system

Abstract

The system according to this embodiment aims to automate the inventory management of household consumables and food items and to support replenishment at the appropriate time. [Solution] The system according to the embodiment comprises a monitoring unit, an ordering unit, a refrigerator monitoring unit, and a notification unit. The monitoring unit monitors the remaining amount of detergent. The ordering unit automatically places an order when the remaining amount monitored by the monitoring unit falls below a certain amount. The refrigerator monitoring unit monitors the inventory of food items in the refrigerator. The notification unit informs the system of any missing food items based on the inventory monitored by the refrigerator monitoring unit.

Description

Technical Field

[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 a character of the chatbot, 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, there is a problem that inventory management of consumables and foodstuffs in the home is troublesome and replenishment at an appropriate timing is difficult.

[0005] The system according to the embodiment aims to automate the inventory management of consumables and foodstuffs in the home and support replenishment at an appropriate timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an ordering unit, a refrigerator monitoring unit, and a notification unit. The monitoring unit monitors the remaining amount of detergent. The ordering unit automatically places an order when the remaining amount monitored by the monitoring unit falls below a certain amount. The refrigerator monitoring unit monitors the inventory of food items in the refrigerator. The notification unit informs the system of any missing food items based on the inventory monitored by the refrigerator monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate inventory management of household consumables and food items and support replenishment at the appropriate time. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 assistance system according to an embodiment of the present invention is a system designed to solve the problem of not having enough time for housework or the house quickly becoming dirty due to laziness. This household assistance system uses IoT (Vision AI) equipped cameras permanently installed in front of the washing machine, in the living room, inside the refrigerator, etc., to assist with all household chores. This system has a function to automatically order detergent from e-commerce when it runs out, a function to constantly monitor the ingredients in the refrigerator and notify the user of any missing ingredients, and a function to tell the AI ​​which ingredients are missing from the refrigerator when the user sends a message such as "I'm going to make curry today" via LINE® or a dedicated app. As a result, the user can perform accurate actions with fewer instructions, significantly reducing the burden of housework. Furthermore, convenience is improved by utilizing the LINE ecosystem and the Yahoo® ecosystem. For example, let's explain the automatic detergent ordering function. In this function, the camera monitors the remaining amount of detergent and automatically places an order from an e-commerce site when it falls below a certain amount. Next, let's explain the function that notifies the user of any missing ingredients. In this function, the camera inside the refrigerator monitors the inventory of ingredients and notifies the user of any missing ingredients when the user gives the instruction "I'm going to make curry". This allows users to easily identify and purchase necessary ingredients. Furthermore, these functions work together, enabling users to perform accurate actions with minimal instructions. For example, the automatic detergent ordering function and the function that notifies users of ingredient shortages work together, significantly reducing the burden of household chores. It also clarifies how each function provides information to the user. For instance, the notification unit not only notifies the user of missing ingredients but also provides a link to an e-commerce site, allowing users to easily place orders. In this way, the household assistance system reduces the burden on users and enables them to perform household chores efficiently.

[0029] The household assistance system according to this embodiment comprises a monitoring unit, an ordering unit, and a notification unit. The monitoring unit monitors the remaining amount of detergent. The monitoring unit monitors the remaining amount of detergent using, for example, a camera. The camera is, for example, a high-resolution camera that can accurately detect the remaining amount in the detergent bottle. The monitoring unit also monitors the inventory of food items in the refrigerator. The monitoring unit monitors the inventory of food items using, for example, a camera installed inside the refrigerator. The camera is, for example, a camera equipped with a wide-angle lens that can monitor the entire inside of the refrigerator. The ordering unit automatically places an order when the remaining amount monitored by the monitoring unit falls below a certain amount. The ordering unit automatically places an order from, for example, an e-commerce site. The ordering unit can, for example, access an e-commerce site pre-set by the user and automatically order the necessary products. The notification unit informs the user of any missing food items based on the inventory monitored by the monitoring unit. The notification unit notifies the user of any missing food items. The notification unit can, for example, send a notification to the user through a smartphone app. The notification unit also provides a link to the e-commerce site. The notification unit, for example, provides a link to an e-commerce site simultaneously with the notification, allowing the user to easily place an order. This enables the household assistance system according to the embodiment to reduce the user's burden and perform household chores efficiently.

[0030] The monitoring unit can monitor the remaining amount of detergent using a camera. The camera may include, for example, a high-resolution camera. The camera can accurately detect the remaining amount in the detergent bottle. For example, the camera can read the label on the detergent bottle and calculate the remaining amount. The camera can also measure the height of the detergent bottle and estimate the remaining amount. Furthermore, the camera can detect changes in the color of the detergent bottle and determine the remaining amount. In this way, the remaining amount of detergent can be accurately monitored using the camera. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input image data acquired by the camera into a generating AI and have the generating AI perform the process of estimating the remaining amount of detergent from the image data.

[0031] The ordering unit can automatically place orders from e-commerce sites. For example, the ordering unit can access e-commerce sites pre-configured by the user and automatically order the necessary products. For instance, the ordering unit can log in using the user's e-commerce site account information, add products to the cart, and confirm the order. The ordering unit can also automatically make payments using the payment method set by the user. Furthermore, the ordering unit can save the order history and refer to it for future orders. This reduces the user's effort by allowing them to place orders automatically from e-commerce sites. Some or all of the above processes in the ordering unit may be performed using AI, for example, or not. For example, the ordering unit can input the user's order history into a generating AI and have the generating AI perform the process of selecting the optimal supplier.

[0032] The monitoring unit can monitor the inventory of food items inside the refrigerator using cameras. These cameras may include, for example, cameras equipped with wide-angle lenses. The cameras can monitor the entire interior of the refrigerator. For example, the cameras can photograph food items on each shelf to check inventory. The cameras can also read food labels and manage inventory. Furthermore, the cameras can detect changes in food color to determine freshness. This allows for accurate monitoring of food inventory within the refrigerator using cameras. Some or all of the above-described processes in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input image data acquired by the cameras into a generating AI, which can then perform the process of estimating food inventory from the image data.

[0033] The notification unit can notify the user of missing ingredients. The notification unit can send notifications to the user, for example, through a smartphone app. For example, the notification unit can send a push notification to an app set by the user to inform them of missing ingredients. The notification unit can also send notifications to the user via email. For example, the notification unit can send a list of missing ingredients to an email address set by the user. Furthermore, the notification unit can also notify the user through a voice assistant. For example, the notification unit can have the voice assistant read out the missing ingredients. This makes it easy for the user to know what ingredients they need by notifying them of missing ingredients. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the user's notification history into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0034] The notification unit can provide links to e-commerce sites. For example, the notification unit can provide a link to an e-commerce site simultaneously with a notification, allowing users to easily place orders. For example, the notification unit can send links to users via a smartphone app. The notification unit can also send links to users via email. For example, the notification unit can send a link to an e-commerce site to an email address set by the user. Furthermore, the notification unit can provide links to users via a voice assistant. For example, the notification unit can have a voice assistant read out the link. This allows users to easily place orders by providing links to e-commerce sites. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's link provision history into a generating AI and have the generating AI perform the process of selecting the optimal link destination.

[0035] The monitoring unit can analyze the detergent usage history and determine the optimal monitoring timing. For example, the monitoring unit can analyze detergent consumption patterns from past usage history and set the optimal monitoring timing. For example, the monitoring unit can identify detergent consumption patterns based on past usage frequency and amount, and adjust the monitoring timing based on those patterns. The monitoring unit can also identify time periods when detergent is used frequently and focus monitoring during those times. For example, the monitoring unit will focus monitoring the remaining amount of detergent during time periods when laundry is frequently performed. Furthermore, if detergent usage suddenly increases, the monitoring unit can temporarily increase the monitoring frequency to prompt early replenishment. For example, if detergent usage suddenly increases, the monitoring unit will notify the user to replenish early. This allows monitoring to be performed at the optimal timing by analyzing the usage history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input detergent usage history data into a generating AI and have the generating AI execute the process of determining the optimal monitoring timing.

[0036] The monitoring unit can apply different monitoring algorithms depending on the type and amount of detergent used. For example, the monitoring unit can apply different monitoring algorithms to liquid detergents and powder detergents, performing monitoring according to their respective characteristics. For example, in the case of liquid detergents, the monitoring unit measures the remaining amount in the bottle, and in the case of powder detergents, it measures the weight of the container. The monitoring unit can also monitor detergents used in large quantities more frequently and prompt for earlier replenishment. For example, the monitoring unit can focus on monitoring the remaining amount of detergents used in large quantities and notify the user to replenish early. Furthermore, the monitoring unit can apply a dedicated monitoring algorithm to specific brands of detergents and set the optimal replenishment timing. For example, the monitoring unit can analyze the usage patterns of a specific brand of detergent and set the optimal replenishment timing based on those patterns. This enables more accurate monitoring by performing monitoring according to the type and amount of detergent used. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input detergent type and usage data into a generating AI and have the generating AI execute the process of applying the optimal monitoring algorithm.

[0037] The monitoring unit can adjust the timing of detergent level monitoring by considering the user's lifestyle patterns. For example, the monitoring unit can identify the time of day when the user does laundry and focus on monitoring the detergent level during that time. For example, the monitoring unit can analyze the user's laundry frequency and timing and adjust the monitoring timing based on that data. The monitoring unit can also reduce the monitoring frequency when the user is out and focus on monitoring when the user is at home. For example, the monitoring unit can consider the user's time out and focus on monitoring the detergent level when the user is at home. Furthermore, the monitoring unit can flexibly adjust the timing of detergent level monitoring to match the user's lifestyle rhythm. For example, the monitoring unit can consider the user's wake-up and bedtime and monitor accordingly. By adjusting the monitoring timing to match the user's lifestyle patterns, monitoring can be performed at a more appropriate time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user lifestyle pattern data into a generating AI and have the generating AI execute a process to determine the optimal monitoring timing.

[0038] The monitoring unit can simultaneously monitor the remaining amount of other household items when monitoring the remaining amount of detergent. For example, when monitoring the remaining amount of detergent, the monitoring unit can also simultaneously monitor the remaining amounts of fabric softener and bleach and prompt for replenishment as needed. For example, the monitoring unit can use a camera that monitors the remaining amount of detergent to simultaneously monitor the remaining amounts of fabric softener and bleach bottles. The monitoring unit can also simultaneously monitor the remaining amounts of other consumables around the washing machine (laundry nets, clothespins, etc.). For example, the monitoring unit can use cameras installed around the washing machine to monitor the remaining amounts of other consumables. Furthermore, the monitoring unit can check the maintenance status of the washing machine at the same time as monitoring the remaining amount of detergent and suggest necessary maintenance. For example, the monitoring unit can monitor the degree of soiling of the washing machine filter and notify the user of necessary maintenance. This allows for more efficient housework by simultaneously monitoring the remaining amounts of other household items. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input remaining quantity data of other household items into the generating AI, and have the generating AI perform the process of determining the optimal monitoring timing.

[0039] The ordering department can analyze past order history and select the optimal supplier. For example, the ordering department can select the most reliable supplier based on past order history. For example, the ordering department can analyze past order history and select the supplier that delivers the fastest. The ordering department can also prioritize suppliers that have received high ratings in the past, according to the user's preferences. For example, the ordering department can prioritize suppliers that users have given high ratings to in the past. Furthermore, the ordering department can analyze past order history and select the supplier with the best cost performance. For example, the ordering department can select the cheapest and highest quality supplier based on past order history. In this way, the optimal supplier can be selected by analyzing past order history. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input past order history data into a generating AI and have the generating AI perform the process of selecting the optimal supplier.

[0040] The ordering system allows users to select different e-commerce sites based on their preferences when placing an order. For example, if a user prefers a specific brand of detergent, the ordering system will prioritize e-commerce sites that carry that brand. For instance, the ordering system will search for e-commerce sites that carry the brand of detergent specified by the user and select the most suitable site. Furthermore, if price is a priority, the ordering system can select the cheapest e-commerce site. For example, the ordering system will compare prices across multiple e-commerce sites and select the cheapest one. Additionally, if delivery speed is a priority, the ordering system can select the site with the fastest delivery. For example, the ordering system will compare delivery options across multiple e-commerce sites and select the site with the fastest delivery. This allows for more satisfying orders by selecting e-commerce sites that match the user's preferences. Some or all of the above processes in the ordering system may be performed using AI, or not. For example, the ordering system can input user preference data into a generating AI and have the AI ​​perform the process of selecting the most suitable e-commerce site.

[0041] The ordering system can select an appropriate delivery method when an order is placed, taking into account the user's geographical location. For example, if the user is at home, the ordering system will select a standard delivery method. For example, the ordering system will select the optimal delivery method based on the user's geographical location. The ordering system can also select pickup at the nearest convenience store if the user is out. For example, the ordering system will select the nearest pickup location based on the user's geographical location. Furthermore, if the user is traveling, the ordering system can select a delivery method to their travel destination address. For example, the ordering system will select a delivery method to their travel destination address based on the user's geographical location. In this way, the system can select the optimal delivery method by taking into account the user's geographical location. Some or all of the above processing in the ordering system may be performed using AI, for example, or without AI. For example, the ordering system can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting the optimal delivery method.

[0042] The ordering system allows users to order other necessary household items at the same time as placing an order. For example, when ordering detergent, the ordering system can also order related items such as fabric softener and bleach. For example, the ordering system can check the inventory of related items and automatically order the necessary items at the same time as ordering detergent. The ordering system can also order consumables for the washing machine (such as laundry nets and clothespins) at the same time. For example, the ordering system can check the inventory of consumables for the washing machine and automatically order the necessary items. Furthermore, the ordering system can automatically detect household items that the user has previously ordered and are running low on stock, and order them simultaneously. For example, based on the user's order history, the ordering system can detect items that are running low on stock and automatically order the necessary items. This allows for more efficient household chores by ordering other necessary household items at the same time. Some or all of the above processes in the ordering system may be performed using AI, or not. For example, the ordering system can input inventory data for other household items into a generating AI and have the generating AI perform the process of determining the optimal ordering timing.

[0043] The monitoring unit can analyze the consumption history of ingredients and determine the appropriate monitoring timing. For example, the monitoring unit can analyze the consumption patterns of ingredients from past consumption history and set the optimal monitoring timing. For example, the monitoring unit can identify the consumption patterns of ingredients based on past consumption frequency and quantity, and adjust the monitoring timing based on those patterns. The monitoring unit can also identify time periods when ingredients are consumed frequently and focus monitoring during those times. For example, the monitoring unit focuses monitoring during time periods when ingredients are consumed frequently. Furthermore, if the consumption of ingredients suddenly increases, the monitoring unit can temporarily increase the monitoring frequency to encourage early replenishment. For example, when the consumption of ingredients suddenly increases, the monitoring unit will notify the user to replenish early. This allows monitoring to be performed at the optimal timing by analyzing the consumption history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the consumption history data of ingredients into a generating AI and have the generating AI execute the process of determining the optimal monitoring timing.

[0044] The monitoring unit can apply different monitoring algorithms depending on the type and amount of ingredients used. For example, the monitoring unit can apply different monitoring algorithms to vegetables and meats, performing monitoring according to their respective characteristics. For instance, the monitoring unit prioritizes freshness monitoring for vegetables and expiration date monitoring for meats. The monitoring unit can also monitor ingredients used in large quantities more frequently and prompt earlier replenishment. For example, the monitoring unit can focus on monitoring the inventory of ingredients used in large quantities and notify early replenishment. Furthermore, the monitoring unit can apply a dedicated monitoring algorithm to specific brands of ingredients and set the optimal replenishment timing. For example, the monitoring unit can analyze the usage patterns of specific brands of ingredients and set the optimal replenishment timing based on those patterns. This enables more accurate monitoring by performing monitoring according to the type and amount of ingredients used. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input ingredient type and usage data into a generating AI and have the generating AI execute the process of applying the optimal monitoring algorithm.

[0045] The monitoring unit can adjust the timing of its monitoring of food inventory, taking into account the user's eating habits. For example, the monitoring unit can identify the time of day when the user prepares meals and focus its monitoring on food inventory during that time. For example, the monitoring unit can analyze the frequency and timing of the user's meals and adjust the monitoring timing based on that data. Furthermore, if the user frequently eats out, the monitoring unit can reduce the monitoring frequency and focus its monitoring on times when the user is at home. For example, the monitoring unit can consider the user's frequency of eating out and focus its monitoring on food inventory when the user is at home. In addition, the monitoring unit can flexibly adjust the timing of food inventory monitoring to match the user's eating rhythm. For example, the monitoring unit can consider the time and type of meals the user eats and monitor accordingly. By adjusting the monitoring timing to match the user's eating habits, monitoring can be performed at a more appropriate time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's eating habits pattern data into a generating AI and have the generating AI execute the process of determining the optimal monitoring timing.

[0046] The monitoring unit can monitor the inventory of other items in the refrigerator simultaneously when monitoring the inventory of food items. For example, when monitoring the inventory of food items, the monitoring unit can also monitor the inventory of condiments and beverages at the same time and prompt replenishment as needed. For example, the monitoring unit can use a camera that monitors the inventory of food items to simultaneously monitor the remaining amount in condiment and beverage bottles. The monitoring unit can also simultaneously monitor the inventory of consumables in the refrigerator (such as plastic wrap and aluminum foil). For example, the monitoring unit can use a camera installed inside the refrigerator to monitor the inventory of consumables. Furthermore, the monitoring unit can check the maintenance status of the refrigerator at the same time as monitoring the inventory of food items and suggest necessary maintenance. For example, the monitoring unit can monitor the degree of dirtiness of the refrigerator filter and notify the user of necessary maintenance. This allows for more efficient housework by simultaneously monitoring the inventory of other items in the refrigerator. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input inventory data of other items in the refrigerator into a generating AI and have the generating AI execute a process to determine the optimal monitoring timing.

[0047] The notification unit can analyze past notification history and select an appropriate notification method. For example, the notification unit can select the notification method that the user is most likely to respond to from past notification history. For example, the notification unit can analyze past notification history and select the most effective notification method. The notification unit can also prioritize notification methods that the user has preferred to use in the past (email, push notifications, etc.). For example, the notification unit can select the optimal notification method based on the notification methods the user has preferred to use in the past. Furthermore, the notification unit can analyze past notification history and set the most effective notification timing. For example, the notification unit can set the optimal notification timing based on past notification history. In this way, the optimal notification method can be selected by analyzing past notification history. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0048] The notification unit can select different notification formats according to the user's preferences when sending notifications. For example, if the user prefers visual information, the notification unit can send notifications with images. The notification unit can also send notifications as voice messages if the user prefers voice notifications. Furthermore, if the user prefers text notifications, the notification unit can send notifications as concise text messages. By selecting a notification format that suits the user's preferences, it becomes possible to provide more satisfying notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user preference data into a generating AI and have the generating AI perform the process of selecting the optimal notification format.

[0049] The notification unit can adjust the timing of notifications by considering the user's lifestyle patterns. For example, the notification unit can identify the time of day when the user prepares meals and focus notifications during that time. For example, the notification unit can analyze the frequency and timing of the user's meals and adjust the notification timing based on that data. The notification unit can also refrain from sending notifications when the user is out and focus notifications when the user is at home. For example, the notification unit can consider the user's time out and send notifications when the user is at home. Furthermore, the notification unit can flexibly adjust the timing of notifications to match the user's lifestyle rhythm. For example, the notification unit can consider the user's wake-up and bedtime and send notifications accordingly. By adjusting the notification timing to match the user's lifestyle patterns, notifications can be sent at more appropriate times. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's lifestyle pattern data into a generating AI and have the generating AI execute the process of determining the optimal notification timing.

[0050] The notification unit can also notify users of shortages of other necessary household items at the same time as sending a notification. For example, the notification unit can notify users of shortages of condiments and beverages at the same time as notifying them of shortages of ingredients. For example, the notification unit can use a camera that monitors the inventory of ingredients to simultaneously monitor the remaining amount of condiments and beverage bottles and prompt replenishment as needed. The notification unit can also notify users of shortages of consumables in the refrigerator (such as plastic wrap and aluminum foil). For example, the notification unit can use a camera installed inside the refrigerator to monitor the inventory of consumables and prompt replenishment as needed. Furthermore, the notification unit can notify users of the maintenance status of the refrigerator at the same time as notifying them of shortages of ingredients and suggest necessary maintenance. For example, the notification unit can monitor the cleanliness of the refrigerator filter and notify users of necessary maintenance. This allows for more efficient housework by simultaneously notifying users of shortages of other necessary household items. Some or all of the above processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input inventory data of other household items into a generating AI and have the generating AI perform the process of determining the optimal notification timing.

[0051] The notification unit can analyze past link provision history and select appropriate link destinations. For example, the notification unit can prioritize providing links to e-commerce sites that users have used most frequently based on past link provision history. For example, the notification unit can analyze past link provision history and select the most effective link destination. The notification unit can also prioritize providing links to e-commerce sites that users have previously given high ratings to. For example, the notification unit can prioritize providing links to e-commerce sites that users have previously given high ratings to. Furthermore, the notification unit can analyze past link provision history and set the most effective link provision timing. For example, the notification unit can set the optimal link provision timing based on past link provision history. This allows the optimal link destination to be selected by analyzing past link provision history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past link provision history data into a generating AI and have the generating AI perform the process of selecting the optimal link destination.

[0052] The notification unit can select links to different e-commerce sites based on the user's preferences when providing links. For example, if a user prefers a specific brand of detergent, the notification unit can provide a link to an e-commerce site that carries that brand. For instance, the notification unit can search for e-commerce sites that carry the detergent brand specified by the user and provide the most suitable link. Furthermore, if the user prioritizes price, the notification unit can provide a link to the cheapest e-commerce site. For example, the notification unit can compare prices across multiple e-commerce sites and provide the cheapest link. Additionally, if the user prioritizes delivery speed, the notification unit can provide a link to the e-commerce site with the fastest delivery. For example, the notification unit can compare delivery options across multiple e-commerce sites and provide the link with the fastest delivery. This allows for more satisfying link provision by selecting e-commerce site links that match the user's preferences. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user preference data into a generating AI and have the generating AI perform the process of selecting the most suitable link.

[0053] The notification unit can adjust the timing of link delivery by considering the user's lifestyle patterns. For example, the notification unit can identify the time of day when the user prepares meals and focus on providing links during that time. For example, the notification unit can analyze the frequency and timing of the user's meals and adjust the timing of link delivery based on that data. The notification unit can also refrain from providing links when the user is out and focus on providing links when the user is at home. For example, the notification unit can consider the user's time out and provide links when the user is at home. Furthermore, the notification unit can flexibly adjust the timing of link delivery to match the user's lifestyle rhythm. For example, the notification unit can consider the user's wake-up and bedtime and provide links according to those times. By adjusting the timing of link delivery to match the user's lifestyle patterns, links can be provided at a more appropriate time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's lifestyle pattern data into a generating AI and have the generating AI execute the process of determining the optimal timing for providing links.

[0054] The notification unit can provide links to other related products simultaneously when providing links. For example, when providing a link to detergent, the notification unit can also provide links to related products such as fabric softener and bleach. For example, when providing a link to detergent, the notification unit can check the inventory of related products and provide links to the necessary products. The notification unit can also provide links to seasonings and beverages simultaneously with links to food ingredients. For example, when providing a link to food ingredients, the notification unit can check the inventory of seasonings and beverages and provide links to the necessary products. Furthermore, the notification unit can also provide links to consumables in the refrigerator (such as plastic wrap and aluminum foil) simultaneously. For example, the notification unit can check the inventory of consumables in the refrigerator and provide links to the necessary products. By providing links to other related products simultaneously, household chores can be performed more efficiently. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input inventory data for other related products into a generating AI and have the generating AI perform the process of determining the optimal timing for providing links.

[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 monitoring unit can learn the user's lifestyle patterns and automatically adjust the optimal monitoring timing. For example, it can analyze how often and when the user does laundry and adjust the timing of monitoring the remaining amount of detergent based on that data. It can also analyze how often and when the user opens the refrigerator and adjust the timing of monitoring food inventory based on that data. Furthermore, the monitoring unit can flexibly adjust the frequency and timing of monitoring to match the user's daily rhythm. This enables optimal monitoring tailored to the user's lifestyle patterns, supporting household chores more efficiently.

[0057] The ordering department can analyze a user's purchase history and suggest the optimal timing for ordering. For example, it can analyze the frequency of use and consumption rate of products a user has purchased in the past and suggest the optimal timing for ordering based on that data. It can also analyze a user's tendency to purchase specific products and suggest the timing for ordering related products based on that data. Furthermore, based on the user's purchase history, the ordering department can suggest ordering timing tailored to specific seasons or events. This enables efficient household support by suggesting the optimal timing for ordering based on the user's purchase history.

[0058] The monitoring unit can collect user health data and provide household support tailored to their health condition. For example, it can collect user heart rate and sleep data and adjust the priority of household chores according to their health status. It can also collect user dietary data and suggest ingredients that consider nutritional balance. Furthermore, it can collect user exercise data and suggest household chores according to their exercise level. This enables optimal household support tailored to the user's health condition.

[0059] The monitoring unit can adjust the priority of household chores, taking into account the user's family structure. For example, it can adjust the frequency of laundry and grocery inventory management according to the number and ages of family members. It can also suggest groceries, taking into account the family's health status and allergy information. Furthermore, it can adjust the timing of household chores, taking into account the family's schedule. This enables optimal household support tailored to the family's structure.

[0060] The monitoring unit can collect user energy consumption data and provide household support that takes energy efficiency into consideration. For example, the monitoring unit can collect user electricity consumption data and suggest the use of energy-efficient appliances. It can also collect user water usage data and suggest household chores that take water conservation into consideration. Furthermore, the monitoring unit can collect user gas usage data and suggest household chores that take gas conservation into consideration. This enables optimal household support that takes energy efficiency into account.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The monitoring unit monitors the remaining amount of detergent. For example, it uses a high-resolution camera to accurately detect the amount of detergent remaining in the bottle. The monitoring unit also monitors the inventory of food items inside the refrigerator. For example, it uses a camera with a wide-angle lens to monitor the entire inside of the refrigerator. Step 2: The ordering unit automatically places an order when the remaining amount of detergent, monitored by the monitoring unit, falls below a certain level. For example, it accesses an e-commerce site pre-configured by the user and automatically orders the necessary products. Step 3: The notification unit informs the user of any missing ingredients based on the inventory in the refrigerator monitored by the monitoring unit. For example, it sends a notification to the user via a smartphone app and provides a link to an e-commerce site.

[0063] (Example of form 2) The household assistance system according to an embodiment of the present invention is a system designed to solve the problem of not having enough time for housework or the house quickly becoming dirty due to laziness. This household assistance system uses IoT (Vision AI) equipped cameras permanently installed in front of the washing machine, in the living room, inside the refrigerator, etc., to assist with all household chores. This system has a function to automatically order detergent from e-commerce when it runs out, a function to constantly monitor the ingredients in the refrigerator and notify the user of any missing ingredients, and a function to tell the AI ​​what ingredients are missing from the refrigerator when the user sends a message to the AI ​​via LINE or a dedicated app saying, "I'm making curry today." As a result, the user can perform accurate actions with fewer instructions, significantly reducing the burden of housework. Furthermore, convenience is improved by utilizing the LINE ecosystem and the Yahoo ecosystem. For example, let's explain the automatic detergent ordering function. In this function, the camera monitors the remaining amount of detergent and automatically places an order from an e-commerce site when it falls below a certain amount. Next, let's explain the function that notifies the user of any missing ingredients. In this function, the camera inside the refrigerator monitors the inventory of ingredients and notifies the user of any missing ingredients when the user gives the instruction, "I'm making curry." As a result, the user can easily find out what ingredients they need and purchase them. Furthermore, these functions work together to enable users to perform precise actions with fewer instructions. For example, the automatic detergent ordering function and the function that notifies the user of ingredient shortages work together, significantly reducing the burden of housework for the user. It is also clear how each function provides information to the user. For example, the notification unit not only notifies the user of missing ingredients but also provides a link to an e-commerce site, allowing the user to easily place an order. In this way, the housework assistance system reduces the burden on the user and allows them to perform housework efficiently.

[0064] The household assistance system according to this embodiment comprises a monitoring unit, an ordering unit, and a notification unit. The monitoring unit monitors the remaining amount of detergent. The monitoring unit monitors the remaining amount of detergent using, for example, a camera. The camera is, for example, a high-resolution camera that can accurately detect the remaining amount in the detergent bottle. The monitoring unit also monitors the inventory of food items in the refrigerator. The monitoring unit monitors the inventory of food items using, for example, a camera installed inside the refrigerator. The camera is, for example, a camera equipped with a wide-angle lens that can monitor the entire inside of the refrigerator. The ordering unit automatically places an order when the remaining amount monitored by the monitoring unit falls below a certain amount. The ordering unit automatically places an order from, for example, an e-commerce site. The ordering unit can, for example, access an e-commerce site pre-set by the user and automatically order the necessary products. The notification unit informs the user of any missing food items based on the inventory monitored by the monitoring unit. The notification unit notifies the user of any missing food items. The notification unit can, for example, send a notification to the user through a smartphone app. The notification unit also provides a link to the e-commerce site. The notification unit, for example, provides a link to an e-commerce site simultaneously with the notification, allowing the user to easily place an order. This enables the household assistance system according to the embodiment to reduce the user's burden and perform household chores efficiently.

[0065] The monitoring unit can monitor the remaining amount of detergent using a camera. The camera may include, for example, a high-resolution camera. The camera can accurately detect the remaining amount in the detergent bottle. For example, the camera can read the label on the detergent bottle and calculate the remaining amount. The camera can also measure the height of the detergent bottle and estimate the remaining amount. Furthermore, the camera can detect changes in the color of the detergent bottle and determine the remaining amount. In this way, the remaining amount of detergent can be accurately monitored using the camera. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input image data acquired by the camera into a generating AI and have the generating AI perform the process of estimating the remaining amount of detergent from the image data.

[0066] The ordering unit can automatically place orders from e-commerce sites. For example, the ordering unit can access e-commerce sites pre-configured by the user and automatically order the necessary products. For instance, the ordering unit can log in using the user's e-commerce site account information, add products to the cart, and confirm the order. The ordering unit can also automatically make payments using the payment method set by the user. Furthermore, the ordering unit can save the order history and refer to it for future orders. This reduces the user's effort by allowing them to place orders automatically from e-commerce sites. Some or all of the above processes in the ordering unit may be performed using AI, for example, or not. For example, the ordering unit can input the user's order history into a generating AI and have the generating AI perform the process of selecting the optimal supplier.

[0067] The monitoring unit can monitor the inventory of food items inside the refrigerator using cameras. These cameras may include, for example, cameras equipped with wide-angle lenses. The cameras can monitor the entire interior of the refrigerator. For example, the cameras can photograph food items on each shelf to check inventory. The cameras can also read food labels and manage inventory. Furthermore, the cameras can detect changes in food color to determine freshness. This allows for accurate monitoring of food inventory within the refrigerator using cameras. Some or all of the above-described processes in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input image data acquired by the cameras into a generating AI, which can then perform the process of estimating food inventory from the image data.

[0068] The notification unit can notify the user of missing ingredients. The notification unit can send notifications to the user, for example, through a smartphone app. For example, the notification unit can send a push notification to an app set by the user to inform them of missing ingredients. The notification unit can also send notifications to the user via email. For example, the notification unit can send a list of missing ingredients to an email address set by the user. Furthermore, the notification unit can also notify the user through a voice assistant. For example, the notification unit can have the voice assistant read out the missing ingredients. This makes it easy for the user to know what ingredients they need by notifying them of missing ingredients. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the user's notification history into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0069] The notification unit can provide links to e-commerce sites. For example, the notification unit can provide a link to an e-commerce site simultaneously with a notification, allowing users to easily place orders. For example, the notification unit can send links to users via a smartphone app. The notification unit can also send links to users via email. For example, the notification unit can send a link to an e-commerce site to an email address set by the user. Furthermore, the notification unit can provide links to users via a voice assistant. For example, the notification unit can have a voice assistant read out the link. This allows users to easily place orders by providing links to e-commerce sites. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's link provision history into a generating AI and have the generating AI perform the process of selecting the optimal link destination.

[0070] The monitoring unit can estimate the user's emotions and adjust the frequency of detergent level monitoring based on the estimated emotions. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions. The monitoring unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also 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 monitoring unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate timing of notifications by adjusting the monitoring frequency according to 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 in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0071] The monitoring unit can analyze the detergent usage history and determine the optimal monitoring timing. For example, the monitoring unit can analyze detergent consumption patterns from past usage history and set the optimal monitoring timing. For example, the monitoring unit can identify detergent consumption patterns based on past usage frequency and amount, and adjust the monitoring timing based on those patterns. The monitoring unit can also identify time periods when detergent is used frequently and focus monitoring during those times. For example, the monitoring unit will focus monitoring the remaining amount of detergent during time periods when laundry is frequently performed. Furthermore, if detergent usage suddenly increases, the monitoring unit can temporarily increase the monitoring frequency to prompt early replenishment. For example, if detergent usage suddenly increases, the monitoring unit will notify the user to replenish early. This allows monitoring to be performed at the optimal timing by analyzing the usage history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input detergent usage history data into a generating AI and have the generating AI execute the process of determining the optimal monitoring timing.

[0072] The monitoring unit can apply different monitoring algorithms depending on the type and amount of detergent used. For example, the monitoring unit can apply different monitoring algorithms to liquid detergents and powder detergents, performing monitoring according to their respective characteristics. For example, in the case of liquid detergents, the monitoring unit measures the remaining amount in the bottle, and in the case of powder detergents, it measures the weight of the container. The monitoring unit can also monitor detergents used in large quantities more frequently and prompt for earlier replenishment. For example, the monitoring unit can focus on monitoring the remaining amount of detergents used in large quantities and notify the user to replenish early. Furthermore, the monitoring unit can apply a dedicated monitoring algorithm to specific brands of detergents and set the optimal replenishment timing. For example, the monitoring unit can analyze the usage patterns of a specific brand of detergent and set the optimal replenishment timing based on those patterns. This enables more accurate monitoring by performing monitoring according to the type and amount of detergent used. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input detergent type and usage data into a generating AI and have the generating AI execute the process of applying the optimal monitoring algorithm.

[0073] The monitoring unit can estimate the user's emotions and determine the priority of detergent level monitoring based on the estimated emotions. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions. The monitoring unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also 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 monitoring unit can calculate an emotion score based on fluctuations in heart rate. This allows monitoring to be performed at a more appropriate time by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0074] The monitoring unit can adjust the timing of detergent level monitoring by considering the user's lifestyle patterns. For example, the monitoring unit can identify the time of day when the user does laundry and focus on monitoring the detergent level during that time. For example, the monitoring unit can analyze the user's laundry frequency and timing and adjust the monitoring timing based on that data. The monitoring unit can also reduce the monitoring frequency when the user is out and focus on monitoring when the user is at home. For example, the monitoring unit can consider the user's time out and focus on monitoring the detergent level when the user is at home. Furthermore, the monitoring unit can flexibly adjust the timing of detergent level monitoring to match the user's lifestyle rhythm. For example, the monitoring unit can consider the user's wake-up and bedtime and monitor accordingly. By adjusting the monitoring timing to match the user's lifestyle patterns, monitoring can be performed at a more appropriate time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user lifestyle pattern data into a generating AI and have the generating AI execute a process to determine the optimal monitoring timing.

[0075] The monitoring unit can simultaneously monitor the remaining amount of other household items when monitoring the remaining amount of detergent. For example, when monitoring the remaining amount of detergent, the monitoring unit can also simultaneously monitor the remaining amounts of fabric softener and bleach and prompt for replenishment as needed. For example, the monitoring unit can use a camera that monitors the remaining amount of detergent to simultaneously monitor the remaining amounts of fabric softener and bleach bottles. The monitoring unit can also simultaneously monitor the remaining amounts of other consumables around the washing machine (laundry nets, clothespins, etc.). For example, the monitoring unit can use cameras installed around the washing machine to monitor the remaining amounts of other consumables. Furthermore, the monitoring unit can check the maintenance status of the washing machine at the same time as monitoring the remaining amount of detergent and suggest necessary maintenance. For example, the monitoring unit can monitor the degree of soiling of the washing machine filter and notify the user of necessary maintenance. This allows for more efficient housework by simultaneously monitoring the remaining amounts of other household items. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input remaining quantity data of other household items into the generating AI, and have the generating AI perform the process of determining the optimal monitoring timing.

[0076] The ordering system can estimate the user's emotions and adjust the timing of orders based on those emotions. For example, the ordering system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the ordering system can calculate an emotion score based on changes in facial expressions. The ordering system can also record the user's voice and estimate their emotions using voice analysis technology. For example, the ordering system can analyze the tone and speed of the voice and calculate an emotion score. The ordering system can also 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 ordering system can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate timing of orders by adjusting the timing according to 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 in the order department may be performed using AI, for example, or without AI. For example, the order department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0077] The ordering department can analyze past order history and select the optimal supplier. For example, the ordering department can select the most reliable supplier based on past order history. For example, the ordering department can analyze past order history and select the supplier that delivers the fastest. The ordering department can also prioritize suppliers that have received high ratings in the past, according to the user's preferences. For example, the ordering department can prioritize suppliers that users have given high ratings to in the past. Furthermore, the ordering department can analyze past order history and select the supplier with the best cost performance. For example, the ordering department can select the cheapest and highest quality supplier based on past order history. In this way, the optimal supplier can be selected by analyzing past order history. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input past order history data into a generating AI and have the generating AI perform the process of selecting the optimal supplier.

[0078] The ordering system allows users to select different e-commerce sites based on their preferences when placing an order. For example, if a user prefers a specific brand of detergent, the ordering system will prioritize e-commerce sites that carry that brand. For instance, the ordering system will search for e-commerce sites that carry the brand of detergent specified by the user and select the most suitable site. Furthermore, if price is a priority, the ordering system can select the cheapest e-commerce site. For example, the ordering system will compare prices across multiple e-commerce sites and select the cheapest one. Additionally, if delivery speed is a priority, the ordering system can select the site with the fastest delivery. For example, the ordering system will compare delivery options across multiple e-commerce sites and select the site with the fastest delivery. This allows for more satisfying orders by selecting e-commerce sites that match the user's preferences. Some or all of the above processes in the ordering system may be performed using AI, or not. For example, the ordering system can input user preference data into a generating AI and have the AI ​​perform the process of selecting the most suitable e-commerce site.

[0079] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, the ordering system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For instance, it can calculate an emotion score based on changes in facial expressions. The ordering system can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the ordering system 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, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate timing in order prioritizing orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 order department may be performed using AI, for example, or without AI. For example, the order department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0080] The ordering system can select an appropriate delivery method when an order is placed, taking into account the user's geographical location. For example, if the user is at home, the ordering system will select a standard delivery method. For example, the ordering system will select the optimal delivery method based on the user's geographical location. The ordering system can also select pickup at the nearest convenience store if the user is out. For example, the ordering system will select the nearest pickup location based on the user's geographical location. Furthermore, if the user is traveling, the ordering system can select a delivery method to their travel destination address. For example, the ordering system will select a delivery method to their travel destination address based on the user's geographical location. In this way, the system can select the optimal delivery method by taking into account the user's geographical location. Some or all of the above processing in the ordering system may be performed using AI, for example, or without AI. For example, the ordering system can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting the optimal delivery method.

[0081] The ordering system allows users to order other necessary household items at the same time as placing an order. For example, when ordering detergent, the ordering system can also order related items such as fabric softener and bleach. For example, the ordering system can check the inventory of related items and automatically order the necessary items at the same time as ordering detergent. The ordering system can also order consumables for the washing machine (such as laundry nets and clothespins) at the same time. For example, the ordering system can check the inventory of consumables for the washing machine and automatically order the necessary items. Furthermore, the ordering system can automatically detect household items that the user has previously ordered and are running low on stock, and order them simultaneously. For example, based on the user's order history, the ordering system can detect items that are running low on stock and automatically order the necessary items. This allows for more efficient household chores by ordering other necessary household items at the same time. Some or all of the above processes in the ordering system may be performed using AI, or not. For example, the ordering system can input inventory data for other household items into a generating AI and have the generating AI perform the process of determining the optimal ordering timing.

[0082] The monitoring unit can estimate the user's emotions and adjust the frequency of monitoring food inventory based on the estimated emotions. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions. The monitoring unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also 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 monitoring unit can calculate an emotion score based on fluctuations in heart rate. This allows for notifications to be sent at a more appropriate time by adjusting the monitoring frequency according to 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 in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0083] The monitoring unit can analyze the consumption history of ingredients and determine the appropriate monitoring timing. For example, the monitoring unit can analyze the consumption patterns of ingredients from past consumption history and set the optimal monitoring timing. For example, the monitoring unit can identify the consumption patterns of ingredients based on past consumption frequency and quantity, and adjust the monitoring timing based on those patterns. The monitoring unit can also identify time periods when ingredients are consumed frequently and focus monitoring during those times. For example, the monitoring unit focuses monitoring during time periods when ingredients are consumed frequently. Furthermore, if the consumption of ingredients suddenly increases, the monitoring unit can temporarily increase the monitoring frequency to encourage early replenishment. For example, when the consumption of ingredients suddenly increases, the monitoring unit will notify the user to replenish early. This allows monitoring to be performed at the optimal timing by analyzing the consumption history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the consumption history data of ingredients into a generating AI and have the generating AI execute the process of determining the optimal monitoring timing.

[0084] The monitoring unit can apply different monitoring algorithms depending on the type and amount of ingredients used. For example, the monitoring unit can apply different monitoring algorithms to vegetables and meats, performing monitoring according to their respective characteristics. For instance, the monitoring unit prioritizes freshness monitoring for vegetables and expiration date monitoring for meats. The monitoring unit can also monitor ingredients used in large quantities more frequently and prompt earlier replenishment. For example, the monitoring unit can focus on monitoring the inventory of ingredients used in large quantities and notify early replenishment. Furthermore, the monitoring unit can apply a dedicated monitoring algorithm to specific brands of ingredients and set the optimal replenishment timing. For example, the monitoring unit can analyze the usage patterns of specific brands of ingredients and set the optimal replenishment timing based on those patterns. This enables more accurate monitoring by performing monitoring according to the type and amount of ingredients used. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input ingredient type and usage data into a generating AI and have the generating AI execute the process of applying the optimal monitoring algorithm.

[0085] The monitoring unit can estimate the user's emotions and determine the priority of monitoring food inventory based on the estimated emotions. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions. The monitoring unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also 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 monitoring unit can calculate an emotion score based on fluctuations in heart rate. This allows monitoring to be performed at a more appropriate time by determining priorities according to 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0086] The monitoring unit can adjust the timing of its monitoring of food inventory, taking into account the user's eating habits. For example, the monitoring unit can identify the time of day when the user prepares meals and focus its monitoring on food inventory during that time. For example, the monitoring unit can analyze the frequency and timing of the user's meals and adjust the monitoring timing based on that data. Furthermore, if the user frequently eats out, the monitoring unit can reduce the monitoring frequency and focus its monitoring on times when the user is at home. For example, the monitoring unit can consider the user's frequency of eating out and focus its monitoring on food inventory when the user is at home. In addition, the monitoring unit can flexibly adjust the timing of food inventory monitoring to match the user's eating rhythm. For example, the monitoring unit can consider the time and type of meals the user eats and monitor accordingly. By adjusting the monitoring timing to match the user's eating habits, monitoring can be performed at a more appropriate time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's eating habits pattern data into a generating AI and have the generating AI execute the process of determining the optimal monitoring timing.

[0087] The monitoring unit can monitor the inventory of other items in the refrigerator simultaneously when monitoring the inventory of food items. For example, when monitoring the inventory of food items, the monitoring unit can also monitor the inventory of condiments and beverages at the same time and prompt replenishment as needed. For example, the monitoring unit can use a camera that monitors the inventory of food items to simultaneously monitor the remaining amount in condiment and beverage bottles. The monitoring unit can also simultaneously monitor the inventory of consumables in the refrigerator (such as plastic wrap and aluminum foil). For example, the monitoring unit can use a camera installed inside the refrigerator to monitor the inventory of consumables. Furthermore, the monitoring unit can check the maintenance status of the refrigerator at the same time as monitoring the inventory of food items and suggest necessary maintenance. For example, the monitoring unit can monitor the degree of dirtiness of the refrigerator filter and notify the user of necessary maintenance. This allows for more efficient housework by simultaneously monitoring the inventory of other items in the refrigerator. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input inventory data of other items in the refrigerator into a generating AI and have the generating AI execute a process to determine the optimal monitoring timing.

[0088] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on changes in facial expressions. The notification unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. The notification unit can also 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 notification unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate timing of notifications by adjusting the timing according to 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0089] The notification unit can analyze past notification history and select an appropriate notification method. For example, the notification unit can select the notification method that the user is most likely to respond to from past notification history. For example, the notification unit can analyze past notification history and select the most effective notification method. The notification unit can also prioritize notification methods that the user has preferred to use in the past (email, push notifications, etc.). For example, the notification unit can select the optimal notification method based on the notification methods the user has preferred to use in the past. Furthermore, the notification unit can analyze past notification history and set the most effective notification timing. For example, the notification unit can set the optimal notification timing based on past notification history. In this way, the optimal notification method can be selected by analyzing past notification history. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0090] The notification unit can select different notification formats according to the user's preferences when sending notifications. For example, if the user prefers visual information, the notification unit can send notifications with images. The notification unit can also send notifications as voice messages if the user prefers voice notifications. Furthermore, if the user prefers text notifications, the notification unit can send notifications as concise text messages. By selecting a notification format that suits the user's preferences, it becomes possible to provide more satisfying notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user preference data into a generating AI and have the generating AI perform the process of selecting the optimal notification format.

[0091] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on changes in facial expressions. The notification unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. The notification unit can also 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 notification unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate timing of notifications by determining notification priorities according to 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0092] The notification unit can adjust the timing of notifications by considering the user's lifestyle patterns. For example, the notification unit can identify the time of day when the user prepares meals and focus notifications during that time. For example, the notification unit can analyze the frequency and timing of the user's meals and adjust the notification timing based on that data. The notification unit can also refrain from sending notifications when the user is out and focus notifications when the user is at home. For example, the notification unit can consider the user's time out and send notifications when the user is at home. Furthermore, the notification unit can flexibly adjust the timing of notifications to match the user's lifestyle rhythm. For example, the notification unit can consider the user's wake-up and bedtime and send notifications accordingly. By adjusting the notification timing to match the user's lifestyle patterns, notifications can be sent at more appropriate times. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's lifestyle pattern data into a generating AI and have the generating AI execute the process of determining the optimal notification timing.

[0093] The notification unit can also notify users of shortages of other necessary household items at the same time as sending a notification. For example, the notification unit can notify users of shortages of condiments and beverages at the same time as notifying them of shortages of ingredients. For example, the notification unit can use a camera that monitors the inventory of ingredients to simultaneously monitor the remaining amount of condiments and beverage bottles and prompt replenishment as needed. The notification unit can also notify users of shortages of consumables in the refrigerator (such as plastic wrap and aluminum foil). For example, the notification unit can use a camera installed inside the refrigerator to monitor the inventory of consumables and prompt replenishment as needed. Furthermore, the notification unit can notify users of the maintenance status of the refrigerator at the same time as notifying them of shortages of ingredients and suggest necessary maintenance. For example, the notification unit can monitor the cleanliness of the refrigerator filter and notify users of necessary maintenance. This allows for more efficient housework by simultaneously notifying users of shortages of other necessary household items. Some or all of the above processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input inventory data of other household items into a generating AI and have the generating AI perform the process of determining the optimal notification timing.

[0094] The notification unit can estimate the user's emotions and adjust the timing of link provision based on the estimated emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on changes in facial expressions. The notification unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. The notification unit can also 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 notification unit can calculate an emotion score based on fluctuations in heart rate. This allows the timing of link provision to be adjusted according to the user's emotions, enabling the provision of links at a more appropriate time. 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0095] The notification unit can analyze past link provision history and select appropriate link destinations. For example, the notification unit can prioritize providing links to e-commerce sites that users have used most frequently based on past link provision history. For example, the notification unit can analyze past link provision history and select the most effective link destination. The notification unit can also prioritize providing links to e-commerce sites that users have previously given high ratings to. For example, the notification unit can prioritize providing links to e-commerce sites that users have previously given high ratings to. Furthermore, the notification unit can analyze past link provision history and set the most effective link provision timing. For example, the notification unit can set the optimal link provision timing based on past link provision history. This allows the optimal link destination to be selected by analyzing past link provision history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past link provision history data into a generating AI and have the generating AI perform the process of selecting the optimal link destination.

[0096] The notification unit can select links to different e-commerce sites based on the user's preferences when providing links. For example, if a user prefers a specific brand of detergent, the notification unit can provide a link to an e-commerce site that carries that brand. For instance, the notification unit can search for e-commerce sites that carry the detergent brand specified by the user and provide the most suitable link. Furthermore, if the user prioritizes price, the notification unit can provide a link to the cheapest e-commerce site. For example, the notification unit can compare prices across multiple e-commerce sites and provide the cheapest link. Additionally, if the user prioritizes delivery speed, the notification unit can provide a link to the e-commerce site with the fastest delivery. For example, the notification unit can compare delivery options across multiple e-commerce sites and provide the link with the fastest delivery. This allows for more satisfying link provision by selecting e-commerce site links that match the user's preferences. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user preference data into a generating AI and have the generating AI perform the process of selecting the most suitable link.

[0097] The notification unit can estimate the user's emotions and determine the priority of link provision based on the estimated user emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on changes in facial expressions. The notification unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. The notification unit can also 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 notification unit can calculate an emotion score based on fluctuations in heart rate. This allows the system to prioritize link provision according to the user's emotions, enabling the provision of links at a more appropriate time. 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0098] The notification unit can adjust the timing of link delivery by considering the user's lifestyle patterns. For example, the notification unit can identify the time of day when the user prepares meals and focus on providing links during that time. For example, the notification unit can analyze the frequency and timing of the user's meals and adjust the timing of link delivery based on that data. The notification unit can also refrain from providing links when the user is out and focus on providing links when the user is at home. For example, the notification unit can consider the user's time out and provide links when the user is at home. Furthermore, the notification unit can flexibly adjust the timing of link delivery to match the user's lifestyle rhythm. For example, the notification unit can consider the user's wake-up and bedtime and provide links according to those times. By adjusting the timing of link delivery to match the user's lifestyle patterns, links can be provided at a more appropriate time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's lifestyle pattern data into a generating AI and have the generating AI execute the process of determining the optimal timing for providing links.

[0099] The notification unit can provide links to other related products simultaneously when providing links. For example, when providing a link to detergent, the notification unit can also provide links to related products such as fabric softener and bleach. For example, when providing a link to detergent, the notification unit can check the inventory of related products and provide links to the necessary products. The notification unit can also provide links to seasonings and beverages simultaneously with links to food ingredients. For example, when providing a link to food ingredients, the notification unit can check the inventory of seasonings and beverages and provide links to the necessary products. Furthermore, the notification unit can also provide links to consumables in the refrigerator (such as plastic wrap and aluminum foil) simultaneously. For example, the notification unit can check the inventory of consumables in the refrigerator and provide links to the necessary products. By providing links to other related products simultaneously, household chores can be performed more efficiently. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input inventory data for other related products into a generating AI and have the generating AI perform the process of determining the optimal timing for providing links. === Hard Collateral 1-1 === Each of the multiple elements described above, including the monitoring unit, ordering unit, and notification unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the smart device 14 to monitor the remaining amount of detergent and the inventory of food items in the refrigerator. The ordering unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and automatically places orders from the e-commerce site based on the data from the monitoring unit. The notification unit is implemented, for example, by the control unit 46A of the smart device 14, and notifies the user of any missing food items and provides a link to the e-commerce site. === Hard Collateral 1-2 === Each of the multiple elements described above, including the monitoring unit, ordering unit, and notification unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the smart glasses 214 to monitor the remaining amount of detergent and the inventory of food items in the refrigerator. The ordering unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and automatically places orders from the e-commerce site based on the data from the monitoring unit. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214, and notifies the user of any missing food items and provides a link to the e-commerce site. === Hard Collateral 1-3 === Each of the multiple elements described above, including the monitoring unit, ordering unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the headset terminal 314 to monitor the remaining amount of detergent and the inventory of food items in the refrigerator. The ordering unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which automatically places orders from the e-commerce site based on the data from the monitoring unit. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314, which notifies the user of any missing food items and provides a link to the e-commerce site. === Hard Collateral 1-4 === Each of the multiple elements described above, including the monitoring unit, ordering unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the robot 414 to monitor the remaining amount of detergent and the inventory of food items in the refrigerator. The ordering unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which automatically places orders from the e-commerce site based on the data from the monitoring unit. The notification unit is implemented by, for example, the control unit 46A of the robot 414, which notifies the user of any missing food items and provides a link to the e-commerce site.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The monitoring unit can learn the user's lifestyle patterns and automatically adjust the optimal monitoring timing. For example, it can analyze how often and when the user does laundry and adjust the timing of monitoring the remaining amount of detergent based on that data. It can also analyze how often and when the user opens the refrigerator and adjust the timing of monitoring food inventory based on that data. Furthermore, the monitoring unit can flexibly adjust the frequency and timing of monitoring to match the user's daily rhythm. This enables optimal monitoring tailored to the user's lifestyle patterns, supporting household chores more efficiently.

[0102] The ordering department can analyze a user's purchase history and suggest the optimal timing for ordering. For example, it can analyze the frequency of use and consumption rate of products a user has purchased in the past and suggest the optimal timing for ordering based on that data. It can also analyze a user's tendency to purchase specific products and suggest the timing for ordering related products based on that data. Furthermore, based on the user's purchase history, the ordering department can suggest ordering timing tailored to specific seasons or events. This enables efficient household support by suggesting the optimal timing for ordering based on the user's purchase history.

[0103] The notification unit can estimate the user's emotions and customize notification content based on those emotions. For example, if the user is feeling stressed, the notification unit will send a concise and easy-to-understand notification. If the user is relaxed, it can send a notification with more detailed information. Furthermore, if the user is excited, the notification unit can send a notification with a positive message. This allows for more effective household support by providing optimal notification content tailored to the user's emotions.

[0104] The monitoring unit can collect user health data and provide household support tailored to their health condition. For example, it can collect user heart rate and sleep data and adjust the priority of household chores according to their health status. It can also collect user dietary data and suggest ingredients that consider nutritional balance. Furthermore, it can collect user exercise data and suggest household chores according to their exercise level. This enables optimal household support tailored to the user's health condition.

[0105] The ordering system can estimate the user's emotions and customize the order based on those emotions. For example, if the user is feeling stressed, the ordering system can suggest products with relaxing effects. If the user is tired, it can suggest products suitable for replenishing energy. Furthermore, if the user is happy, it can suggest products tailored to a special event. This allows for more satisfying household support by providing optimal orders that match the user's emotions.

[0106] The monitoring unit can adjust the priority of household chores, taking into account the user's family structure. For example, it can adjust the frequency of laundry and grocery inventory management according to the number and ages of family members. It can also suggest groceries, taking into account the family's health status and allergy information. Furthermore, it can adjust the timing of household chores, taking into account the family's schedule. This enables optimal household support tailored to the family's structure.

[0107] The notification unit can estimate the user's emotions and adjust the tone of notifications based on those emotions. For example, if the user is feeling down, the notification unit can send a notification containing an encouraging message. If the user is angry, it can send a notification in a calm tone. Furthermore, if the user is happy, it can send a notification containing a congratulatory message. This allows for more effective household support by providing the optimal notification tone according to the user's emotions.

[0108] The monitoring unit can collect user energy consumption data and provide household support that takes energy efficiency into consideration. For example, the monitoring unit can collect user electricity consumption data and suggest the use of energy-efficient appliances. It can also collect user water usage data and suggest household chores that take water conservation into consideration. Furthermore, the monitoring unit can collect user gas usage data and suggest household chores that take gas conservation into consideration. This enables optimal household support that takes energy efficiency into account.

[0109] The ordering system can estimate the user's emotions and adjust order priorities based on those estimates. For example, if the user is in a hurry, the ordering system will prioritize orders for items that can be delivered quickly. If the user is relaxed, they can place an order according to the regular delivery schedule. Furthermore, if the user is preparing for a specific event, the system can prioritize orders for items related to that event. This allows for more satisfying household support by providing optimal order priorities tailored to the user's emotions.

[0110] The notification unit can estimate the user's emotions and adjust the frequency of notifications based on those emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications. Conversely, if the user is relaxed, it can increase the frequency of notifications. Furthermore, if the user is busy, it can send only important notifications. This allows for more effective household support by providing an optimal notification frequency tailored to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The monitoring unit monitors the remaining amount of detergent. For example, it uses a high-resolution camera to accurately detect the amount of detergent remaining in the bottle. The monitoring unit also monitors the inventory of food items inside the refrigerator. For example, it uses a camera with a wide-angle lens to monitor the entire inside of the refrigerator. Step 2: The ordering unit automatically places an order when the remaining amount of detergent, monitored by the monitoring unit, falls below a certain level. For example, it accesses an e-commerce site pre-configured by the user and automatically orders the necessary products. Step 3: The notification unit informs the user of any missing ingredients based on the inventory in the refrigerator monitored by the monitoring unit. For example, it sends a notification to the user via a smartphone app and provides a link to an e-commerce site.

[0113] 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.

[0114] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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. 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.

[0131] 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.

[0132] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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. 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.

[0147] 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.

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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. 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.

[0164] 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.

[0165] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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. A monitoring unit that monitors the remaining amount of detergent, An ordering unit that automatically places an order when the remaining quantity monitored by the aforementioned monitoring unit falls below a certain amount, The refrigerator monitoring unit monitors the inventory of food items inside the refrigerator, A notification unit that informs of missing ingredients based on the inventory monitored by the aforementioned refrigerator monitoring unit, Equipped with A system characterized by the following features.

2. The aforementioned monitoring unit, A camera is used to monitor the remaining amount of detergent. The system according to feature 1.

3. The aforementioned ordering section is, Orders are automatically placed through the e-commerce site. The system according to feature 1.

4. The aforementioned monitoring unit, Using cameras to monitor the inventory of food items in the refrigerator. The system according to feature 1.

5. The aforementioned notification unit, Notify the user of any missing ingredients. The system according to feature 1.

6. The aforementioned notification unit, Provide links to e-commerce sites The system according to feature 1.

7. The aforementioned monitoring unit, Includes an algorithm that estimates the user's emotions and adjusts the frequency of detergent level monitoring based on the estimated user emotions. The system according to feature 1.

8. The aforementioned monitoring unit, Analyze detergent usage history to determine appropriate monitoring timing. The system according to feature 1.

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