficiently purchase the products they need. Some or all of the above processing in the search unit may be performed using AI, for example, or not. For example, the search unit can identify products using an AI model that takes a captured image as input and outputs products with the same packaging.

[0035] The reception unit can take photos of the parts of a refrigerator or shelf that the user wants to manage inventory for. The reception unit provides an interface for taking photos of the inside of a refrigerator, for example, by using a smartphone camera to take photos of the inside of the refrigerator. The reception unit can also provide an interface for taking photos of shelves, for example, by using a smartphone camera to take photos of the inside of shelves. Furthermore, the reception unit has a function to send the photos taken by the user to the system. For example, the reception unit uploads the taken photos to a cloud server and sends them to the analysis unit. This allows the reception unit to easily take photos of the parts of a refrigerator or shelf that the user wants to manage inventory for and send them to the system. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can send photos using an AI model that takes photos taken by the user as input and sends them to the system.

[0036] The analysis unit can identify objects in an image and count their quantities. For example, the analysis unit uses AI object detection capabilities to identify objects in a captured image. For instance, it uses an image analysis algorithm to identify objects based on their shape and color. The analysis unit can also identify the type of object by comparing its characteristics with a database. For example, it reads the labels on objects in an image to identify their type. Furthermore, the analysis unit has a function to count the number of identified objects. For example, it counts the number of identical objects in an image and calculates the inventory count. This allows the analysis unit to accurately identify and count objects in an image. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can use an AI model that takes a captured image as input and outputs the type and quantity of objects to perform object identification and counting.

[0037] The display unit can display inventory status in a list on the app. For example, the display unit displays inventory status in a list on the app based on information analyzed by the analysis unit. For example, the display unit can display the quantity and type of inventory in graph or list format. The display unit can also compare the inventory quantity set by the user with the current inventory quantity and highlight items that are running low. For example, the display unit can display items with low inventory in red to alert the user. Furthermore, the display unit has a function to update inventory status in real time. For example, the display unit periodically receives information from the analysis unit to keep the inventory status up to date. This allows the display unit to easily grasp the inventory status of daily necessities in the home and manage it efficiently. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes information from the analysis unit as input and displays inventory status.

[0038] The reception desk can analyze the user's past shooting history and select the optimal shooting method. For example, the reception desk can analyze the time of day of photos taken by the user in the past and suggest the optimal shooting time. For example, the reception desk can identify the time of day when the user's photos were most successful based on the data of photos taken by the user in the past. The reception desk can also analyze the angle and position of photos taken by the user in the past and suggest the optimal shooting method. For example, the reception desk can identify the angle and position when the user's photos were most successful based on the data of photos taken by the user in the past. Furthermore, the reception desk can analyze the quality of photos taken by the user in the past and suggest the optimal camera settings. For example, the reception desk can identify the camera settings for taking the highest quality photos based on the data of photos taken by the user in the past. In this way, the reception desk can analyze the user's past shooting history and select the optimal shooting method. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can select a shooting method using a generative AI model that takes the user's past shooting history data as input and outputs the optimal shooting method.

[0039] The reception unit can filter photos based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the reception unit can filter photos to prioritize healthy foods in the refrigerator. For example, based on the user's health consciousness, the reception unit can prioritize photographing vegetables and fruits in the refrigerator. The reception unit can also filter photos to prioritize only essential items if the user is busy. For example, based on the user's busyness, the reception unit can prioritize photographing only the main ingredients in the refrigerator. Furthermore, if the user is planning a specific event (e.g., a party), the reception unit can filter photos to prioritize items related to that event. For example, if the user is planning a party, the reception unit can prioritize photographing drinks and snacks in the refrigerator. This allows the reception unit to filter photos based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, generative AI, or without generative AI. For example, the reception unit can perform filtering using a generative AI model that takes data on the user's lifestyle and areas of interest as input and outputs filtering results.

[0040] The reception unit can prioritize photographing highly relevant parts of an image, taking into account the user's geographical location. For example, if the user is at home, the reception unit will prioritize photographing the refrigerator or kitchen shelves. For example, based on the user's geographical location, the reception unit will prioritize photographing the refrigerator or kitchen shelves at home. The reception unit can also prioritize photographing the contents of the bag the user is carrying if they are out. For example, based on the user's geographical location, the reception unit will prioritize photographing the contents of the bag the user is carrying if they are out. Furthermore, if the user is traveling, the reception unit can prioritize photographing items needed at their travel destination. For example, based on the user's geographical location, the reception unit will prioritize photographing items needed at their travel destination. This allows the reception unit to prioritize photographing highly relevant parts of an image, taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, generative AI, or without generative AI. For example, the reception unit can determine the priority of photography using a generative AI model that takes the user's geographical location as input and outputs highly relevant parts.

[0041] The reception desk can analyze the user's social media activity when taking photos and capture relevant parts. For example, if the user frequently posts photos of food on social media, the reception desk can prioritize photographing the ingredients in the refrigerator. Similarly, if the user posts fashion-related content on social media, the reception desk can prioritize photographing the clothes in the closet. Furthermore, if the user posts photos of pets on social media, the reception desk can prioritize photographing pet supplies. This allows the reception desk to analyze the user's social media activity and capture relevant parts. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can determine the priority of photography using a generative AI model that takes the user's social media activity data as input and outputs relevant parts.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the items during the analysis. For example, the analysis unit can perform a detailed analysis on important items and display the inventory quantity and frequency of use. For example, the analysis unit can provide detailed inventory information for important items such as food and pharmaceuticals. The analysis unit can also perform a simplified analysis on less important items and display only the inventory quantity. For example, the analysis unit can provide simplified inventory information for less important items such as daily necessities and consumables. Furthermore, the analysis unit can adjust the display order of the analysis results according to the importance of the items. For example, the analysis unit can prioritize the display of important items so that users can quickly check them. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the items. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can adjust the level of detail of the analysis using a generative AI model that takes item importance data as input and outputs the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the item during analysis. For example, for items in the food category, the analysis unit performs analysis that takes expiration dates into account. For example, the analysis unit evaluates the freshness of inventory based on food expiration date data. The analysis unit can also perform analysis that takes seasons and trends into account for items in the clothing category. For example, the analysis unit evaluates the appropriateness of inventory based on seasonal and trend data for clothing. Furthermore, the analysis unit can also perform analysis that takes consumption rates into account for items in the daily necessities category. For example, the analysis unit evaluates the timing of inventory replenishment based on daily necessities consumption rate data. In this way, the analysis unit can apply different analysis algorithms depending on the category of the item. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can apply an analysis algorithm using a generative AI model that takes item category data as input and outputs an analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the submission date of the items during the analysis. For example, the analysis unit may prioritize the analysis of recently added items. For example, the analysis unit may prioritize the analysis of recently added items based on the submission date data of the items. The analysis unit may also prioritize the analysis of items that have not been used for a long period of time. For example, the analysis unit may prioritize the analysis of items that have not been used for a long period of time based on the usage history data of the items. Furthermore, the analysis unit may also prioritize the analysis of items that the user frequently uses during specific periods. For example, the analysis unit may prioritize the analysis of items that the user frequently uses during specific periods based on the user's usage pattern data. This allows the analysis unit to determine the priority of analysis based on the submission date of the items. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit may determine the priority of analysis using a generative AI model that takes the submission date data of the items as input and outputs the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relationships between items during the analysis. For example, the analysis unit can analyze items of the same category together. For example, the analysis unit can analyze items of the same category together based on item category data. The analysis unit can also prioritize the analysis of items with high usage frequency. For example, the analysis unit can prioritize the analysis of items with high usage frequency based on item usage frequency data. Furthermore, the analysis unit can prioritize the analysis of items used by the user in a specific event. For example, the analysis unit can prioritize the analysis of items used in a specific event based on the user's event data. This allows the analysis unit to adjust the order of analysis based on the relationships between items. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can adjust the order of analysis using a generative AI model that takes item relationship data as input and outputs the order of analysis.

[0046] The display unit can adjust the level of detail displayed based on the importance of the inventory. For example, the display unit can display detailed information for important inventory. For example, the display unit can provide detailed inventory information for important inventory such as food and pharmaceuticals. The display unit can also display concise information for less important inventory. For example, the display unit can provide concise inventory information for less important inventory such as daily necessities and consumables. Furthermore, the display unit can adjust the display order according to the importance of the inventory. For example, the display unit can prioritize the display of important inventory so that users can quickly check it. In this way, the display unit can adjust the level of detail displayed based on the importance of the inventory. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can adjust the level of detail displayed using a generative AI model that takes inventory importance data as input and outputs the level of detail of the display.

[0047] The display unit can apply different display algorithms depending on the inventory category when displaying information. For example, for food category inventory, the display unit can display information that takes expiration dates into consideration. For example, the display unit can evaluate the freshness of the inventory based on food expiration date data. The display unit can also display information that takes seasons and trends into consideration for clothing category inventory. For example, the display unit can evaluate the appropriateness of the inventory based on seasonal and trend data for clothing. Furthermore, the display unit can also display information that takes consumption rate into consideration for daily necessities category inventory. For example, the display unit can evaluate the timing of inventory replenishment based on daily necessities consumption rate data. This allows the display unit to apply different display algorithms depending on the inventory category. Some or all of the above processing in the display unit may be performed using, for example, generative AI, or without generative AI. For example, the display unit can apply a display algorithm using a generative AI model that takes inventory category data as input and outputs a display algorithm.

[0048] The display unit can determine the display priority based on the inventory submission date when displaying information. For example, the display unit may prioritize displaying recently added inventory. For example, the display unit may prioritize displaying recently added inventory based on inventory submission date data. The display unit can also prioritize displaying inventory that has not been used for a long period of time. For example, the display unit may prioritize displaying inventory that has not been used for a long period of time based on inventory usage history data. Furthermore, the display unit may prioritize displaying inventory that users frequently use at specific times. For example, the display unit may prioritize displaying inventory that users frequently use at specific times based on user usage pattern data. In this way, the display unit can determine the display priority based on the inventory submission date. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can determine the display priority using a generative AI model that takes inventory submission date data as input and outputs the display priority.

[0049] The display unit can adjust the display order based on the relationships between inventory items when displaying them. For example, the display unit can group together inventory items of the same category. For example, the display unit can group together inventory items of the same category based on inventory category data. The display unit can also prioritize the display of inventory items that are frequently used. For example, the display unit can prioritize the display of inventory items that are frequently used based on inventory usage frequency data. Furthermore, the display unit can prioritize the display of inventory items that the user will use for a specific event. For example, the display unit can prioritize the display of inventory items that will be used for a specific event based on the user's event data. In this way, the display unit can adjust the display order based on the relationships between inventory items. Some or all of the above processing in the display unit may be performed using, for example, generative AI, or without generative AI. For example, the display unit can adjust the display order using a generative AI model that takes inventory relationship data as input and outputs the display order.

[0050] The list creation unit can adjust the level of detail in a list based on the importance of the inventory when creating the list. For example, the list creation unit can create a detailed list for important inventory. For example, the list creation unit can provide a detailed list for important inventory such as food and medicine. The list creation unit can also create a concise list for less important inventory. For example, the list creation unit can provide a concise list for less important inventory such as daily necessities and consumables. Furthermore, the list creation unit can adjust the display order of the list according to the importance of the inventory. For example, the list creation unit can prioritize the display of important inventory so that users can quickly check it. In this way, the list creation unit can adjust the level of detail in the list based on the importance of the inventory. Some or all of the above processing in the list creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the list creation unit can adjust the level of detail in the list using a generative AI model that takes inventory importance data as input and outputs the level of detail in the list.

[0051] The list creation unit can determine the priority of the list based on the inventory submission date when creating the list. For example, the list creation unit can prioritize adding recently added inventory to the list. For example, the list creation unit can prioritize adding recently added inventory to the list based on inventory submission date data. The list creation unit can also prioritize adding inventory that has not been used for a long time to the list. For example, the list creation unit can prioritize adding inventory that has not been used for a long time to the list based on inventory usage history data. Furthermore, the list creation unit can also prioritize adding inventory that users frequently use at specific times to the list. For example, the list creation unit can prioritize adding inventory that users frequently use at specific times to the list based on user usage pattern data. In this way, the list creation unit can determine the priority of the list based on the inventory submission date. Some or all of the above processing in the list creation unit may be performed using, for example, generative AI, or without generative AI. For example, the list creation unit can determine the priority of the list using a generative AI model that takes inventory submission date data as input and outputs the list priority.

[0052] The search unit can adjust the level of detail in a search based on the importance of the products. For example, the search unit provides detailed search results for important products. For example, it provides detailed search results for important products such as food and pharmaceuticals. The search unit can also provide concise search results for less important products. For example, it provides concise search results for less important products such as daily necessities and consumables. Furthermore, the search unit can adjust the display order of search results according to the importance of the products. For example, the search unit can prioritize the display of important products so that users can quickly find them. In this way, the search unit can adjust the level of detail in a search based on the importance of the products. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can adjust the level of detail in a search using a generative AI model that takes product importance data as input and outputs the level of detail in the search.

[0053] The search unit can apply different search algorithms depending on the product category during a search. For example, for food products, the search unit considers the expiration date. For instance, it evaluates the freshness of inventory based on food expiration date data. The search unit can also consider seasons and trends for clothing products. For instance, it evaluates the appropriateness of inventory based on seasonal and trend data for clothing. Furthermore, the search unit can consider consumption rate for daily necessities products. For instance, it evaluates the timing of inventory replenishment based on daily necessities consumption rate data. This allows the search unit to apply different search algorithms depending on the product category. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can apply a search algorithm using a generative AI model that takes product category data as input and outputs a search algorithm.

[0054] The search unit can determine search priorities based on the product submission date during a search. For example, the search unit may prioritize displaying recently added products in the search results. For example, the search unit may prioritize displaying recently added products in the search results based on product submission date data. The search unit can also prioritize displaying products that have not been used for a long time in the search results. For example, the search unit may prioritize displaying products that have not been used for a long time in the search results based on product usage history data. Furthermore, the search unit may prioritize displaying products that users frequently use during specific periods in the search results. For example, the search unit may prioritize displaying products that users frequently use during specific periods in the search results based on user usage pattern data. In this way, the search unit can determine search priorities based on the product submission date. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can determine search priorities using a generative AI model that takes product submission date data as input and outputs search priorities.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past purchase history and suggest an optimal shopping list. For example, it can add frequently purchased items to a list based on data of products the user has purchased in the past. It can also analyze a user's purchasing patterns and suggest items appropriate for the season or event. For example, it can add Christmas-related items to a list based on products the user has purchased during the Christmas season in the past. Furthermore, it can suggest health foods and low-calorie products based on the user's health consciousness and diet plans. For example, it can add health-conscious products to a list based on data of health foods the user has purchased in the past. This allows the reception desk to analyze a user's past purchase history and suggest an optimal shopping list.

[0057] The search function can suggest the best place to shop based on the user's current location. For example, if the user is at home, the search function will prioritize suggesting nearby supermarkets and drugstores. If the user is out and about, the search function can also suggest the nearest store to their current location. Furthermore, if the user is traveling, the search function can suggest stores available in their travel destination. For example, the search function suggests the best place to shop based on the user's location. This allows the search function to suggest the best place to shop based on the user's current location.

[0058] The reception unit can recognize user voice commands and support voice operation. For example, if the user says, "Take a picture inside the refrigerator," the reception unit will automatically activate the smartphone camera and take a picture of the inside of the refrigerator. The reception unit can also send instructions to the list creation unit to create a shopping list if the user says, "Create a shopping list." Furthermore, if the user says, "Show the inventory status," the reception unit can send instructions to the display unit to display the inventory status. In this way, the reception unit can recognize user voice commands and support voice operation.

[0059] The display unit can analyze the user's past browsing history and suggest the optimal display method. For example, it can highlight important information based on the information the user has frequently viewed in the past. It can also analyze the user's browsing patterns and suggest the optimal layout. For example, it can suggest the optimal layout based on the layout the user has preferred to use in the past. Furthermore, it can suggest relevant information based on the user's browsing history. For example, it can suggest new information related to the information the user has viewed in the past. In this way, the display unit can analyze the user's past browsing history and suggest the optimal display method.

[0060] The list creation unit can analyze a user's past purchase history and propose the optimal list creation method. For example, it can add frequently purchased items to the list based on data of items the user has purchased in the past. It can also analyze a user's purchasing patterns and suggest items appropriate for seasons and events. For example, it can add Christmas-related items to the list based on items the user has purchased during the Christmas season in the past. Furthermore, it can suggest health foods and low-calorie products based on the user's health consciousness and diet plans. For example, it can add health-conscious products to the list based on data of health foods the user has purchased in the past. In this way, the list creation unit can analyze a user's past purchase history and propose the optimal list creation method.

[0061] The display unit can customize the inventory information displayed based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the display unit will prioritize displaying inventory information for health foods. Also, if the user is busy, the display unit can display only the minimum necessary items. Furthermore, if the user is planning a specific event (e.g., a party), the display unit can prioritize displaying inventory information for items related to that event. For example, if the user is planning a party, the display unit will prioritize displaying inventory information for drinks and snacks. In this way, the display unit can customize the inventory information displayed based on the user's current lifestyle and areas of interest.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception unit provides an interface for users to take photos. For example, the reception unit can take photos of the inside of refrigerators or shelves using a smartphone camera. The reception unit also has a function to send the photos taken by the user to the system. For example, the reception unit uploads the taken photos to a cloud server and sends them to the analysis unit. Step 2: The analysis unit uses AI object detection capabilities to identify and count the objects in the captured image. For example, the analysis unit uses an image analysis algorithm to identify objects based on their shape and color. The analysis unit can also identify the type of object by comparing its characteristics with a database. For example, the analysis unit can read the labels on the objects in the image to identify their type. Step 3: The display unit displays the inventory status in a list on the app based on the information analyzed by the analysis unit. For example, the display unit can display the quantity and type of inventory in graph or list format. The display unit can also compare the inventory quantity set by the user with the current inventory quantity and highlight items that are running low. For example, the display unit can display items with low inventory in red to alert the user.

[0064] (Example of form 2) The household goods inventory management tool according to an embodiment of the present invention is a system that allows users to keep track of their household goods inventory simply by taking a picture. This system works by taking a picture of the area to be managed, such as a refrigerator or shelves. The AI ​​object detection function identifies the items in the image and counts their quantities. This allows the inventory status to be displayed in a list on the app. Furthermore, by setting a desired inventory level in advance, the system can calculate the number of items needed based on the current inventory and create a shopping list. For example, it is possible to register items from packages that have been photographed and identified in the past. Additionally, users can search for and purchase products with the same (or similar) packaging from various shopping sites based on the image. This allows users to easily purchase necessary items. This mechanism allows users to easily keep track of their inventory in refrigerators and shelves and efficiently purchase necessary items. For example, simply taking a picture of the inside of a refrigerator displays the current inventory status in a list on the app, and missing items are automatically added to the shopping list. Furthermore, since users can search for and purchase products with the same (or similar) packaging from various shopping sites based on the image, the effort of shopping is reduced. As a result, the household goods inventory management tool allows users to easily keep track of and efficiently manage their household goods inventory.

[0065] The daily necessities inventory management tool according to this embodiment comprises a reception unit, an analysis unit, and a display unit. The reception unit provides an interface for the user to take photos. For example, the reception unit can take photos of the inside of a refrigerator or shelves using a smartphone camera. The reception unit also has a function to send the photos taken by the user to the system. For example, the reception unit uploads the taken photos to a cloud server and sends them to the analysis unit. The analysis unit uses AI object detection functionality to identify items in the captured image and count their quantities. For example, the analysis unit uses an image analysis algorithm to identify items based on their shape and color. The analysis unit can also identify the type of item by comparing the characteristics of the items with a database. For example, the analysis unit reads the labels of items in the image to identify the type of item. The display unit displays the inventory status in a list on the app based on the information analyzed by the analysis unit. For example, the display unit displays the quantity and type of inventory in graph or list format. The display unit can also compare the inventory quantity set by the user with the current inventory quantity and highlight the items that are lacking. For example, the display unit shows items with low stock in red to alert the user. This allows the household goods inventory management tool according to this embodiment to easily grasp and efficiently manage the inventory of household goods in the home.

[0066] The reception unit provides an interface for users to take photos. For example, the reception unit allows users to take pictures of refrigerators and shelves using their smartphone cameras. Specifically, when a user launches the app and selects the camera function, guidelines are displayed for photographing items inside the refrigerator or shelves. These guidelines guide the user to properly frame the items, improving the accuracy of the photograph. The reception unit also has a function to send the photos taken by the user to the system. For example, the reception unit uploads the photographed photos to a cloud server and sends them to the analysis unit. During uploading, the resolution and file size of the photos are automatically adjusted, allowing for quick and efficient data transmission. Furthermore, the reception unit also provides a function that allows users to take multiple photos at once and send them all together. This allows users to photograph the inventory status of multiple locations at once and manage them efficiently. The reception unit also has features such as voice guidance and vibration feedback to simplify user operation, and is designed for intuitive operation. As a result, the reception unit makes it possible for users to easily photograph the inventory status of daily necessities and send them to the system.

[0067] The analysis unit uses AI object detection capabilities to identify and count items in captured images. Specifically, the analysis unit uses image analysis algorithms to identify items based on their shape and color. For example, it uses a deep learning-based object detection model to recognize each item in the image with high accuracy. This model is pre-trained on a large dataset of item images and can accurately identify items even under various angles and lighting conditions. The analysis unit can also identify the type of item by comparing its features with a database. For example, it can read the labels on items in the image to identify the type of item. By using OCR (Optical Character Recognition) technology to extract text information from the labels and comparing it with a database, it obtains detailed information about the item. Furthermore, the analysis unit uses a duplicate detection algorithm for identical items to count the number of items. This allows for accurate inventory counts even if the same item is photographed multiple times. The analysis unit performs these processes in real time and can quickly provide analysis results for photos taken by the user. This allows the analysis unit to accurately and quickly extract inventory information from images taken by the user, improving the overall efficiency of the system.

[0068] The display unit displays inventory status in a list format on the app based on information analyzed by the analysis unit. Specifically, the display unit shows the quantity and type of inventory in graph and list formats. For example, it uses bar graphs and pie charts to visually display the inventory quantity of each item in an easy-to-understand way. In addition, the list format displays detailed information such as the item name, inventory quantity, and last updated date and time, allowing users to easily check the inventory status. Furthermore, the display unit can compare the inventory quantity set by the user with the current inventory quantity and highlight items that are low. For example, the display unit can display items with low inventory in red to alert the user. This allows users to quickly identify items that are running low and replenish them promptly. The display unit also provides a customizable filter function, allowing users to narrow down inventory information based on specific categories or importance. For example, it can display inventory status by category such as food, daily necessities, and consumables, allowing users to quickly access the information they need. The display unit also has a function to display inventory fluctuation history, allowing users to understand consumption trends by displaying past inventory status in graph form. This allows the display unit to provide users with a powerful tool for gaining a detailed understanding of inventory status and managing it efficiently.

[0069] The list creation unit calculates the number of items that are missing based on a pre-set inventory level and creates a shopping list. The list creation unit compares the inventory level set by the user with the current inventory level and automatically lists the missing items. For example, if the inventory level set by the user is 10 and the current inventory level is 5, the list creation unit calculates that 5 items are missing. The list creation unit also has a function to add missing items to the shopping list. For example, the list creation unit adds missing items to the list and notifies the user. Furthermore, the list creation unit can also send the shopping list to the user's smartphone. For example, the list creation unit displays the shopping list on the app so that the user can check it. In this way, the list creation unit can provide support for the user to efficiently purchase the items they need. Some or all of the above processing in the list creation unit may be performed using AI, for example, or not using AI. For example, the list creation unit can calculate the number of items that are missing using an AI model that takes the inventory level set by the user and the current inventory level as input and outputs the number of items that are missing.

[0070] The search unit searches for products with the same packaging as an image on various shopping sites and makes a purchase. The search unit identifies products with the same (or similar) packaging from a captured image and searches for them on various shopping sites. For example, the search unit uses image recognition technology to extract the features of the packaging and compare them with a database. The search unit can also search for the identified products on various shopping sites and suggest the best place to buy them. For example, the search unit searches for products on shopping sites such as Amazon and Yahoo and compares prices and availability. Furthermore, the search unit can provide a link for the user to purchase the selected product. For example, the search unit adds the selected product to the cart and supports the purchase process. In this way, the search unit can provide support for the user to efficiently purchase the products they need. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can identify products using an AI model that takes a captured image as input and outputs products with the same packaging.

[0071] The reception unit can take photos of the parts of a refrigerator or shelf that the user wants to manage inventory for. The reception unit provides an interface for taking photos of the inside of a refrigerator, for example, by using a smartphone camera to take photos of the inside of the refrigerator. The reception unit can also provide an interface for taking photos of shelves, for example, by using a smartphone camera to take photos of the inside of shelves. Furthermore, the reception unit has a function to send the photos taken by the user to the system. For example, the reception unit uploads the taken photos to a cloud server and sends them to the analysis unit. This allows the reception unit to easily take photos of the parts of a refrigerator or shelf that the user wants to manage inventory for and send them to the system. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can send photos using an AI model that takes photos taken by the user as input and sends them to the system.

[0072] The analysis unit can identify objects in an image and count their quantities. For example, the analysis unit uses AI object detection capabilities to identify objects in a captured image. For instance, it uses an image analysis algorithm to identify objects based on their shape and color. The analysis unit can also identify the type of object by comparing its characteristics with a database. For example, it reads the labels on objects in an image to identify their type. Furthermore, the analysis unit has a function to count the number of identified objects. For example, it counts the number of identical objects in an image and calculates the inventory count. This allows the analysis unit to accurately identify and count objects in an image. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can use an AI model that takes a captured image as input and outputs the type and quantity of objects to perform object identification and counting.

[0073] The display unit can display inventory status in a list on the app. For example, the display unit displays inventory status in a list on the app based on information analyzed by the analysis unit. For example, the display unit can display the quantity and type of inventory in graph or list format. The display unit can also compare the inventory quantity set by the user with the current inventory quantity and highlight items that are running low. For example, the display unit can display items with low inventory in red to alert the user. Furthermore, the display unit has a function to update inventory status in real time. For example, the display unit periodically receives information from the analysis unit to keep the inventory status up to date. This allows the display unit to easily grasp the inventory status of daily necessities in the home and manage it efficiently. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes information from the analysis unit as input and displays inventory status.

[0074] The reception unit can estimate the user's emotions and adjust the timing of photo taking based on the estimated emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expression and determine the optimal timing for taking a photo. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows the reception unit to adjust the timing of photo taking based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0075] The reception desk can analyze the user's past shooting history and select the optimal shooting method. For example, the reception desk can analyze the time of day of photos taken by the user in the past and suggest the optimal shooting time. For example, the reception desk can identify the time of day when the user's photos were most successful based on the data of photos taken by the user in the past. The reception desk can also analyze the angle and position of photos taken by the user in the past and suggest the optimal shooting method. For example, the reception desk can identify the angle and position when the user's photos were most successful based on the data of photos taken by the user in the past. Furthermore, the reception desk can analyze the quality of photos taken by the user in the past and suggest the optimal camera settings. For example, the reception desk can identify the camera settings for taking the highest quality photos based on the data of photos taken by the user in the past. In this way, the reception desk can analyze the user's past shooting history and select the optimal shooting method. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can select a shooting method using a generative AI model that takes the user's past shooting history data as input and outputs the optimal shooting method.

[0076] The reception unit can filter photos based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the reception unit can filter photos to prioritize healthy foods in the refrigerator. For example, based on the user's health consciousness, the reception unit can prioritize photographing vegetables and fruits in the refrigerator. The reception unit can also filter photos to prioritize only essential items if the user is busy. For example, based on the user's busyness, the reception unit can prioritize photographing only the main ingredients in the refrigerator. Furthermore, if the user is planning a specific event (e.g., a party), the reception unit can filter photos to prioritize items related to that event. For example, if the user is planning a party, the reception unit can prioritize photographing drinks and snacks in the refrigerator. This allows the reception unit to filter photos based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, generative AI, or without generative AI. For example, the reception unit can perform filtering using a generative AI model that takes data on the user's lifestyle and areas of interest as input and outputs filtering results.

[0077] The reception unit can estimate the user's emotions and determine the priority of subjects to photograph based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine the priority of subjects to photograph. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows the reception unit to determine the priority of subjects to photograph based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0078] The reception unit can prioritize photographing highly relevant parts of an image, taking into account the user's geographical location. For example, if the user is at home, the reception unit will prioritize photographing the refrigerator or kitchen shelves. For example, based on the user's geographical location, the reception unit will prioritize photographing the refrigerator or kitchen shelves at home. The reception unit can also prioritize photographing the contents of the bag the user is carrying if they are out. For example, based on the user's geographical location, the reception unit will prioritize photographing the contents of the bag the user is carrying if they are out. Furthermore, if the user is traveling, the reception unit can prioritize photographing items needed at their travel destination. For example, based on the user's geographical location, the reception unit will prioritize photographing items needed at their travel destination. This allows the reception unit to prioritize photographing highly relevant parts of an image, taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, generative AI, or without generative AI. For example, the reception unit can determine the priority of photography using a generative AI model that takes the user's geographical location as input and outputs highly relevant parts.

[0079] The reception desk can analyze the user's social media activity when taking photos and capture relevant parts. For example, if the user frequently posts photos of food on social media, the reception desk can prioritize photographing the ingredients in the refrigerator. Similarly, if the user posts fashion-related content on social media, the reception desk can prioritize photographing the clothes in the closet. Furthermore, if the user posts photos of pets on social media, the reception desk can prioritize photographing pet supplies. This allows the reception desk to analyze the user's social media activity and capture relevant parts. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can determine the priority of photography using a generative AI model that takes the user's social media activity data as input and outputs relevant parts.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the presentation of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the analysis unit to adjust the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the items during the analysis. For example, the analysis unit can perform a detailed analysis on important items and display the inventory quantity and frequency of use. For example, the analysis unit can provide detailed inventory information for important items such as food and pharmaceuticals. The analysis unit can also perform a simplified analysis on less important items and display only the inventory quantity. For example, the analysis unit can provide simplified inventory information for less important items such as daily necessities and consumables. Furthermore, the analysis unit can adjust the display order of the analysis results according to the importance of the items. For example, the analysis unit can prioritize the display of important items so that users can quickly check them. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the items. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can adjust the level of detail of the analysis using a generative AI model that takes item importance data as input and outputs the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the item during analysis. For example, for items in the food category, the analysis unit performs analysis that takes expiration dates into account. For example, the analysis unit evaluates the freshness of inventory based on food expiration date data. The analysis unit can also perform analysis that takes seasons and trends into account for items in the clothing category. For example, the analysis unit evaluates the appropriateness of inventory based on seasonal and trend data for clothing. Furthermore, the analysis unit can also perform analysis that takes consumption rates into account for items in the daily necessities category. For example, the analysis unit evaluates the timing of inventory replenishment based on daily necessities consumption rate data. In this way, the analysis unit can apply different analysis algorithms depending on the category of the item. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can apply an analysis algorithm using a generative AI model that takes item category data as input and outputs an analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the length of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the analysis unit to adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0084] The analysis unit can determine the priority of analysis based on the submission date of the items during the analysis. For example, the analysis unit may prioritize the analysis of recently added items. For example, the analysis unit may prioritize the analysis of recently added items based on the submission date data of the items. The analysis unit may also prioritize the analysis of items that have not been used for a long period of time. For example, the analysis unit may prioritize the analysis of items that have not been used for a long period of time based on the usage history data of the items. Furthermore, the analysis unit may also prioritize the analysis of items that the user frequently uses during specific periods. For example, the analysis unit may prioritize the analysis of items that the user frequently uses during specific periods based on the user's usage pattern data. This allows the analysis unit to determine the priority of analysis based on the submission date of the items. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit may determine the priority of analysis using a generative AI model that takes the submission date data of the items as input and outputs the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relationships between items during the analysis. For example, the analysis unit can analyze items of the same category together. For example, the analysis unit can analyze items of the same category together based on item category data. The analysis unit can also prioritize the analysis of items with high usage frequency. For example, the analysis unit can prioritize the analysis of items with high usage frequency based on item usage frequency data. Furthermore, the analysis unit can prioritize the analysis of items used by the user in a specific event. For example, the analysis unit can prioritize the analysis of items used in a specific event based on the user's event data. This allows the analysis unit to adjust the order of analysis based on the relationships between items. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can adjust the order of analysis using a generative AI model that takes item relationship data as input and outputs the order of analysis.

[0086] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, the display unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on changes in facial expressions and adjust the display method. The display unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the display unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the display unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on fluctuations in heart rate. This allows the display unit to adjust the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0087] The display unit can adjust the level of detail displayed based on the importance of the inventory. For example, the display unit can display detailed information for important inventory. For example, the display unit can provide detailed inventory information for important inventory such as food and pharmaceuticals. The display unit can also display concise information for less important inventory. For example, the display unit can provide concise inventory information for less important inventory such as daily necessities and consumables. Furthermore, the display unit can adjust the display order according to the importance of the inventory. For example, the display unit can prioritize the display of important inventory so that users can quickly check it. In this way, the display unit can adjust the level of detail displayed based on the importance of the inventory. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can adjust the level of detail displayed using a generative AI model that takes inventory importance data as input and outputs the level of detail of the display.

[0088] The display unit can apply different display algorithms depending on the inventory category when displaying information. For example, for food category inventory, the display unit can display information that takes expiration dates into consideration. For example, the display unit can evaluate the freshness of the inventory based on food expiration date data. The display unit can also display information that takes seasons and trends into consideration for clothing category inventory. For example, the display unit can evaluate the appropriateness of the inventory based on seasonal and trend data for clothing. Furthermore, the display unit can also display information that takes consumption rate into consideration for daily necessities category inventory. For example, the display unit can evaluate the timing of inventory replenishment based on daily necessities consumption rate data. This allows the display unit to apply different display algorithms depending on the inventory category. Some or all of the above processing in the display unit may be performed using, for example, generative AI, or without generative AI. For example, the display unit can apply a display algorithm using a generative AI model that takes inventory category data as input and outputs a display algorithm.

[0089] The display unit can estimate the user's emotions and adjust the display length based on the estimated emotions. For example, the display unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on changes in facial expressions and adjust the display length. The display unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the display unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the display unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on fluctuations in heart rate. This allows the display unit to adjust the display length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0090] The display unit can determine the display priority based on the inventory submission date when displaying information. For example, the display unit may prioritize displaying recently added inventory. For example, the display unit may prioritize displaying recently added inventory based on inventory submission date data. The display unit can also prioritize displaying inventory that has not been used for a long period of time. For example, the display unit may prioritize displaying inventory that has not been used for a long period of time based on inventory usage history data. Furthermore, the display unit may prioritize displaying inventory that users frequently use at specific times. For example, the display unit may prioritize displaying inventory that users frequently use at specific times based on user usage pattern data. In this way, the display unit can determine the display priority based on the inventory submission date. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can determine the display priority using a generative AI model that takes inventory submission date data as input and outputs the display priority.

[0091] The display unit can adjust the display order based on the relationships between inventory items when displaying them. For example, the display unit can group together inventory items of the same category. For example, the display unit can group together inventory items of the same category based on inventory category data. The display unit can also prioritize the display of inventory items that are frequently used. For example, the display unit can prioritize the display of inventory items that are frequently used based on inventory usage frequency data. Furthermore, the display unit can prioritize the display of inventory items that the user will use for a specific event. For example, the display unit can prioritize the display of inventory items that will be used for a specific event based on the user's event data. In this way, the display unit can adjust the display order based on the relationships between inventory items. Some or all of the above processing in the display unit may be performed using, for example, generative AI, or without generative AI. For example, the display unit can adjust the display order using a generative AI model that takes inventory relationship data as input and outputs the display order.

[0092] The list creation unit can estimate the user's emotions and adjust the list creation method based on the estimated emotions. For example, the list creation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the list creation unit can calculate an emotion score based on changes in facial expressions and adjust the list creation method. The list creation unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the list creation unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the list creation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the list creation unit can calculate an emotion score based on fluctuations in heart rate. This allows the list creation unit to adjust the list creation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can perform emotion estimation using an AI model that takes user facial expression data as input and outputs an emotion score.

[0093] The list creation unit can adjust the level of detail in a list based on the importance of the inventory when creating the list. For example, the list creation unit can create a detailed list for important inventory. For example, the list creation unit can provide a detailed list for important inventory such as food and medicine. The list creation unit can also create a concise list for less important inventory. For example, the list creation unit can provide a concise list for less important inventory such as daily necessities and consumables. Furthermore, the list creation unit can adjust the display order of the list according to the importance of the inventory. For example, the list creation unit can prioritize the display of important inventory so that users can quickly check it. In this way, the list creation unit can adjust the level of detail in the list based on the importance of the inventory. Some or all of the above processing in the list creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the list creation unit can adjust the level of detail in the list using a generative AI model that takes inventory importance data as input and outputs the level of detail in the list.

[0094] The list creation unit can estimate the user's emotions and determine the priority of list creation based on the estimated user emotions. For example, the list creation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the list creation unit can calculate an emotion score based on changes in facial expressions and determine the priority of list creation. The list creation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the list creation unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the list creation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the list creation unit can calculate an emotion score based on fluctuations in heart rate. As a result, the list creation unit can determine the priority of list creation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can perform emotion estimation using an AI model that takes user facial expression data as input and outputs an emotion score.

[0095] The list creation unit can determine the priority of the list based on the inventory submission date when creating the list. For example, the list creation unit can prioritize adding recently added inventory to the list. For example, the list creation unit can prioritize adding recently added inventory to the list based on inventory submission date data. The list creation unit can also prioritize adding inventory that has not been used for a long time to the list. For example, the list creation unit can prioritize adding inventory that has not been used for a long time to the list based on inventory usage history data. Furthermore, the list creation unit can also prioritize adding inventory that users frequently use at specific times to the list. For example, the list creation unit can prioritize adding inventory that users frequently use at specific times to the list based on user usage pattern data. In this way, the list creation unit can determine the priority of the list based on the inventory submission date. Some or all of the above processing in the list creation unit may be performed using, for example, generative AI, or without generative AI. For example, the list creation unit can determine the priority of the list using a generative AI model that takes inventory submission date data as input and outputs the list priority.

[0096] The search unit can estimate the user's emotions and adjust the search method based on the estimated emotions. For example, the search unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on changes in facial expressions and adjust the search method. The search unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the search unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the search unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on fluctuations in heart rate. This allows the search unit to adjust the search method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the search unit may be performed using AI, for example, or without AI. For example, the search unit can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0097] The search unit can adjust the level of detail in a search based on the importance of the products. For example, the search unit provides detailed search results for important products. For example, it provides detailed search results for important products such as food and pharmaceuticals. The search unit can also provide concise search results for less important products. For example, it provides concise search results for less important products such as daily necessities and consumables. Furthermore, the search unit can adjust the display order of search results according to the importance of the products. For example, the search unit can prioritize the display of important products so that users can quickly find them. In this way, the search unit can adjust the level of detail in a search based on the importance of the products. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can adjust the level of detail in a search using a generative AI model that takes product importance data as input and outputs the level of detail in the search.

[0098] The search unit can apply different search algorithms depending on the product category during a search. For example, for food products, the search unit considers the expiration date. For instance, it evaluates the freshness of inventory based on food expiration date data. The search unit can also consider seasons and trends for clothing products. For instance, it evaluates the appropriateness of inventory based on seasonal and trend data for clothing. Furthermore, the search unit can consider consumption rate for daily necessities products. For instance, it evaluates the timing of inventory replenishment based on daily necessities consumption rate data. This allows the search unit to apply different search algorithms depending on the product category. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can apply a search algorithm using a generative AI model that takes product category data as input and outputs a search algorithm.

[0099] The search unit can estimate the user's emotions and determine search priorities based on the estimated emotions. For example, the search unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on changes in facial expressions and determine search priorities. The search unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the search unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the search unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on fluctuations in heart rate. This allows the search unit to determine search priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the search unit may be performed using AI, for example, or without AI. For example, the search unit can estimate emotions using an AI model that takes user facial expression data as input and outputs an emotion score.

[0100] The search unit can determine search priorities based on the product submission date during a search. For example, the search unit may prioritize displaying recently added products in the search results. For example, the search unit may prioritize displaying recently added products in the search results based on product submission date data. The search unit can also prioritize displaying products that have not been used for a long time in the search results. For example, the search unit may prioritize displaying products that have not been used for a long time in the search results based on product usage history data. Furthermore, the search unit may prioritize displaying products that users frequently use during specific periods in the search results. For example, the search unit may prioritize displaying products that users frequently use during specific periods in the search results based on user usage pattern data. In this way, the search unit can determine search priorities based on the product submission date. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can determine search priorities using a generative AI model that takes product submission date data as input and outputs search priorities.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can analyze a user's past purchase history and suggest an optimal shopping list. For example, it can add frequently purchased items to a list based on data of products the user has purchased in the past. It can also analyze a user's purchasing patterns and suggest items appropriate for the season or event. For example, it can add Christmas-related items to a list based on products the user has purchased during the Christmas season in the past. Furthermore, it can suggest health foods and low-calorie products based on the user's health consciousness and diet plans. For example, it can add health-conscious products to a list based on data of health foods the user has purchased in the past. This allows the reception desk to analyze a user's past purchase history and suggest an optimal shopping list.

[0103] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is stressed, the analysis unit will display the results concisely to reduce the user's burden. If the user is relaxed, the analysis unit will display detailed results to allow the user to understand the information more deeply. Furthermore, if the user is excited, the analysis unit can display the results in a visually appealing format to capture the user's interest. For example, the analysis unit can calculate an emotion score based on the user's facial expression data and adjust the display method accordingly. This allows the analysis unit to adjust how the analysis results are displayed based on the user's emotions.

[0104] The search function can suggest the best place to shop based on the user's current location. For example, if the user is at home, the search function will prioritize suggesting nearby supermarkets and drugstores. If the user is out and about, the search function can also suggest the nearest store to their current location. Furthermore, if the user is traveling, the search function can suggest stores available in their travel destination. For example, the search function suggests the best place to shop based on the user's location. This allows the search function to suggest the best place to shop based on the user's current location.

[0105] The reception unit can recognize user voice commands and support voice operation. For example, if the user says, "Take a picture inside the refrigerator," the reception unit will automatically activate the smartphone camera and take a picture of the inside of the refrigerator. The reception unit can also send instructions to the list creation unit to create a shopping list if the user says, "Create a shopping list." Furthermore, if the user says, "Show the inventory status," the reception unit can send instructions to the display unit to display the inventory status. In this way, the reception unit can recognize user voice commands and support voice operation.

[0106] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on those emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of important items to reduce the user's burden. If the user is relaxed, the analysis unit can also analyze all items equally. Furthermore, if the user is excited, the analysis unit can prioritize the analysis of items that are likely to interest them. For example, the analysis unit can calculate an emotion score based on the user's facial expression data and determine the priority of the analysis. This allows the analysis unit to determine the priority of the analysis based on the user's emotions.

[0107] The display unit can analyze the user's past browsing history and suggest the optimal display method. For example, it can highlight important information based on the information the user has frequently viewed in the past. It can also analyze the user's browsing patterns and suggest the optimal layout. For example, it can suggest the optimal layout based on the layout the user has preferred to use in the past. Furthermore, it can suggest relevant information based on the user's browsing history. For example, it can suggest new information related to the information the user has viewed in the past. In this way, the display unit can analyze the user's past browsing history and suggest the optimal display method.

[0108] The reception desk can estimate the user's emotions and provide feedback on the photo shoot based on those emotions. For example, if the reception desk is feeling anxious, it can explain the photo shoot procedure in detail to alleviate the user's anxiety. If the user is confident, the reception desk can provide concise feedback to facilitate the user's operation. Furthermore, if the user is excited, the reception desk can provide positive feedback to increase the user's motivation. For instance, the reception desk can calculate an emotion score based on the user's facial expression data and provide feedback accordingly. This allows the reception desk to provide photo shoot feedback based on the user's emotions.

[0109] The list creation unit can analyze a user's past purchase history and propose the optimal list creation method. For example, it can add frequently purchased items to the list based on data of items the user has purchased in the past. It can also analyze a user's purchasing patterns and suggest items appropriate for seasons and events. For example, it can add Christmas-related items to the list based on items the user has purchased during the Christmas season in the past. Furthermore, it can suggest health foods and low-calorie products based on the user's health consciousness and diet plans. For example, it can add health-conscious products to the list based on data of health foods the user has purchased in the past. In this way, the list creation unit can analyze a user's past purchase history and propose the optimal list creation method.

[0110] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is stressed, the search engine can display search results concisely to reduce the user's burden. Conversely, if the user is relaxed, the search engine can display detailed search results to allow the user to understand the information more deeply. Furthermore, if the user is excited, the search engine can display search results in a visually appealing format to capture the user's interest. For instance, the search engine can calculate an emotion score based on the user's facial expression data and adjust the display method accordingly. This allows the search engine to adjust how search results are displayed based on the user's emotions.

[0111] The display unit can customize the inventory information displayed based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the display unit will prioritize displaying inventory information for health foods. Also, if the user is busy, the display unit can display only the minimum necessary items. Furthermore, if the user is planning a specific event (e.g., a party), the display unit can prioritize displaying inventory information for items related to that event. For example, if the user is planning a party, the display unit will prioritize displaying inventory information for drinks and snacks. In this way, the display unit can customize the inventory information displayed based on the user's current lifestyle and areas of interest.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception unit provides an interface for users to take photos. For example, the reception unit can take photos of the inside of refrigerators or shelves using a smartphone camera. The reception unit also has a function to send the photos taken by the user to the system. For example, the reception unit uploads the taken photos to a cloud server and sends them to the analysis unit. Step 2: The analysis unit uses AI object detection capabilities to identify and count the objects in the captured image. For example, the analysis unit uses an image analysis algorithm to identify objects based on their shape and color. The analysis unit can also identify the type of object by comparing its characteristics with a database. For example, the analysis unit can read the labels on the objects in the image to identify their type. Step 3: The display unit displays the inventory status in a list on the app based on the information analyzed by the analysis unit. For example, the display unit can display the quantity and type of inventory in graph or list format. The display unit can also compare the inventory quantity set by the user with the current inventory quantity and highlight items that are running low. For example, the display unit can display items with low inventory in red to alert the user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] For example, the reception unit can take pictures of the inside of refrigerators and shelves using the camera 42 of the smart device 14. For example, the reception unit transmits the captured pictures to the data processing unit 12 via the communication I / F 44 of the smart device 14. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses the AI ​​object detection function to identify items in the captured images and counts their quantities. For example, the display unit uses the display 40A of the smart device 14 to display the inventory status in a list on the app. For example, the list creation unit is implemented by the identification processing unit 290 of the data processing unit 12, which compares the inventory quantity set by the user with the current inventory quantity and automatically lists the items that are missing. For example, the search unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies products with the same (or similar) packaging from the captured images and searches for them on various shopping sites. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] For example, the reception unit can use the camera 42 of the smart glasses 214 to take pictures of the inside of refrigerators and shelves. For example, the reception unit transmits the captured pictures to the data processing unit 12 via the communication I / F 44 of the smart glasses 214. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses the AI ​​object detection function to identify items in the captured images and counts their quantities. For example, the display unit uses the display of the smart glasses 214 to display the inventory status in a list on the app. For example, the list creation unit is implemented by the identification processing unit 290 of the data processing unit 12, which compares the inventory quantity set by the user with the current inventory quantity and automatically lists the items that are missing. For example, the search unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies products with the same (or similar) packaging from the captured images and searches for them on various shopping sites. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] For example, the reception unit can take pictures of the inside of refrigerators and shelves using the camera 42 of the headset terminal 314. For example, the reception unit transmits the captured pictures to the data processing unit 12 via the communication I / F 44 of the headset terminal 314. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses the AI ​​object detection function to identify items in the captured images and counts their quantities. For example, the display unit uses the display 343 of the headset terminal 314 to display the inventory status in a list on the app. For example, the list creation unit is implemented by the identification processing unit 290 of the data processing unit 12, which compares the inventory quantity set by the user with the current inventory quantity and automatically lists the items that are missing. For example, the search unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies products with the same (or similar) packaging from the captured images and searches for them on various shopping sites. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] For example, the reception unit can use the camera 42 of the robot 414 to take pictures of the inside of refrigerators and shelves. For example, the reception unit transmits the pictures taken via the communication I / F 44 of the robot 414 to the data processing unit 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses the AI ​​object detection function to identify items in the captured images and counts their quantities. For example, the display unit uses the display of the robot 414 to display the inventory status in a list on the app. For example, the list creation unit is implemented by the identification processing unit 290 of the data processing unit 12, which compares the inventory quantity set by the user with the current inventory quantity and automatically lists the items that are missing. For example, the search unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies products with the same (or similar) packaging from the captured images and searches for them on various shopping sites. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The reception desk where photos are taken, An analysis unit analyzes the images taken by the reception unit, identifies the items, and counts them. The system includes a display unit that displays the inventory status based on the information analyzed by the analysis unit. A system characterized by the following features. (Note 2) It includes a list creation unit that calculates the number of items needed based on a pre-set inventory level and creates a shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a search function that allows users to find and purchase products with the same packaging as shown in the image on various shopping websites. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Take a picture of the part of the refrigerator or shelf where you want to manage your inventory. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Identify and count the number of items in an image. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Display inventory status in the app The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of photo shoots based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past shooting history and selects the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When taking photos, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of subjects to photograph based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When taking photos, the system prioritizes capturing relevant areas by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When taking photos, the system analyzes the user's social media activity and captures relevant parts. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the item. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between items. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying information, adjust the level of detail based on the importance of the inventory. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying items, different display algorithms are applied depending on the inventory category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is It estimates the user's emotions and adjusts the display length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying inventory, the display priority is determined based on when the inventory was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying items, adjust the display order based on inventory relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned list creation unit, We estimate the user's sentiment and adjust the list creation method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned list creation unit, When creating a list, adjust the level of detail based on the importance of the inventory. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned list creation unit, The system estimates the user's emotions and determines the priority of list creation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned list creation unit, When creating the list, prioritize the list based on when the inventory was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned search unit, It estimates the user's sentiment and adjusts the search method based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned search unit, When searching, adjust the search level based on the importance of the product. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned search unit, When searching, different search algorithms are applied depending on the product category. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned search unit, It estimates the user's sentiment and determines search priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned search unit, When searching, search priorities are determined based on when the product was submitted. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where photos are taken, An analysis unit analyzes the images taken by the reception unit, identifies the items, and counts them. The system includes a display unit that displays the inventory status based on the information analyzed by the analysis unit. A system characterized by the following features.

2. It includes a list creation unit that calculates the number of items needed based on a pre-set inventory level and creates a shopping list. The system according to feature 1.

3. It features a search function that allows users to find and purchase products with the same packaging as shown in the image on various shopping websites. The system according to feature 1.

4. The aforementioned reception unit is Take a picture of the part of the refrigerator or shelf where you want to manage your inventory. The system according to feature 1.

5. The aforementioned analysis unit, Identify and count the number of items in an image. The system according to feature 1.

6. The aforementioned display unit is Display inventory status in the app The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of photo shoots based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past shooting history and selects the optimal shooting method. The system according to feature 1.

Images