system

Abstract

The system according to this embodiment aims to automatically order necessary products based on household consumption data and deliver them quickly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a selection unit, an order unit, a delivery unit, and an evaluation unit. The collection unit collects consumption data within the household. The analysis unit analyzes the data collected by the collection unit. The selection unit selects products based on the data analyzed by the analysis unit. The order unit automatically orders the products selected by the selection unit. The delivery unit quickly delivers the products ordered by the order unit. The evaluation unit receives user feedback on the products delivered by the delivery unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a system that automatically orders necessary products based on household consumption data and delivers them quickly has not been fully realized, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically order necessary products based on household consumption data and deliver them quickly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a selection unit, an order unit, a delivery unit, and an evaluation unit. The data collection unit collects consumption data within the household. The analysis unit analyzes the data collected by the data collection unit. The selection unit selects products based on the data analyzed by the analysis unit. The order unit automatically orders the products selected by the selection unit. The delivery unit quickly delivers the products ordered by the order unit. The evaluation unit receives user feedback on the products delivered by the delivery unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically order necessary products based on household consumption data and deliver them quickly. [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 AI ​​shopping agent system according to an embodiment of the present invention is an AI service designed for the elderly and busy people. This AI shopping agent system collects household consumption data in real time, analyzes that data with AI to select necessary products, and places orders automatically. Furthermore, it ensures prompt delivery and improves future orders based on user feedback. This mechanism provides peace of mind and convenience to people who have difficulty shopping on their own, improving their quality of life. For example, it collects household consumption data in real time. For example, it monitors the inventory status of refrigerators and pantries with sensors and records the products that have been consumed. This data is input into the AI, which analyzes consumption patterns. For example, it can determine the amount of milk consumed each week and predict the timing of the next order. Next, the AI ​​selects the optimal products based on the user's preferences and budget. For example, for a user who prefers a particular brand of milk, it will prioritize selecting products of that brand. It also selects the most cost-effective products within the budget. Furthermore, the AI ​​automatically places orders before necessary products run out. For example, when the milk inventory is low, it automatically places the next order. At this time, it selects the optimal delivery company to ensure prompt delivery. Once an order is complete, the user receives a notification and can review the order details. After the product arrives, the user evaluates it, and the AI ​​learns from this feedback to improve future orders. For example, if delivery is delayed or the product does not meet expectations, the AI ​​uses this feedback to improve future orders. This system provides peace of mind and convenience to those who have difficulty shopping on their own, improving their quality of life. For instance, the elderly and busy individuals can avoid the hassle of shopping and always have necessary items on hand. Furthermore, because the AI ​​selects the most suitable products according to the user's preferences and budget, a highly satisfying shopping experience is provided. In this way, the AI ​​shopping agent system can improve user convenience.

[0029] The AI ​​shopping agent system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, an order unit, a delivery unit, and an evaluation unit. The collection unit collects consumption data within the household. The collection unit monitors the inventory status of refrigerators and pantries using sensors, for example, and records the consumed products. The collection unit can monitor the inventory status of food in real time using sensors in the refrigerator, for example. The collection unit can also monitor the inventory status of daily necessities using sensors in the pantry. The collection unit can record the amount of food consumed using sensors in the refrigerator, and the amount of daily necessities consumed using sensors in the pantry. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze consumption patterns based on the collected data and predict the timing of the next order. The analysis unit can determine the amount of milk consumed weekly based on the collected data and predict the timing of the next order. The analysis unit can also analyze the consumption patterns of daily necessities based on the collected data and predict the timing of the next order. The analysis unit analyzes food consumption based on collected data and predicts the timing of the next order. The selection unit selects products based on the data analyzed by the analysis unit. The selection unit selects the optimal products based on user preferences and budget. For example, the selection unit can prioritize selecting products of a specific brand for users who prefer that brand of milk. The selection unit can also select the most cost-effective product within the budget. The selection unit selects the optimal products based on user preferences and budget. The ordering unit automatically places orders for the products selected by the selection unit. For example, the ordering unit automatically places orders before necessary products run out. For example, the ordering unit can automatically place the next order when the milk stock is low. The ordering unit can also automatically place the next order when the daily necessities stock is low. The ordering unit automatically places orders before necessary products run out. The delivery unit quickly delivers the products ordered by the ordering unit. For example, the delivery unit selects the optimal delivery company and ensures prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery.Furthermore, the delivery department can select the most suitable delivery company and ensure prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery. The evaluation department receives user feedback on the goods delivered by the delivery department. The evaluation department can, for example, improve the next order based on user feedback. The evaluation department can, for example, improve the next order based on user feedback. The evaluation department can, for example, improve the next order based on user feedback. In this way, the AI ​​shopping agent system according to the embodiment can improve user convenience.

[0030] The data collection unit collects consumption data within the home. Specifically, it monitors the inventory status of refrigerators and pantries using sensors and records the items consumed. Sensors in the refrigerator, for example, can monitor the inventory status of each food item in real time using weight sensors and RFID tags. Weight sensors detect changes in the weight of food items, accurately determining consumption. RFID tags are attached to each food item, and a reader in the refrigerator reads the tags to manage inventory status. Similarly, sensors in the pantry monitor the inventory status of daily necessities using weight sensors and RFID tags. This allows the data collection unit to record food consumption using refrigerator sensors and daily necessities consumption using pantry sensors. Furthermore, the data collection unit also allows users to manually input consumption data through a smartphone app or voice assistant. For example, when a user takes milk out of the refrigerator, they can report "I used milk" to the voice assistant, and consumption data will be recorded. This allows the data collection unit to collect accurate and comprehensive consumption data by combining automatic collection by sensors and manual input by users. The collected data is sent to a cloud server and managed so that the analysis unit can access it.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes consumption patterns based on the collected data and predicts the timing of the next order. The analysis unit uses AI to process data in real time and understand consumption patterns. For example, based on the collected data, it can determine the amount of milk consumed each week and predict the timing of the next order. The AI ​​learns from past consumption data and analyzes trends in consumption patterns. This allows the analysis unit to predict user consumption behavior and calculate the optimal ordering timing. The analysis unit can also analyze consumption patterns of daily necessities based on the collected data and predict the timing of the next order. For example, it can analyze how often daily necessities such as toilet paper and detergent are consumed and predict when to place an order before the stock runs low. Furthermore, the analysis unit can also consider fluctuations in consumption patterns due to seasons and special events. For example, since beverage consumption tends to increase in the summer, the analysis unit can predict this fluctuation and place an order at the appropriate time. The analysis unit can also respond to changes in the user's lifestyle and family structure, and reflect fluctuations in consumption patterns in real time. This allows the analysis unit to accurately predict user consumption behavior and provide the optimal timing for placing orders.

[0032] The selection unit selects products based on data analyzed by the analysis unit. Specifically, it selects the most suitable products based on the user's preferences and budget. The selection unit uses AI to analyze the user's past purchase history and rating data to understand their preferences. For example, a user who prefers a specific brand of milk can have products from that brand prioritized. The AI ​​learns from the user's rating data and prioritizes selecting products that the user has given high ratings to. The selection unit can also select the most cost-effective product within a user's budget. For example, if there are multiple products in the same category, it compares price and quality to select the best one. Furthermore, the selection unit can also select products according to the season or special events. For example, it may prioritize cold beverages and ice cream in the summer and hot beverages and soups in the winter. The selection unit can also select products considering the user's health condition and allergy information. For example, if a user has a specific food allergy, it will select products that do not contain that food. In this way, the selection unit can select the most suitable products according to the user's preferences, budget, and health condition, thereby improving user satisfaction.

[0033] The ordering department automatically places orders for products selected by the selection department. Specifically, it automatically places orders before necessary products run out. The ordering department accesses online stores via the internet, adds selected products to the cart, and completes the order process. For example, it can automatically place the next order when the milk stock is low. The ordering department securely manages user account information and payment information, and places orders quickly and accurately. The ordering department can also automatically place the next order when the stock of daily necessities is low. For example, if the stock of toilet paper or detergent is low, the ordering department will automatically place the next order to prevent stockouts. Furthermore, the ordering department can compare multiple online stores and select the store that offers the cheapest price and fastest delivery. This allows the ordering department to place the most economical and efficient orders for the user. In addition, the ordering department manages the order history and can refer to past order details to make future orders smoother. This allows the ordering department to reduce user effort and provide an efficient shopping experience.

[0034] The delivery department ensures the prompt delivery of goods ordered by the order department. Specifically, it selects the most suitable delivery company to ensure fast delivery. The delivery department compares the services of multiple delivery companies and selects the one that offers the most reliable and fastest delivery. For example, it selects the company that provides the fastest delivery in a specific area or time slot to deliver the ordered goods quickly. The delivery department strengthens its collaboration with delivery companies and can track the delivery status in real time. This allows users to check the delivery status of their ordered goods in real time and receive their goods with peace of mind. In addition, the delivery department can provide flexible delivery schedules that take into account the user's desired delivery date, time, and destination. For example, if a user wants to receive their goods on a specific date and time, the delivery department will set up a delivery schedule that meets that request. Furthermore, the delivery department pays attention to the packaging and handling of goods to ensure that they are delivered safely. In this way, the delivery department can provide fast and safe delivery and improve user satisfaction.

[0035] The evaluation department receives user feedback on products delivered by the delivery department. Specifically, it uses user feedback to improve future orders. The evaluation department collects ratings and comments from users through smartphone apps and websites. For example, it collects evaluation data on the quality of products received and the speed of delivery. The evaluation department analyzes the collected feedback and reflects it in future orders. For example, if a user gives a high rating to a particular product, that product will be prioritized in the next order. Also, if a user is dissatisfied with a particular delivery company, the evaluation department can change the delivery company for the next order based on that feedback. Furthermore, the evaluation department can also improve the entire system based on user feedback. For example, based on user feedback, it can review the functions of the collection, analysis, selection, order, and delivery departments and improve the system to make it more user-friendly. In this way, the evaluation department can use user feedback to improve the overall quality of the system and increase user satisfaction.

[0036] The data collection unit can monitor the inventory status of refrigerators and pantries using sensors and record the items consumed. For example, the data collection unit can use sensors inside the refrigerator to monitor the food inventory status in real time. For example, the sensors inside the refrigerator can record the amount of food consumed. The data collection unit can also use sensors inside the pantry to monitor the inventory status of daily necessities. For example, the sensors inside the pantry can record the amount of daily necessities consumed. This allows for accurate recording of consumed items by monitoring the inventory status of refrigerators and pantries with sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by sensors inside the refrigerator into a generating AI, which can then analyze the data and record the inventory status.

[0037] The analysis unit can analyze consumption patterns based on collected data and predict the timing of the next order. For example, the analysis unit can determine the amount of milk consumed weekly based on collected data and predict the timing of the next order. The analysis unit can also analyze consumption patterns of daily necessities based on collected data and predict the timing of the next order. For example, the analysis unit can analyze the amount of food consumed based on collected data and predict the timing of the next order. By analyzing consumption patterns and predicting the timing of the next order, necessary products can be ordered at the appropriate time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI, which can analyze the data to understand consumption patterns and predict the timing of the next order.

[0038] The selection unit can select the optimal product based on the user's preferences and budget. For example, for a user who prefers a particular brand of milk, the selection unit will prioritize selecting products of that brand. The selection unit can also select the most cost-effective product within a given budget. The selection unit selects the optimal product based on the user's preferences and budget. This improves user satisfaction by selecting the optimal product based on the user's preferences and budget. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input data on the user's preferences and budget into a generating AI, which can then analyze the data and select the optimal product.

[0039] The ordering department can automatically place orders before necessary products run out. For example, the ordering department can automatically place the next order when the milk inventory is running low. The ordering department can also automatically place the next order when the daily necessities inventory is running low. The ordering department can automatically place orders before necessary products run out. This prevents shortages of products by automatically placing orders before necessary products run out. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input inventory data into a generating AI, which can analyze the data to determine the timing of orders and place them automatically.

[0040] The delivery department can select the most suitable delivery company and ensure prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery. The delivery department can also, for example, select the most suitable delivery company and ensure prompt delivery. By selecting the most suitable delivery company and ensuring prompt delivery, products can be delivered to users quickly. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input delivery company data into a generating AI, which can analyze the data to select the most suitable delivery company and ensure prompt delivery.

[0041] The evaluation unit can improve the next order based on user feedback. The evaluation unit can, for example, improve the next order based on user feedback. The evaluation unit can, for example, improve the next order based on user feedback. The evaluation unit can also improve the next order based on user feedback. The evaluation unit can, for example, improve the next order based on user feedback. This improves user satisfaction by improving the next order based on user feedback. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback data into a generating AI, and the generating AI can analyze the data to improve the next order.

[0042] The data collection unit can analyze the user's past consumption history and select the optimal collection method when collecting household consumption data. For example, the data collection unit can prioritize collecting products that the user has frequently consumed in the past. For example, the data collection unit can analyze the user's past consumption patterns and determine the optimal collection timing. The data collection unit can also predict the consumption of specific products based on the user's past consumption history and adjust the collection method accordingly. For example, the data collection unit can prioritize collecting products that the user has frequently consumed in the past. This enables efficient data collection by analyzing the user's past consumption history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past consumption history data into a generating AI, which can then analyze the data and select the optimal collection method.

[0043] The data collection unit can filter the collected consumption data based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the data collection unit can prioritize collecting consumption data for health foods. For example, if the user is busy, the data collection unit can prioritize collecting consumption data for easy-to-prepare products. Furthermore, if the user has a specific hobby, the data collection unit can prioritize collecting consumption data for products related to that hobby. For example, if the user is health-conscious, the data collection unit can prioritize collecting consumption data for health foods. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's lifestyle and areas of interest into a generating AI, which can then analyze and filter the data.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting consumption data. For example, the data collection unit can prioritize the collection of consumption data for local specialties in the area where the user lives. For example, if the user is traveling, the data collection unit can prioritize the collection of consumption data at their travel destination. Furthermore, if the user frequently visits a particular region, the data collection unit can prioritize the collection of consumption data in that region. For example, the data collection unit can prioritize the collection of consumption data for local specialties in the area where the user lives. This enables region-specific data collection by considering the user's geographical location when collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0045] The data collection unit can analyze the user's social media activity and collect relevant data when collecting consumption data. For example, the data collection unit can prioritize collecting consumption data for products mentioned by the user on social media. For example, the data collection unit can prioritize collecting consumption data for brands that the user follows on social media. The data collection unit can also collect consumption data based on trends in communities that the user participates in on social media. For example, the data collection unit can prioritize collecting consumption data for products mentioned by the user on social media. This makes it possible to collect data based on the user's interests by analyzing the user's social media activity and collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0046] The analysis unit can improve the accuracy of its analysis by referring to past consumption patterns when analyzing collected data. For example, the analysis unit can analyze current consumption data based on the user's past consumption patterns. For example, the analysis unit can analyze the consumption trends of a specific product from the user's past consumption history. The analysis unit can also predict future consumption by referring to the user's past consumption patterns. For example, the analysis unit can analyze current consumption data based on the user's past consumption patterns. By analyzing the data by referring to past consumption patterns, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past consumption pattern data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0047] The analysis unit can apply different analysis algorithms to each data category when analyzing the collected data. For example, the analysis unit can apply an analysis algorithm that takes expiration dates into account to data in the food category. For example, the analysis unit can apply an analysis algorithm that takes consumption frequency into account to data in the daily necessities category. Furthermore, the analysis unit can apply an analysis algorithm that takes the user's health status into account to data in health-related products. For example, the analysis unit can apply an analysis algorithm that takes expiration dates into account to data in the food category. By applying different analysis algorithms to each data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each data category into a generating AI, and the generating AI can analyze the data and apply them.

[0048] The analysis unit can determine the priority of analysis based on the data submission date when analyzing collected data. For example, the analysis unit can prioritize the analysis of the latest data to understand real-time consumption patterns. For example, the analysis unit can refer to past data to analyze long-term consumption patterns. The analysis unit can also focus on analyzing data collected during a specific period to understand seasonal consumption patterns. For example, the analysis unit can prioritize the analysis of the latest data to understand real-time consumption patterns. This allows for the understanding of real-time consumption patterns by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI, which can then analyze the data and determine the priority of analysis.

[0049] The analysis unit can improve the accuracy of its analysis by referring to relevant external data when analyzing collected data. For example, the analysis unit can refer to market trend data to analyze consumption patterns. For example, the analysis unit can refer to weather data to analyze fluctuations in consumption patterns due to weather. The analysis unit can also refer to social media trend data to analyze consumption patterns. For example, the analysis unit can refer to market trend data to analyze consumption patterns. By referring to relevant external data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input external data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0050] The selection unit can select the most suitable products by referring to the user's past purchase history. For example, the selection unit can select similar products based on products the user has purchased in the past. For example, the selection unit can prioritize selecting products of a specific brand based on the user's past purchase history. The selection unit can also suggest the most suitable product combination by referring to the user's past purchase history. For example, the selection unit can select similar products based on products the user has purchased in the past. In this way, by selecting products by referring to the user's past purchase history, products that match the user's preferences can be selected. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past purchase history data into a generating AI, which can analyze the data and select the most suitable products.

[0051] The selection unit can apply different selection algorithms to each product category when selecting products. For example, the selection unit can apply a selection algorithm that takes expiration dates into account to products in the food category. For example, the selection unit can apply a selection algorithm that takes consumption frequency into account to products in the daily necessities category. Furthermore, the selection unit can apply a selection algorithm that takes the user's health status into account to products in the health-related products category. For example, the selection unit can apply a selection algorithm that takes expiration dates into account to products in the food category. By applying different selection algorithms to each product category, more accurate product selection becomes possible. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input different selection algorithms for each product category into a generating AI, and the generating AI can analyze the data and apply them.

[0052] The selection unit can prioritize selecting highly relevant products by considering the user's geographical location information when selecting products. For example, the selection unit can prioritize selecting local specialties from the user's region. For example, if the user is traveling, the selection unit can select products suitable for consumption at their travel destination. Furthermore, if the user frequently visits a particular region, the selection unit can select products suitable for consumption in that region. For example, the selection unit can prioritize selecting local specialties from the user's region. This makes it possible to select products that are specific to a region by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize selecting highly relevant products.

[0053] The selection unit can analyze a user's social media activity and select relevant products when selecting items. For example, the selection unit can prioritize products mentioned by the user on social media. For example, the selection unit can prioritize products from brands that the user follows on social media. The selection unit can also select products based on trends in communities that the user participates in on social media. For example, the selection unit can prioritize products mentioned by the user on social media. This makes it possible to select products based on the user's interests by analyzing the user's social media activity and selecting products accordingly. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI, which can then analyze the data and select relevant products.

[0054] The ordering system can select the optimal ordering method by referring to the user's past order history when placing an order. For example, the ordering system can prioritize ordering products that the user has frequently ordered in the past. For example, the ordering system can prioritize ordering products of a specific brand based on the user's past order history. The ordering system can also suggest the optimal combination of products by referring to the user's past order history. For example, the ordering system can prioritize ordering products that the user has frequently ordered in the past. This allows for orders tailored to the user's preferences by referring to the user's past order history when placing an order. Some or all of the above processes in the ordering system may be performed using AI, for example, or not using AI. For example, the ordering system can input the user's past order history data into a generating AI, which can analyze the data and select the optimal ordering method.

[0055] The ordering department can adjust the timing of orders by considering the inventory status of the products when placing orders. For example, the ordering department can prioritize ordering products with low inventory. For example, the ordering department can postpone ordering products with ample inventory to the next order. The ordering department can also check inventory status in real time and place orders at the optimal time. For example, the ordering department can prioritize ordering products with low inventory. By adjusting the timing of orders while considering the inventory status of the products, it is possible to prevent inventory shortages. Some or all of the above processes in the ordering department may be performed using AI, or not. For example, the ordering department can input product inventory status data into a generating AI, which can analyze the data and adjust the timing of orders.

[0056] The ordering department can select the most suitable delivery company when an order is placed, taking into account the user's geographical location. For example, the ordering department can select a company that can deliver quickly to the user's area of ​​residence. For example, if the user is traveling, the ordering department can select a company that can deliver to their travel destination. Furthermore, if the user frequently visits a particular region, the ordering department can select a company that can deliver to that region. For example, the ordering department can select a company that can deliver quickly to the user's area of ​​residence. By selecting a delivery company that takes the user's geographical location into account, faster delivery becomes possible. Some or all of the above processing in the ordering department may be performed using AI, for example, or not. For example, the ordering department can input the user's geographical location into a generating AI, which can then analyze the data and select the most suitable delivery company.

[0057] The ordering department can analyze a user's social media activity when placing an order and prioritize ordering relevant products. For example, it can prioritize ordering products that the user has mentioned on social media. For example, it can prioritize ordering products from brands that the user follows on social media. The ordering department can also order products based on trends in communities that the user participates in on social media. For example, it can prioritize ordering products that the user has mentioned on social media. This allows for ordering products based on the user's interests by analyzing their social media activity. Some or all of the above processing in the ordering department may be performed using AI, or not. For example, the ordering department can input the user's social media activity data into a generating AI, which can then analyze the data and prioritize ordering relevant products.

[0058] The delivery department can select the optimal delivery method when delivering goods by referring to the user's past delivery history. For example, the delivery department can prioritize selecting delivery methods that the user has used in the past. For example, the delivery department can prioritize selecting a specific delivery company based on the user's past delivery history. The delivery department can also propose the optimal delivery schedule by referring to the user's past delivery history. For example, the delivery department can prioritize selecting delivery methods that the user has used in the past. This makes it possible to deliver goods in a way that suits the user's preferences by selecting a delivery method based on the user's past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's past delivery history data into a generating AI, which can then analyze the data and select the optimal delivery method.

[0059] The delivery department can select the optimal delivery company by referring to the performance data of delivery companies when delivering goods. For example, the delivery department can select the optimal company based on the past performance data of delivery companies. For example, the delivery department can select the optimal company by referring to the real-time performance data of delivery companies. The delivery department can also select the optimal company by referring to the customer evaluation data of delivery companies. For example, the delivery department can select the optimal company based on the past performance data of delivery companies. This enables fast and reliable delivery by selecting a delivery company by referring to the performance data of delivery companies. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the performance data of delivery companies into a generating AI, and the generating AI can analyze the data and select the optimal delivery company.

[0060] The delivery department can select the optimal delivery route when delivering goods, taking into account the user's geographical location information. For example, the delivery department can select the optimal delivery route by considering the traffic conditions in the area where the user lives. For example, if the user is traveling, the delivery department can select the optimal delivery route by considering the traffic conditions at the travel destination. Furthermore, if the user frequently visits a particular area, the delivery department can select the optimal delivery route by considering the traffic conditions in that area. For example, the delivery department can select the optimal delivery route by considering the traffic conditions in the area where the user lives. By selecting a delivery route that takes into account the user's geographical location information, efficient delivery becomes possible. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to select the optimal delivery route.

[0061] The delivery department can analyze a user's social media activity and provide relevant delivery information when delivering products. For example, the delivery department can prioritize delivery information for products mentioned by the user on social media. For example, the delivery department can prioritize delivery information for products from brands that the user follows on social media. The delivery department can also provide delivery information based on trends in communities that the user participates in on social media. For example, the delivery department can prioritize delivery information for products mentioned by the user on social media. This enables delivery based on the user's interests by analyzing the user's social media activity and providing delivery information accordingly. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's social media activity data into a generating AI, which can then analyze the data and provide relevant delivery information.

[0062] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating products. For example, the evaluation unit can evaluate similar products based on the user's past evaluation history. For example, the evaluation unit can prioritize evaluating products of a specific brand based on the user's past evaluation history. The evaluation unit can also propose optimal evaluation criteria by referring to the user's past evaluation history. For example, the evaluation unit can evaluate similar products based on the user's past evaluation history. This makes it possible to perform evaluations that match the user's preferences by referring to the user's past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past evaluation history data into a generating AI, which can analyze the data and select the optimal evaluation method.

[0063] The evaluation unit can apply different evaluation algorithms to each product category when evaluating products. For example, the evaluation unit can apply an evaluation algorithm that considers taste and quality to products in the food category. For example, the evaluation unit can apply an evaluation algorithm that considers usability and durability to products in the daily necessities category. Furthermore, the evaluation unit can apply an evaluation algorithm that considers effectiveness and safety to products in the health-related products category. For example, the evaluation unit can apply an evaluation algorithm that considers taste and quality to products in the food category. By applying different evaluation algorithms to each product category, more accurate evaluations become possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input different evaluation algorithms for each product category into a generating AI, and the generating AI can analyze the data and apply them.

[0064] The evaluation unit can determine the priority of product evaluations by considering the user's geographical location. For example, the evaluation unit may prioritize evaluating local specialties from the user's region. For example, if the user is traveling, the evaluation unit may prioritize evaluating products suitable for consumption at their travel destination. Furthermore, if the user frequently visits a particular region, the evaluation unit may prioritize evaluating products suitable for consumption in that region. For example, the evaluation unit may prioritize evaluating local specialties from the user's region. This allows for region-specific evaluations by considering the user's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the user's geographical location into a generating AI, which can then analyze the data to determine the priority of evaluations.

[0065] The evaluation unit can analyze a user's social media activity and provide relevant evaluation information when evaluating products. For example, the evaluation unit can prioritize providing evaluation information for products mentioned by the user on social media. For example, the evaluation unit can prioritize providing evaluation information for brands that the user follows on social media. The evaluation unit can also provide evaluation information based on trends in communities that the user participates in on social media. For example, the evaluation unit can prioritize providing evaluation information for products mentioned by the user on social media. This enables evaluations based on the user's interests by analyzing the user's social media activity and providing evaluation information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user social media activity data into a generating AI, which can then analyze the data and provide relevant evaluation information.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can analyze a user's past consumption history and select the optimal collection method when collecting household consumption data. For example, it can prioritize collecting products that the user has frequently consumed in the past. It can also analyze the user's past consumption patterns and determine the optimal collection timing. Furthermore, it can predict the consumption volume of specific products based on the user's past consumption history and adjust the collection method accordingly. By analyzing the user's past consumption history and selecting the optimal collection method, efficient data collection becomes possible.

[0068] The analysis unit can improve the accuracy of its analysis by referring to past consumption patterns when analyzing collected data. For example, it can analyze current consumption data based on a user's past consumption patterns. It can also analyze consumption trends for specific products based on a user's past consumption history. Furthermore, it can predict future consumption by referring to a user's past consumption patterns. In this way, the accuracy of the analysis is improved by analyzing data while referring to past consumption patterns.

[0069] The selection unit can select the most suitable products by referring to the user's past purchase history. For example, it can select similar products based on products the user has purchased in the past. It can also prioritize products from a specific brand based on the user's past purchase history. Furthermore, it can suggest the optimal product combination by referring to the user's past purchase history. In this way, by selecting products based on the user's past purchase history, products that match the user's preferences can be selected.

[0070] The ordering department can adjust the timing of orders by considering the inventory status of the products. For example, it can prioritize ordering products with low stock. Products with ample stock can be postponed to the next order. Furthermore, it can check inventory status in real time and place orders at the optimal time. In this way, by adjusting the timing of orders based on product inventory status, it is possible to prevent stock shortages.

[0071] The delivery department can select the most suitable delivery company by referring to the delivery company's performance data when shipping goods. For example, it can select the best company based on the company's past performance data. It can also select the best company by referring to the delivery company's real-time performance data. Furthermore, it can select the best company by referring to the delivery company's customer evaluation data. By selecting a delivery company based on its performance data, it becomes possible to deliver goods quickly and reliably.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit collects consumption data within the household. For example, it monitors the inventory status of refrigerators and pantries using sensors and records the products that have been consumed. The collection unit uses sensors inside the refrigerator to monitor the inventory status of food items in real time and sensors inside the pantry to monitor the inventory status of daily necessities. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes consumption patterns based on the collected data and predicts the timing of the next order. The analysis unit can grasp the consumption volume of food and daily necessities and predict the timing of the next order. Step 3: The selection unit selects products based on the data analyzed by the analysis unit. For example, it selects the optimal product based on the user's preferences and budget. The selection unit can prioritize products of a particular brand for users who prefer that brand of milk, and can select the most cost-effective product within the budget. Step 4: The ordering department automatically places orders for the products selected by the selection department. For example, it automatically places orders before necessary products run out. The ordering department can automatically place the next order when the stock of milk or daily necessities is running low. Step 5: The shipping department promptly delivers the goods ordered by the order department. For example, they select the most suitable shipping company and ensure prompt delivery. Step 6: The evaluation department receives user feedback on the products delivered by the delivery department. For example, they use user feedback to improve future orders.

[0074] (Example of form 2) The AI ​​shopping agent system according to an embodiment of the present invention is an AI service designed for the elderly and busy people. This AI shopping agent system collects household consumption data in real time, analyzes that data with AI to select necessary products, and places orders automatically. Furthermore, it ensures prompt delivery and improves future orders based on user feedback. This mechanism provides peace of mind and convenience to people who have difficulty shopping on their own, improving their quality of life. For example, it collects household consumption data in real time. For example, it monitors the inventory status of refrigerators and pantries with sensors and records the products that have been consumed. This data is input into the AI, which analyzes consumption patterns. For example, it can determine the amount of milk consumed each week and predict the timing of the next order. Next, the AI ​​selects the optimal products based on the user's preferences and budget. For example, for a user who prefers a particular brand of milk, it will prioritize selecting products of that brand. It also selects the most cost-effective products within the budget. Furthermore, the AI ​​automatically places orders before necessary products run out. For example, when the milk inventory is low, it automatically places the next order. At this time, it selects the optimal delivery company to ensure prompt delivery. Once an order is complete, the user receives a notification and can review the order details. After the product arrives, the user evaluates it, and the AI ​​learns from this feedback to improve future orders. For example, if delivery is delayed or the product does not meet expectations, the AI ​​uses this feedback to improve future orders. This system provides peace of mind and convenience to those who have difficulty shopping on their own, improving their quality of life. For instance, the elderly and busy individuals can avoid the hassle of shopping and always have necessary items on hand. Furthermore, because the AI ​​selects the most suitable products according to the user's preferences and budget, a highly satisfying shopping experience is provided. In this way, the AI ​​shopping agent system can improve user convenience.

[0075] The AI ​​shopping agent system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, an order unit, a delivery unit, and an evaluation unit. The collection unit collects consumption data within the household. The collection unit monitors the inventory status of refrigerators and pantries using sensors, for example, and records the consumed products. The collection unit can monitor the inventory status of food in real time using sensors in the refrigerator, for example. The collection unit can also monitor the inventory status of daily necessities using sensors in the pantry. The collection unit can record the amount of food consumed using sensors in the refrigerator, and the amount of daily necessities consumed using sensors in the pantry. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze consumption patterns based on the collected data and predict the timing of the next order. The analysis unit can determine the amount of milk consumed weekly based on the collected data and predict the timing of the next order. The analysis unit can also analyze the consumption patterns of daily necessities based on the collected data and predict the timing of the next order. The analysis unit analyzes food consumption based on collected data and predicts the timing of the next order. The selection unit selects products based on the data analyzed by the analysis unit. The selection unit selects the optimal products based on user preferences and budget. For example, the selection unit can prioritize selecting products of a specific brand for users who prefer that brand of milk. The selection unit can also select the most cost-effective product within the budget. The selection unit selects the optimal products based on user preferences and budget. The ordering unit automatically places orders for the products selected by the selection unit. For example, the ordering unit automatically places orders before necessary products run out. For example, the ordering unit can automatically place the next order when the milk stock is low. The ordering unit can also automatically place the next order when the daily necessities stock is low. The ordering unit automatically places orders before necessary products run out. The delivery unit quickly delivers the products ordered by the ordering unit. For example, the delivery unit selects the optimal delivery company and ensures prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery.Furthermore, the delivery department can select the most suitable delivery company and ensure prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery. The evaluation department receives user feedback on the goods delivered by the delivery department. The evaluation department can, for example, improve the next order based on user feedback. The evaluation department can, for example, improve the next order based on user feedback. The evaluation department can, for example, improve the next order based on user feedback. In this way, the AI ​​shopping agent system according to the embodiment can improve user convenience.

[0076] The data collection unit collects consumption data within the home. Specifically, it monitors the inventory status of refrigerators and pantries using sensors and records the items consumed. Sensors in the refrigerator, for example, can monitor the inventory status of each food item in real time using weight sensors and RFID tags. Weight sensors detect changes in the weight of food items, accurately determining consumption. RFID tags are attached to each food item, and a reader in the refrigerator reads the tags to manage inventory status. Similarly, sensors in the pantry monitor the inventory status of daily necessities using weight sensors and RFID tags. This allows the data collection unit to record food consumption using refrigerator sensors and daily necessities consumption using pantry sensors. Furthermore, the data collection unit also allows users to manually input consumption data through a smartphone app or voice assistant. For example, when a user takes milk out of the refrigerator, they can report "I used milk" to the voice assistant, and consumption data will be recorded. This allows the data collection unit to collect accurate and comprehensive consumption data by combining automatic collection by sensors and manual input by users. The collected data is sent to a cloud server and managed so that the analysis unit can access it.

[0077] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes consumption patterns based on the collected data and predicts the timing of the next order. The analysis unit uses AI to process data in real time and understand consumption patterns. For example, based on the collected data, it can determine the amount of milk consumed each week and predict the timing of the next order. The AI ​​learns from past consumption data and analyzes trends in consumption patterns. This allows the analysis unit to predict user consumption behavior and calculate the optimal ordering timing. The analysis unit can also analyze consumption patterns of daily necessities based on the collected data and predict the timing of the next order. For example, it can analyze how often daily necessities such as toilet paper and detergent are consumed and predict when to place an order before the stock runs low. Furthermore, the analysis unit can also consider fluctuations in consumption patterns due to seasons and special events. For example, since beverage consumption tends to increase in the summer, the analysis unit can predict this fluctuation and place an order at the appropriate time. The analysis unit can also respond to changes in the user's lifestyle and family structure, and reflect fluctuations in consumption patterns in real time. This allows the analysis unit to accurately predict user consumption behavior and provide the optimal timing for placing orders.

[0078] The selection unit selects products based on data analyzed by the analysis unit. Specifically, it selects the most suitable products based on the user's preferences and budget. The selection unit uses AI to analyze the user's past purchase history and rating data to understand their preferences. For example, a user who prefers a specific brand of milk can have products from that brand prioritized. The AI ​​learns from the user's rating data and prioritizes selecting products that the user has given high ratings to. The selection unit can also select the most cost-effective product within a user's budget. For example, if there are multiple products in the same category, it compares price and quality to select the best one. Furthermore, the selection unit can also select products according to the season or special events. For example, it may prioritize cold beverages and ice cream in the summer and hot beverages and soups in the winter. The selection unit can also select products considering the user's health condition and allergy information. For example, if a user has a specific food allergy, it will select products that do not contain that food. In this way, the selection unit can select the most suitable products according to the user's preferences, budget, and health condition, thereby improving user satisfaction.

[0079] The ordering department automatically places orders for products selected by the selection department. Specifically, it automatically places orders before necessary products run out. The ordering department accesses online stores via the internet, adds selected products to the cart, and completes the order process. For example, it can automatically place the next order when the milk stock is low. The ordering department securely manages user account information and payment information, and places orders quickly and accurately. The ordering department can also automatically place the next order when the stock of daily necessities is low. For example, if the stock of toilet paper or detergent is low, the ordering department will automatically place the next order to prevent stockouts. Furthermore, the ordering department can compare multiple online stores and select the store that offers the cheapest price and fastest delivery. This allows the ordering department to place the most economical and efficient orders for the user. In addition, the ordering department manages the order history and can refer to past order details to make future orders smoother. This allows the ordering department to reduce user effort and provide an efficient shopping experience.

[0080] The delivery department ensures the prompt delivery of goods ordered by the order department. Specifically, it selects the most suitable delivery company to ensure fast delivery. The delivery department compares the services of multiple delivery companies and selects the one that offers the most reliable and fastest delivery. For example, it selects the company that provides the fastest delivery in a specific area or time slot to deliver the ordered goods quickly. The delivery department strengthens its collaboration with delivery companies and can track the delivery status in real time. This allows users to check the delivery status of their ordered goods in real time and receive their goods with peace of mind. In addition, the delivery department can provide flexible delivery schedules that take into account the user's desired delivery date, time, and destination. For example, if a user wants to receive their goods on a specific date and time, the delivery department will set up a delivery schedule that meets that request. Furthermore, the delivery department pays attention to the packaging and handling of goods to ensure that they are delivered safely. In this way, the delivery department can provide fast and safe delivery and improve user satisfaction.

[0081] The evaluation department receives user feedback on products delivered by the delivery department. Specifically, it uses user feedback to improve future orders. The evaluation department collects ratings and comments from users through smartphone apps and websites. For example, it collects evaluation data on the quality of products received and the speed of delivery. The evaluation department analyzes the collected feedback and reflects it in future orders. For example, if a user gives a high rating to a particular product, that product will be prioritized in the next order. Also, if a user is dissatisfied with a particular delivery company, the evaluation department can change the delivery company for the next order based on that feedback. Furthermore, the evaluation department can also improve the entire system based on user feedback. For example, based on user feedback, it can review the functions of the collection, analysis, selection, order, and delivery departments and improve the system to make it more user-friendly. In this way, the evaluation department can use user feedback to improve the overall quality of the system and increase user satisfaction.

[0082] The data collection unit can monitor the inventory status of refrigerators and pantries using sensors and record the items consumed. For example, the data collection unit can use sensors inside the refrigerator to monitor the food inventory status in real time. For example, the sensors inside the refrigerator can record the amount of food consumed. The data collection unit can also use sensors inside the pantry to monitor the inventory status of daily necessities. For example, the sensors inside the pantry can record the amount of daily necessities consumed. This allows for accurate recording of consumed items by monitoring the inventory status of refrigerators and pantries with sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by sensors inside the refrigerator into a generating AI, which can then analyze the data and record the inventory status.

[0083] The analysis unit can analyze consumption patterns based on collected data and predict the timing of the next order. For example, the analysis unit can determine the amount of milk consumed weekly based on collected data and predict the timing of the next order. The analysis unit can also analyze consumption patterns of daily necessities based on collected data and predict the timing of the next order. For example, the analysis unit can analyze the amount of food consumed based on collected data and predict the timing of the next order. By analyzing consumption patterns and predicting the timing of the next order, necessary products can be ordered at the appropriate time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI, which can analyze the data to understand consumption patterns and predict the timing of the next order.

[0084] The selection unit can select the optimal product based on the user's preferences and budget. For example, for a user who prefers a particular brand of milk, the selection unit will prioritize selecting products of that brand. The selection unit can also select the most cost-effective product within a given budget. The selection unit selects the optimal product based on the user's preferences and budget. This improves user satisfaction by selecting the optimal product based on the user's preferences and budget. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input data on the user's preferences and budget into a generating AI, which can then analyze the data and select the optimal product.

[0085] The ordering department can automatically place orders before necessary products run out. For example, the ordering department can automatically place the next order when the milk inventory is running low. The ordering department can also automatically place the next order when the daily necessities inventory is running low. The ordering department can automatically place orders before necessary products run out. This prevents shortages of products by automatically placing orders before necessary products run out. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input inventory data into a generating AI, which can analyze the data to determine the timing of orders and place them automatically.

[0086] The delivery department can select the most suitable delivery company and ensure prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery. The delivery department can, for example, select the most suitable delivery company and ensure prompt delivery. The delivery department can also, for example, select the most suitable delivery company and ensure prompt delivery. By selecting the most suitable delivery company and ensuring prompt delivery, products can be delivered to users quickly. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input delivery company data into a generating AI, which can analyze the data to select the most suitable delivery company and ensure prompt delivery.

[0087] The evaluation unit can improve the next order based on user feedback. The evaluation unit can, for example, improve the next order based on user feedback. The evaluation unit can, for example, improve the next order based on user feedback. The evaluation unit can also improve the next order based on user feedback. The evaluation unit can, for example, improve the next order based on user feedback. This improves user satisfaction by improving the next order based on user feedback. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback data into a generating AI, and the generating AI can analyze the data to improve the next order.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of consumption data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of consumption data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of consumption data collection to collect more detailed data. The data collection unit can also automate consumption data collection if the user is busy, saving the user time and effort. For example, if the user is stressed, the data collection unit can reduce the frequency of consumption data collection to alleviate the user's burden. This reduces the user's burden by adjusting the timing of consumption data collection based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can analyze the data and adjust the timing of consumption data collection.

[0089] The data collection unit can analyze the user's past consumption history and select the optimal collection method when collecting household consumption data. For example, the data collection unit can prioritize collecting products that the user has frequently consumed in the past. For example, the data collection unit can analyze the user's past consumption patterns and determine the optimal collection timing. The data collection unit can also predict the consumption of specific products based on the user's past consumption history and adjust the collection method accordingly. For example, the data collection unit can prioritize collecting products that the user has frequently consumed in the past. This enables efficient data collection by analyzing the user's past consumption history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past consumption history data into a generating AI, which can then analyze the data and select the optimal collection method.

[0090] The data collection unit can filter the collected consumption data based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the data collection unit can prioritize collecting consumption data for health foods. For example, if the user is busy, the data collection unit can prioritize collecting consumption data for easy-to-prepare products. Furthermore, if the user has a specific hobby, the data collection unit can prioritize collecting consumption data for products related to that hobby. For example, if the user is health-conscious, the data collection unit can prioritize collecting consumption data for health foods. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's lifestyle and areas of interest into a generating AI, which can then analyze and filter the data.

[0091] The data collection unit can estimate the user's emotions and determine the priority of consumption data to collect based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can prioritize collecting consumption data for products that help reduce stress. For example, if the user is relaxed, the data collection unit can prioritize collecting consumption data for products that have a relaxing effect. Also, if the user is busy, the data collection unit can prioritize collecting consumption data for products that help save time. For example, if the user is feeling stressed, the data collection unit can prioritize collecting consumption data for products that help reduce stress. This makes it possible to collect data that meets the user's needs by prioritizing consumption data based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generating AI, and the generating AI can analyze the data to determine the priority of consumption data.

[0092] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting consumption data. For example, the data collection unit can prioritize the collection of consumption data for local specialties in the area where the user lives. For example, if the user is traveling, the data collection unit can prioritize the collection of consumption data at their travel destination. Furthermore, if the user frequently visits a particular region, the data collection unit can prioritize the collection of consumption data in that region. For example, the data collection unit can prioritize the collection of consumption data for local specialties in the area where the user lives. This enables region-specific data collection by considering the user's geographical location when collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0093] The data collection unit can analyze the user's social media activity and collect relevant data when collecting consumption data. For example, the data collection unit can prioritize collecting consumption data for products mentioned by the user on social media. For example, the data collection unit can prioritize collecting consumption data for brands that the user follows on social media. The data collection unit can also collect consumption data based on trends in communities that the user participates in on social media. For example, the data collection unit can prioritize collecting consumption data for products mentioned by the user on social media. This makes it possible to collect data based on the user's interests by analyzing the user's social media activity and collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0094] The analysis unit can estimate the user's emotions and adjust the method of analyzing consumption patterns based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can focus on analyzing consumption patterns of products that help reduce stress. For example, if the user is relaxed, the analysis unit can focus on analyzing consumption patterns of products that have a relaxing effect. Also, if the user is busy, the analysis unit can focus on analyzing consumption patterns of products that help save time. For example, if the user is feeling stressed, the analysis unit can focus on analyzing consumption patterns of products that help reduce stress. By adjusting the method of analyzing consumption patterns based on the user's emotions, analysis tailored to the user's needs becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, and the generating AI can analyze the data and adjust the method of analyzing consumption patterns.

[0095] The analysis unit can improve the accuracy of its analysis by referring to past consumption patterns when analyzing collected data. For example, the analysis unit can analyze current consumption data based on the user's past consumption patterns. For example, the analysis unit can analyze the consumption trends of a specific product from the user's past consumption history. The analysis unit can also predict future consumption by referring to the user's past consumption patterns. For example, the analysis unit can analyze current consumption data based on the user's past consumption patterns. By analyzing the data by referring to past consumption patterns, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past consumption pattern data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0096] The analysis unit can apply different analysis algorithms to each data category when analyzing the collected data. For example, the analysis unit can apply an analysis algorithm that takes expiration dates into account to data in the food category. For example, the analysis unit can apply an analysis algorithm that takes consumption frequency into account to data in the daily necessities category. Furthermore, the analysis unit can apply an analysis algorithm that takes the user's health status into account to data in health-related products. For example, the analysis unit can apply an analysis algorithm that takes expiration dates into account to data in the food category. By applying different analysis algorithms to each data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each data category into a generating AI, and the generating AI can analyze the data and apply them.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can display simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can display detailed analysis results. The analysis unit can also display concise analysis results if the user is busy. For example, if the user is feeling stressed, the analysis unit can display simple and easy-to-understand analysis results. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, and the generating AI can analyze the data and adjust the display method of the analysis results.

[0098] The analysis unit can determine the priority of analysis based on the data submission date when analyzing collected data. For example, the analysis unit can prioritize the analysis of the latest data to understand real-time consumption patterns. For example, the analysis unit can refer to past data to analyze long-term consumption patterns. The analysis unit can also focus on analyzing data collected during a specific period to understand seasonal consumption patterns. For example, the analysis unit can prioritize the analysis of the latest data to understand real-time consumption patterns. This allows for the understanding of real-time consumption patterns by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI, which can then analyze the data and determine the priority of analysis.

[0099] The analysis unit can improve the accuracy of its analysis by referring to relevant external data when analyzing collected data. For example, the analysis unit can refer to market trend data to analyze consumption patterns. For example, the analysis unit can refer to weather data to analyze fluctuations in consumption patterns due to weather. The analysis unit can also refer to social media trend data to analyze consumption patterns. For example, the analysis unit can refer to market trend data to analyze consumption patterns. By referring to relevant external data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input external data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0100] The selection unit can estimate the user's emotions and adjust the product selection criteria based on the estimated emotions. For example, if the user is feeling stressed, the selection unit can prioritize selecting products that help reduce stress. For example, if the user is relaxed, the selection unit can prioritize selecting products that have a relaxing effect. Also, if the user is busy, the selection unit can prioritize selecting products that help save time. For example, if the user is feeling stressed, the selection unit can prioritize selecting products that help reduce stress. By adjusting the product selection criteria based on the user's emotions, it becomes possible to select products that meet the user's needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into a generating AI, and the generating AI can analyze the data and adjust the product selection criteria.

[0101] The selection unit can select the most suitable products by referring to the user's past purchase history. For example, the selection unit can select similar products based on products the user has purchased in the past. For example, the selection unit can prioritize selecting products of a specific brand based on the user's past purchase history. The selection unit can also suggest the most suitable product combination by referring to the user's past purchase history. For example, the selection unit can select similar products based on products the user has purchased in the past. In this way, by selecting products by referring to the user's past purchase history, products that match the user's preferences can be selected. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past purchase history data into a generating AI, which can analyze the data and select the most suitable products.

[0102] The selection unit can apply different selection algorithms to each product category when selecting products. For example, the selection unit can apply a selection algorithm that takes expiration dates into account to products in the food category. For example, the selection unit can apply a selection algorithm that takes consumption frequency into account to products in the daily necessities category. Furthermore, the selection unit can apply a selection algorithm that takes the user's health status into account to products in the health-related products category. For example, the selection unit can apply a selection algorithm that takes expiration dates into account to products in the food category. By applying different selection algorithms to each product category, more accurate product selection becomes possible. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input different selection algorithms for each product category into a generating AI, and the generating AI can analyze the data and apply them.

[0103] The selection unit can estimate the user's emotions and determine the priority of products to select based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can prioritize products that help reduce stress. For example, if the user is relaxed, the selection unit can prioritize products that have a relaxing effect. Also, if the user is busy, the selection unit can prioritize products that help save time. For example, if the user is feeling stressed, the selection unit can prioritize products that help reduce stress. By determining the priority of products based on the user's emotions, it becomes possible to select products that meet the user's needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into a generating AI, and the generating AI can analyze the data to determine the priority of products.

[0104] The selection unit can prioritize selecting highly relevant products by considering the user's geographical location information when selecting products. For example, the selection unit can prioritize selecting local specialties from the user's region. For example, if the user is traveling, the selection unit can select products suitable for consumption at their travel destination. Furthermore, if the user frequently visits a particular region, the selection unit can select products suitable for consumption in that region. For example, the selection unit can prioritize selecting local specialties from the user's region. This makes it possible to select products that are specific to a region by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize selecting highly relevant products.

[0105] The selection unit can analyze a user's social media activity and select relevant products when selecting items. For example, the selection unit can prioritize products mentioned by the user on social media. For example, the selection unit can prioritize products from brands that the user follows on social media. The selection unit can also select products based on trends in communities that the user participates in on social media. For example, the selection unit can prioritize products mentioned by the user on social media. This makes it possible to select products based on the user's interests by analyzing the user's social media activity and selecting products accordingly. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI, which can then analyze the data and select relevant products.

[0106] The ordering system can estimate the user's emotions and adjust the timing of orders based on those emotions. For example, if the user is feeling stressed, the ordering system will prioritize ordering products that help reduce stress. If the user is relaxed, the ordering system will prioritize ordering products that have a relaxing effect. The ordering system can also prioritize ordering products that help save time if the user is busy. For example, if the user is feeling stressed, the ordering system will prioritize ordering products that help reduce stress. By adjusting the timing of orders based on the user's emotions, it becomes possible to place orders that meet the user's needs. Some or all of the above processing in the ordering system may be performed using AI, for example, or not. For example, the ordering system can input user emotion data into a generating AI, which can analyze the data and adjust the timing of orders.

[0107] The ordering system can select the optimal ordering method by referring to the user's past order history when placing an order. For example, the ordering system can prioritize ordering products that the user has frequently ordered in the past. For example, the ordering system can prioritize ordering products of a specific brand based on the user's past order history. The ordering system can also suggest the optimal combination of products by referring to the user's past order history. For example, the ordering system can prioritize ordering products that the user has frequently ordered in the past. This allows for orders tailored to the user's preferences by referring to the user's past order history when placing an order. Some or all of the above processes in the ordering system may be performed using AI, for example, or not using AI. For example, the ordering system can input the user's past order history data into a generating AI, which can analyze the data and select the optimal ordering method.

[0108] The ordering department can adjust the timing of orders by considering the inventory status of the products when placing orders. For example, the ordering department can prioritize ordering products with low inventory. For example, the ordering department can postpone ordering products with ample inventory to the next order. The ordering department can also check inventory status in real time and place orders at the optimal time. For example, the ordering department can prioritize ordering products with low inventory. By adjusting the timing of orders while considering the inventory status of the products, it is possible to prevent inventory shortages. Some or all of the above processes in the ordering department may be performed using AI, or not. For example, the ordering department can input product inventory status data into a generating AI, which can analyze the data and adjust the timing of orders.

[0109] The ordering system can estimate the user's emotions and determine the priority of products to be ordered based on those estimated emotions. For example, if the user is stressed, the ordering system can prioritize products that help reduce stress. If the user is relaxed, the ordering system can prioritize products that have a relaxing effect. Also, if the user is busy, the ordering system can prioritize products that help save time. By determining product priorities based on the user's emotions, it becomes possible to place orders that meet the user's needs. Some or all of the above processing in the ordering system may be performed using AI, for example, or not. For example, the ordering system can input user emotion data into a generating AI, which can then analyze the data to determine product priorities.

[0110] The ordering department can select the most suitable delivery company when an order is placed, taking into account the user's geographical location. For example, the ordering department can select a company that can deliver quickly to the user's area of ​​residence. For example, if the user is traveling, the ordering department can select a company that can deliver to their travel destination. Furthermore, if the user frequently visits a particular region, the ordering department can select a company that can deliver to that region. For example, the ordering department can select a company that can deliver quickly to the user's area of ​​residence. By selecting a delivery company that takes the user's geographical location into account, faster delivery becomes possible. Some or all of the above processing in the ordering department may be performed using AI, for example, or not. For example, the ordering department can input the user's geographical location into a generating AI, which can then analyze the data and select the most suitable delivery company.

[0111] The ordering department can analyze a user's social media activity when placing an order and prioritize ordering relevant products. For example, it can prioritize ordering products that the user has mentioned on social media. For example, it can prioritize ordering products from brands that the user follows on social media. The ordering department can also order products based on trends in communities that the user participates in on social media. For example, it can prioritize ordering products that the user has mentioned on social media. This allows for ordering products based on the user's interests by analyzing their social media activity. Some or all of the above processing in the ordering department may be performed using AI, or not. For example, the ordering department can input the user's social media activity data into a generating AI, which can then analyze the data and prioritize ordering relevant products.

[0112] The delivery department can estimate the user's emotions and adjust the delivery timing based on those emotions. For example, if the user is stressed, the delivery department will prioritize expedited delivery. If the user is relaxed, the delivery department can maintain the normal delivery schedule. The delivery department can also adjust the delivery timing to suit the user's needs if the user is busy. For example, if the user is stressed, the delivery department will prioritize expedited delivery. By adjusting the delivery timing based on the user's emotions, it becomes possible to deliver according to the user's needs. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input user emotion data into a generating AI, which can analyze the data and adjust the delivery timing.

[0113] The delivery department can select the optimal delivery method when delivering goods by referring to the user's past delivery history. For example, the delivery department can prioritize selecting delivery methods that the user has used in the past. For example, the delivery department can prioritize selecting a specific delivery company based on the user's past delivery history. The delivery department can also propose the optimal delivery schedule by referring to the user's past delivery history. For example, the delivery department can prioritize selecting delivery methods that the user has used in the past. This makes it possible to deliver goods in a way that suits the user's preferences by selecting a delivery method based on the user's past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's past delivery history data into a generating AI, which can then analyze the data and select the optimal delivery method.

[0114] The delivery department can select the optimal delivery company by referring to the performance data of delivery companies when delivering goods. For example, the delivery department can select the optimal company based on the past performance data of delivery companies. For example, the delivery department can select the optimal company by referring to the real-time performance data of delivery companies. The delivery department can also select the optimal company by referring to the customer evaluation data of delivery companies. For example, the delivery department can select the optimal company based on the past performance data of delivery companies. This enables fast and reliable delivery by selecting a delivery company by referring to the performance data of delivery companies. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the performance data of delivery companies into a generating AI, and the generating AI can analyze the data and select the optimal delivery company.

[0115] The delivery department can estimate the user's emotions and determine delivery priorities based on those emotions. For example, if the user is stressed, the delivery department can prioritize the delivery of products that help reduce stress. If the user is relaxed, the delivery department can prioritize the delivery of products that have a relaxing effect. Also, if the user is busy, the delivery department can prioritize the delivery of products that help save time. For example, if the user is stressed, the delivery department can prioritize the delivery of products that help reduce stress. By determining delivery priorities based on the user's emotions, it becomes possible to deliver products that meet the user's needs. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input user emotion data into a generating AI, which can analyze the data to determine delivery priorities.

[0116] The delivery department can select the optimal delivery route when delivering goods, taking into account the user's geographical location information. For example, the delivery department can select the optimal delivery route by considering the traffic conditions in the area where the user lives. For example, if the user is traveling, the delivery department can select the optimal delivery route by considering the traffic conditions at the travel destination. Furthermore, if the user frequently visits a particular area, the delivery department can select the optimal delivery route by considering the traffic conditions in that area. For example, the delivery department can select the optimal delivery route by considering the traffic conditions in the area where the user lives. By selecting a delivery route that takes into account the user's geographical location information, efficient delivery becomes possible. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to select the optimal delivery route.

[0117] The delivery department can analyze a user's social media activity and provide relevant delivery information when delivering products. For example, the delivery department can prioritize delivery information for products mentioned by the user on social media. For example, the delivery department can prioritize delivery information for products from brands that the user follows on social media. The delivery department can also provide delivery information based on trends in communities that the user participates in on social media. For example, the delivery department can prioritize delivery information for products mentioned by the user on social media. This enables delivery based on the user's interests by analyzing the user's social media activity and providing delivery information accordingly. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's social media activity data into a generating AI, which can then analyze the data and provide relevant delivery information.

[0118] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize adjusting the evaluation criteria for products that help reduce stress. For example, if the user is relaxed, the evaluation unit can prioritize adjusting the evaluation criteria for products that have a relaxing effect. Also, if the user is busy, the evaluation unit can prioritize adjusting the evaluation criteria for products that help save time. For example, if the user is feeling stressed, the evaluation unit can prioritize adjusting the evaluation criteria for products that help reduce stress. By adjusting the evaluation criteria based on the user's emotions, it becomes possible to provide evaluations that meet the user's needs. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generating AI, which can analyze the data and adjust the evaluation criteria.

[0119] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating products. For example, the evaluation unit can evaluate similar products based on the user's past evaluation history. For example, the evaluation unit can prioritize evaluating products of a specific brand based on the user's past evaluation history. The evaluation unit can also propose optimal evaluation criteria by referring to the user's past evaluation history. For example, the evaluation unit can evaluate similar products based on the user's past evaluation history. This makes it possible to perform evaluations that match the user's preferences by referring to the user's past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past evaluation history data into a generating AI, which can analyze the data and select the optimal evaluation method.

[0120] The evaluation unit can apply different evaluation algorithms to each product category when evaluating products. For example, the evaluation unit can apply an evaluation algorithm that considers taste and quality to products in the food category. For example, the evaluation unit can apply an evaluation algorithm that considers usability and durability to products in the daily necessities category. Furthermore, the evaluation unit can apply an evaluation algorithm that considers effectiveness and safety to products in the health-related products category. For example, the evaluation unit can apply an evaluation algorithm that considers taste and quality to products in the food category. By applying different evaluation algorithms to each product category, more accurate evaluations become possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input different evaluation algorithms for each product category into a generating AI, and the generating AI can analyze the data and apply them.

[0121] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can display simple and easy-to-read evaluation results. For example, if the user is relaxed, the evaluation unit can display detailed evaluation results. The evaluation unit can also display concise evaluation results if the user is busy. For example, if the user is feeling stressed, the evaluation unit can display simple and easy-to-read evaluation results. By adjusting the display method of the evaluation results based on the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generating AI, and the generating AI can analyze the data and adjust the display method of the evaluation results.

[0122] The evaluation unit can determine the priority of product evaluations by considering the user's geographical location. For example, the evaluation unit may prioritize evaluating local specialties from the user's region. For example, if the user is traveling, the evaluation unit may prioritize evaluating products suitable for consumption at their travel destination. Furthermore, if the user frequently visits a particular region, the evaluation unit may prioritize evaluating products suitable for consumption in that region. For example, the evaluation unit may prioritize evaluating local specialties from the user's region. This allows for region-specific evaluations by considering the user's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the user's geographical location into a generating AI, which can then analyze the data to determine the priority of evaluations.

[0123] The evaluation unit can analyze a user's social media activity and provide relevant evaluation information when evaluating products. For example, the evaluation unit can prioritize providing evaluation information for products mentioned by the user on social media. For example, the evaluation unit can prioritize providing evaluation information for brands that the user follows on social media. The evaluation unit can also provide evaluation information based on trends in communities that the user participates in on social media. For example, the evaluation unit can prioritize providing evaluation information for products mentioned by the user on social media. This enables evaluations based on the user's interests by analyzing the user's social media activity and providing evaluation information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user social media activity data into a generating AI, which can then analyze the data and provide relevant evaluation information.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The analysis unit can estimate the user's emotions and adjust the analysis method of consumption patterns based on the estimated user emotions. For example, if the user is feeling stressed, the analysis can focus on consumption patterns of products that help reduce stress. If the user is relaxed, the analysis can focus on consumption patterns of products that have a relaxing effect. Furthermore, if the user is busy, the analysis can focus on consumption patterns of products that help save time. In this way, by adjusting the analysis method of consumption patterns based on the user's emotions, it becomes possible to perform analysis that meets the user's needs.

[0126] The selection unit can estimate the user's emotions and adjust the product selection criteria based on those emotions. For example, if the user is feeling stressed, it can prioritize selecting products that help reduce stress. If the user is relaxed, it can prioritize selecting products that have a relaxing effect. Furthermore, if the user is busy, it can prioritize selecting products that help save time. By adjusting the product selection criteria based on the user's emotions, it becomes possible to select products that meet the user's needs.

[0127] The ordering system can estimate the user's emotions and adjust the timing of orders based on those emotions. For example, if a user is feeling stressed, it can prioritize orders for products that help reduce stress. If a user is relaxed, it can prioritize orders for products that have a relaxing effect. Furthermore, if a user is busy, it can prioritize orders for products that help save time. By adjusting the timing of orders based on the user's emotions, it becomes possible to place orders that meet the user's needs.

[0128] The delivery department can estimate the user's emotions and adjust the delivery timing based on those emotions. For example, if the user is stressed, expedited delivery can be prioritized. Conversely, if the user is relaxed, the normal delivery schedule can be maintained. Furthermore, if the user is busy, the delivery timing can be adjusted to suit the user's needs. In this way, by adjusting the delivery timing based on the user's emotions, delivery can be tailored to the user's needs.

[0129] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those emotions. For example, if the user is stressed, the evaluation criteria can be adjusted to focus on products that help reduce stress. Similarly, if the user is relaxed, the evaluation criteria can be adjusted to focus on products with relaxing effects. Furthermore, if the user is busy, the evaluation criteria can be adjusted to focus on products that help save time. By adjusting the evaluation criteria based on the user's emotions, it becomes possible to provide evaluations that meet the user's needs.

[0130] The data collection unit can analyze a user's past consumption history and select the optimal collection method when collecting household consumption data. For example, it can prioritize collecting products that the user has frequently consumed in the past. It can also analyze the user's past consumption patterns and determine the optimal collection timing. Furthermore, it can predict the consumption volume of specific products based on the user's past consumption history and adjust the collection method accordingly. By analyzing the user's past consumption history and selecting the optimal collection method, efficient data collection becomes possible.

[0131] The analysis unit can improve the accuracy of its analysis by referring to past consumption patterns when analyzing collected data. For example, it can analyze current consumption data based on a user's past consumption patterns. It can also analyze consumption trends for specific products based on a user's past consumption history. Furthermore, it can predict future consumption by referring to a user's past consumption patterns. In this way, the accuracy of the analysis is improved by analyzing data while referring to past consumption patterns.

[0132] The selection unit can select the most suitable products by referring to the user's past purchase history. For example, it can select similar products based on products the user has purchased in the past. It can also prioritize products from a specific brand based on the user's past purchase history. Furthermore, it can suggest the optimal product combination by referring to the user's past purchase history. In this way, by selecting products based on the user's past purchase history, products that match the user's preferences can be selected.

[0133] The ordering department can adjust the timing of orders by considering the inventory status of the products. For example, it can prioritize ordering products with low stock. Products with ample stock can be postponed to the next order. Furthermore, it can check inventory status in real time and place orders at the optimal time. In this way, by adjusting the timing of orders based on product inventory status, it is possible to prevent stock shortages.

[0134] The delivery department can select the most suitable delivery company by referring to the delivery company's performance data when shipping goods. For example, it can select the best company based on the company's past performance data. It can also select the best company by referring to the delivery company's real-time performance data. Furthermore, it can select the best company by referring to the delivery company's customer evaluation data. By selecting a delivery company based on its performance data, it becomes possible to deliver goods quickly and reliably.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit collects consumption data within the household. For example, it monitors the inventory status of refrigerators and pantries using sensors and records the products that have been consumed. The collection unit uses sensors inside the refrigerator to monitor the inventory status of food items in real time and sensors inside the pantry to monitor the inventory status of daily necessities. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes consumption patterns based on the collected data and predicts the timing of the next order. The analysis unit can grasp the consumption volume of food and daily necessities and predict the timing of the next order. Step 3: The selection unit selects products based on the data analyzed by the analysis unit. For example, it selects the optimal product based on the user's preferences and budget. The selection unit can prioritize products of a particular brand for users who prefer that brand of milk, and can select the most cost-effective product within the budget. Step 4: The ordering department automatically places orders for the products selected by the selection department. For example, it automatically places orders before necessary products run out. The ordering department can automatically place the next order when the stock of milk or daily necessities is running low. Step 5: The shipping department promptly delivers the goods ordered by the order department. For example, they select the most suitable shipping company and ensure prompt delivery. Step 6: The evaluation department receives user feedback on the products delivered by the delivery department. For example, they use user feedback to improve future orders.

[0137] 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.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, ordering unit, delivery unit, and evaluation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects household consumption data using the sensors of the smart device 14 and transmits it to the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and predicts consumption patterns. The selection unit selects the optimal products based on the user's preferences and budget by the specific processing unit 290 of the data processing unit 12. The ordering unit automatically orders the necessary products by the control unit 46A of the smart device 14. The delivery unit selects the optimal delivery company by the specific processing unit 290 of the data processing unit 12 and ensures prompt delivery. The evaluation unit receives user feedback by the control unit 46A of the smart device 14 and improves the next order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, ordering unit, delivery unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects household consumption data using the sensors of the smart glasses 214 and transmits it to the data processing unit 12. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and predicts consumption patterns. The selection unit selects the optimal products based on the user's preferences and budget using the identification processing unit 290 of the data processing unit 12. The ordering unit automatically orders the necessary products using the control unit 46A of the smart glasses 214. The delivery unit selects the optimal delivery company using the identification processing unit 290 of the data processing unit 12 and ensures prompt delivery. The evaluation unit receives user feedback using the control unit 46A of the smart glasses 214 and improves the next order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, ordering unit, delivery unit, and evaluation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects in-home consumption data using the sensors of the headset terminal 314 and transmits it to the data processing unit 12. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and predicts consumption patterns. The selection unit selects the optimal products based on the user's preferences and budget using the identification processing unit 290 of the data processing unit 12. The ordering unit automatically orders the necessary products using the control unit 46A of the headset terminal 314. The delivery unit selects the optimal delivery company using the identification processing unit 290 of the data processing unit 12 and ensures prompt delivery. The evaluation unit receives user feedback using the control unit 46A of the headset terminal 314 and improves the next order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, ordering unit, delivery unit, and evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects household consumption data using the robot 414's sensors and transmits it to the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and predicts consumption patterns. The selection unit selects the optimal products based on the user's preferences and budget using the specific processing unit 290 of the data processing unit 12. The ordering unit automatically orders the necessary products using the control unit 46A of the robot 414. The delivery unit selects the optimal delivery company using the specific processing unit 290 of the data processing unit 12 and ensures prompt delivery. The evaluation unit receives user feedback using the control unit 46A of the robot 414 and improves the next order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A collection unit that collects household consumption data, An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects products based on the data analyzed by the aforementioned analysis unit, An ordering unit that automatically places an order for the products selected by the aforementioned selection unit, A delivery department that promptly delivers the goods ordered by the aforementioned order department, The system includes an evaluation unit that receives user feedback on products delivered by the delivery unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Sensors are used to monitor the inventory status of refrigerators and pantries, and the consumed items are recorded. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we analyze consumption patterns and predict the timing of the next order. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Select the optimal product based on the user's preferences and budget. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned ordering section is, Automatically place orders for necessary products before you run out. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned delivery department, We select the most suitable shipping carrier and ensure fast delivery. The system described in Appendix 1, characterized by the features described herein. (Note 7) The evaluation unit described above, We will improve your next order based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of consumer data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting household consumption data, we analyze the user's past consumption history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting consumer data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and prioritizes the consumer data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting consumer data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting consumer data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis method of consumption patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing collected data, past consumption patterns are referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing the collected data, different analysis algorithms are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing the collected data, we prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing collected data, we refer to relevant external data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is We estimate user emotions and adjust product selection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is When selecting products, the system refers to the user's past purchase history to select the most suitable product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is When selecting products, different selection algorithms are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is It estimates the user's emotions and determines the priority of products to select based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is When selecting products, the system prioritizes selecting highly relevant products by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is When selecting products, we analyze users' social media activity and select relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering section is, It estimates the user's emotions and adjusts the timing of orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering section is, When ordering a product, the system selects the optimal ordering method by referring to the user's past order history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering section is, When ordering products, we adjust the timing of the order considering the product's stock availability. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering section is, It estimates the user's emotions and determines the priority of items to order based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned ordering section is, When ordering products, the system selects the most suitable delivery carrier by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned ordering section is, When ordering products, the system analyzes the user's social media activity and prioritizes ordering related products. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned delivery department, When shipping products, the system selects the most suitable shipping method by referring to the user's past shipping history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned delivery department, When shipping products, we select the most suitable shipping carrier by referring to the carrier's performance data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned delivery department, The system estimates the user's emotions and determines delivery priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned delivery department, When delivering products, the system selects the optimal delivery route by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned delivery department, When shipping products, we analyze the user's social media activity and provide relevant shipping information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The evaluation unit described above, When evaluating a product, the system selects the most suitable evaluation method by referring to the user's past evaluation history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The evaluation unit described above, When evaluating products, different evaluation algorithms are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 41) The evaluation unit described above, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The evaluation unit described above, When evaluating products, we take the user's geographical location into consideration when determining the priority of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 43) The evaluation unit described above, When evaluating products, we analyze users' social media activity and provide relevant evaluation information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 collection unit that collects household consumption data, An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects products based on the data analyzed by the aforementioned analysis unit, An ordering unit that automatically places an order for the products selected by the aforementioned selection unit, A delivery department that promptly delivers the goods ordered by the aforementioned order department, The system includes an evaluation unit that receives user feedback on products delivered by the delivery unit. A system characterized by the following features.

2. The aforementioned collection unit is Sensors are used to monitor the inventory status of refrigerators and pantries, and the consumed items are recorded. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we analyze consumption patterns and predict the timing of the next order. The system according to feature 1.

4. The aforementioned selection unit is Select the optimal product based on the user's preferences and budget. The system according to feature 1.

5. The aforementioned ordering section is, Automatically place orders for necessary products before you run out. The system according to feature 1.

6. The aforementioned delivery department, We select the most suitable shipping carrier and ensure fast delivery. The system according to feature 1.

7. The evaluation unit, We will improve your next order based on user feedback. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of consumer data collection based on those estimated emotions. The system according to feature 1.

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