system

Abstract

The system according to this embodiment aims to manage inventory and automatically place orders for daily necessities based on the user's purchase history and preferences. [Solution] The system according to the embodiment comprises a learning unit, an ordering unit, a supply unit, a dialogue unit, and a suggestion unit. The learning unit learns the user's purchase history and preferences. The ordering unit manages the inventory of daily necessities and places automatic orders based on the information learned by the learning unit. The supply unit provides the user with information about the products ordered by the ordering unit. The dialogue unit collects new preferences and requests through dialogue with the user. The suggestion unit provides gift suggestions and order support based on the information collected by the dialogue unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, inventory management and automatic ordering of daily necessities based on a user's purchase history and preferences have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform inventory management and automatic ordering of daily necessities based on a user's purchase history and preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, an ordering unit, a supply unit, a dialogue unit, and a suggestion unit. The learning unit learns the user's purchase history and preferences. The ordering unit manages the inventory of daily necessities and places automatic orders based on the information learned by the learning unit. The supply unit provides the user with information about the products ordered by the ordering unit. The dialogue unit collects new preferences and requests through dialogue with the user. The suggestion unit provides gift suggestions and order support based on the information collected by the dialogue unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage inventory and automatically place orders for daily necessities based on the user's purchase history and preferences. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 reception 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 reception 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 personalized automated ordering assistant according to an embodiment of the present invention is an AI solution for reducing the burden of housework for working couples. This personalized automated ordering assistant is an AI agent that learns the user's preferences and purchase history and automatically orders products at the optimal time. The personalized automated ordering assistant has the function of learning the user's purchase history and preferences. The AI ​​analyzes products that the user has purchased in the past, their frequency, and the user's preferences, and predicts the products that will be needed next. For example, if a user purchases detergent on a specific day each month, the AI ​​learns this pattern and predicts the timing of the next purchase. Next, there is an inventory management and automatic ordering function for daily necessities. The AI ​​monitors the user's inventory status and automatically places an order when the inventory is low. This prevents forgetting to buy or running out of stock. For example, if the toilet paper inventory is low, the AI ​​will automatically place an order, saving the user time. Furthermore, there is a dialogue function through a chat interface. The user can communicate their preferences and requests by interacting with the AI. For example, the user can tell the AI, "I would like to add organic detergent to my next order." The AI ​​learns this information and reflects it in the next order. The app also includes gift suggestion and order support features. The AI ​​suggests the optimal gift based on the user's preferences and past gift history. For example, if a user is unsure what to give a friend for their birthday, the AI ​​will suggest a gift that suits their friend's taste and assist with the order. Furthermore, it offers seasonal and trend-based product suggestions. The AI ​​considers the season and current trends to suggest the most suitable products. For instance, it might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. This personalized automated ordering assistant reduces the burden of household chores for working couples, allowing them to manage daily necessities and choose gifts efficiently without being rushed. It also reduces wasteful spending due to unplanned purchases, making household budget management easier. Additionally, understanding user preferences and providing personalized support improves user satisfaction. In summary, the personalized automated ordering assistant reduces the burden of household chores for working couples, enabling them to manage daily necessities and choose gifts efficiently.

[0029] The personalized automated ordering assistant according to this embodiment comprises a learning unit, an ordering unit, a supply unit, a dialogue unit, and a suggestion unit. The learning unit learns the user's purchase history and preferences. For example, the learning unit analyzes products the user has purchased in the past, their frequency, and the user's preferences to predict the next products that will be needed. For example, if the learning unit learns the pattern of purchasing detergent on a specific day each month, it can predict the timing of the next purchase. The learning unit can also generate a list of products that will be needed next based on the user's purchase history. For example, the learning unit analyzes a list of products the user has purchased in the past to predict the timing of the next purchase. The ordering unit manages the inventory of daily necessities and places automatic orders based on the information learned by the learning unit. For example, the ordering unit monitors the user's inventory status and automatically places orders when inventory is low. For example, if the toilet paper inventory is low, the ordering unit can automatically place an order, saving the user time. The ordering unit can also monitor the user's inventory status in real time and automatically order necessary products. For example, the ordering department periodically checks the user's inventory status and automatically places orders when inventory is low. The delivery department provides users with information about the products ordered by the ordering department. For example, the delivery department provides users with detailed information about the ordered products and their delivery status. For example, the delivery department can provide users with detailed information about the ordered products via email or app notifications. The delivery department can also provide users with real-time delivery status of the ordered products. For example, the delivery department displays the delivery status of the ordered products on the app's dashboard. The dialogue department collects new preferences and requests through dialogue with users. For example, the dialogue department interacts with users through a chat interface and collects their preferences and requests. For example, the dialogue department can tell the AI ​​that the user wants to add organic detergent to their next order. The dialogue department can also interact with users through a voice assistant and collect new preferences and requests. For example, the dialogue department can tell the AI ​​that the user wants to add organic detergent to their next order.The suggestion unit provides gift suggestions and order support based on information collected by the dialogue unit. For example, the suggestion unit suggests the most suitable gift based on the user's preferences and past gift history. For instance, if a user is unsure what to give a friend for their birthday, the suggestion unit can suggest a gift that suits the friend's preferences and support the order. The suggestion unit can also suggest products that are best suited to the user, taking into account the season and current trends. For example, in the summer, the suggestion unit might suggest cooling products and sunscreen, and in the winter, it might suggest heating appliances and moisturizing creams. Thus, the personalized automated ordering assistant according to this embodiment can learn the user's purchase history and preferences, manage inventory and place automatic orders for daily necessities, provide product information, collect new preferences and requests through dialogue, and provide gift suggestions and order support.

[0030] The learning unit learns the user's purchase history and preferences. For example, the learning unit analyzes products the user has purchased in the past, their frequency of purchase, and the user's preferences to predict the next products the user will need. Specifically, if a user purchases detergent on a specific day each month, the learning unit can learn this pattern and predict the timing of the next purchase. The learning unit can also generate a list of products the user will need next based on the user's purchase history. For example, the learning unit can analyze a list of products the user has purchased in the past and predict the timing of the next purchase. The learning unit uses AI to analyze the user's purchase patterns and gain a detailed understanding of the user's preferences and purchasing behavior. The AI ​​receives the user's purchase history data as input and analyzes the data using machine learning algorithms. For example, if a user prefers to purchase a particular brand of detergent, the AI ​​will prioritize including that brand of detergent in the list. Also, if a user tends to purchase a particular product during a specific season, the AI ​​will add products appropriate for that season to the list. Furthermore, the learning unit can analyze not only the user's purchase history but also the user's online behavior and social media activity. For example, if a user mentions a specific product on social media, the AI ​​collects that information and incorporates it into the user's preferences. This allows the learning unit to comprehensively analyze the user's purchasing behavior and make more accurate predictions. The learning unit continuously learns the user's purchase history and preferences, generating an optimal product list tailored to the user's needs. This ensures that users don't miss their next purchase opportunity and can obtain the products they need in a timely manner.

[0031] The ordering department manages inventory and automatically places orders for daily necessities based on information learned by the learning department. For example, the ordering department monitors the user's inventory status and automatically places orders when inventory is low. Specifically, the ordering department can monitor the user's inventory status in real time and automatically order necessary products. For example, if the toilet paper inventory is low, the ordering department can automatically place an order, saving the user time and effort. The ordering department also periodically checks the user's inventory status and automatically places orders when inventory is low. The ordering department uses AI to analyze the user's inventory status and place orders at the optimal time. The AI ​​receives the user's inventory data as input and predicts when inventory will be low. For example, if a user uses a certain amount of toilet paper each month, the AI ​​will predict the timing of the next order based on that usage. The ordering department can also use smart sensors to monitor the user's inventory status in real time. The smart sensors detect the user's inventory status and transmit that information to the ordering department. This allows the ordering department to accurately understand the user's inventory status and order necessary products in a timely manner. Furthermore, the ordering department can select the most suitable products by considering the user's purchase history and preferences. For example, if a user prefers a particular brand of toilet paper, the ordering department will prioritize ordering that brand. This allows the ordering department to provide the most suitable products to meet the user's needs and saves the user time and effort.

[0032] The service department provides users with information about products ordered by the ordering department. For example, the service department provides users with detailed information and delivery status of ordered products. Specifically, the service department can provide users with detailed information about ordered products via email or app notifications. The service department can also provide users with real-time delivery status of ordered products. For example, the service department can display the delivery status of ordered products on the app's dashboard. The service department uses AI to provide users with the most relevant information. The AI ​​analyzes the user's purchase history and preferences and prioritizes providing information that is important to the user. For example, if a user is interested in a particular product, the AI ​​will prioritize providing detailed information about that product. The service department can also collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, if a user provides feedback on the information provided, the AI ​​analyzes that feedback and improves the content and format of the information provided. This allows the service department to provide users with the most relevant information and improve user satisfaction. Furthermore, the service department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using a combination of email, app notifications, voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, thereby improving user convenience.

[0033] The dialogue unit collects new preferences and requests through conversations with users. For example, the dialogue unit interacts with users through a chat interface to collect their preferences and requests. Specifically, the dialogue unit can tell the AI ​​that a user would like to add organic detergent to their next order. The dialogue unit can also interact with users through a voice assistant to collect new preferences and requests. For example, the dialogue unit can tell the AI ​​that a user would like to add organic detergent to their next order. The dialogue unit uses AI to analyze conversations with users and understand their preferences and requests in detail. The AI ​​receives user conversation data as input and analyzes the data using natural language processing technology. For example, if a user mentions a specific product, the AI ​​collects that information and reflects it in the user's preferences. Furthermore, the dialogue unit can continuously learn user preferences and requests based on the user's conversation history. This allows the dialogue unit to make optimal suggestions that meet the user's needs. In addition, the dialogue unit can collect user feedback and continuously improve the accuracy and effectiveness of the conversations. For example, when a user provides feedback on a conversation, the AI ​​analyzes that feedback and improves the content and format of the conversation. This allows the conversational unit to provide the most suitable conversation for the user and improve user satisfaction.

[0034] The suggestion department provides gift suggestions and order support based on information collected by the dialogue department. For example, the suggestion department suggests the most suitable gift based on the user's preferences and past gift history. Specifically, if a user is unsure what to give a friend for their birthday, the suggestion department can suggest a gift that suits the friend's preferences and support the order. The suggestion department can also suggest products that are best suited to the user, taking into account the season and current trends. For example, the suggestion department might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. The suggestion department uses AI to analyze the user's preferences and past gift history and suggest the most suitable gift. The AI ​​receives user preference data and past gift history as input and analyzes the data using machine learning algorithms. For example, it analyzes the trends of gifts the user has given in the past and suggests a gift that suits the friend's preferences. The suggestion department can also suggest products that are best suited to the user, taking into account the season and current trends. For example, it might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. This allows the suggestion department to suggest the most suitable gift that meets the user's needs, saving the user time and effort. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user provides feedback on a suggested gift, the AI ​​analyzes that feedback and improves the suggestion. This allows the suggestion department to propose the most suitable gift for the user, thereby increasing user satisfaction.

[0035] The learning unit can analyze a user's past purchase history and preferences to predict the next product they will need. For example, the learning unit can analyze a user's past purchase history to predict the next product they will need. For example, the learning unit can analyze a list of products a user has purchased in the past to predict the timing of their next purchase. The learning unit can also analyze a user's preferences to predict the next product they will need. For example, if a user prefers a particular brand or product category, the learning unit can use that information to predict the next product they will need. In this way, by analyzing a user's past purchase history and preferences, the learning unit can predict the next product they will need. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input a user's past purchase history into a generative AI and have the generative AI predict the next product they will need.

[0036] The ordering department can monitor the user's inventory status and automatically place orders when inventory levels are low. For example, the ordering department can monitor the user's inventory status and automatically place orders when inventory levels are low. For example, if the toilet paper inventory is low, the ordering department can automatically place an order, saving the user time and effort. The ordering department can also monitor the user's inventory status in real time and automatically order necessary products. For example, the ordering department can periodically check the user's inventory status and automatically place orders when inventory levels are low. This prevents users from forgetting to buy items or running out of stock by monitoring their inventory status and automatically placing orders when inventory levels are low. Some or all of the above processes in the ordering department may be performed using, for example, a generation AI, or not using a generation AI. For example, the ordering department can input the user's inventory status into a generation AI and have the generation AI execute automatic orders when inventory levels are low.

[0037] The service provider can provide users with information about ordered products. For example, the service provider can provide users with detailed information about ordered products and their delivery status. For example, the service provider can provide users with detailed information about ordered products via email or app notifications. The service provider can also provide users with real-time delivery status of ordered products. For example, the service provider can display the delivery status of ordered products on the app's dashboard. This allows users to confirm their order details by providing them with information about the ordered products. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input detailed information about ordered products into a generative AI and have the generative AI generate the information to be provided to the user.

[0038] The dialogue unit can collect new preferences and requests through dialogue with the user. For example, the dialogue unit can interact with the user through a chat interface and collect the user's preferences and requests. For example, the dialogue unit can tell the AI ​​that the user wants to add organic detergent to their next order. The dialogue unit can also interact with the user through a voice assistant and collect new preferences and requests. For example, the dialogue unit can tell the AI ​​that the user wants to add organic detergent to their next order by voice. This allows for more personalized support by collecting new preferences and requests through dialogue with the user. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the dialogue unit can input the content of the conversation with the user into a generative AI and have the generative AI collect new preferences and requests.

[0039] The suggestion unit can suggest the most suitable gift based on the user's preferences and past gift history, and support the order process. For example, if a user is unsure what to give a friend for their birthday, the suggestion unit can suggest a gift that suits the friend's taste and support the order. The suggestion unit can also suggest products that are best suited to the user, taking into account the season and current trends. For example, in the summer, the suggestion unit might suggest cooling products and sunscreen, and in the winter, it might suggest heating appliances and moisturizing creams. In this way, by suggesting the most suitable gift based on the user's preferences and past gift history and supporting the order process, the user can choose the appropriate gift. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's preferences and past gift history into a generative AI and have the generative AI suggest the most suitable gift.

[0040] The suggestion unit can suggest the most suitable products to the user, taking into account the season and current trends. For example, the suggestion unit might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. By suggesting the most suitable products to the user, taking into account the season and current trends, the user can purchase products that are in line with the season and current trends. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the season and current trends into a generative AI and have the generative AI suggest the most suitable products.

[0041] The learning unit can analyze a user's past purchase history and predict purchasing patterns related to specific events or seasons. For example, the learning unit can analyze products a user has purchased during the Christmas season in the past and predict what they will need for the next Christmas. It can also analyze products a user has purchased during the summer in the past and predict what they will need for the next summer. Furthermore, it can analyze products a user has purchased for specific events (birthdays, anniversaries, etc.) in the past and predict what they will need for similar events in the future. This allows users to purchase necessary items at the right time by predicting purchasing patterns related to specific events or seasons. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input the user's past purchase history into a generative AI and have the generative AI predict purchasing patterns related to specific events or seasons.

[0042] The learning unit can improve prediction accuracy based on the user's family structure and lifestyle when analyzing purchase history. For example, the learning unit can improve prediction accuracy based on the user's family structure and lifestyle when analyzing purchase history. For example, the learning unit can predict the amount of product needed by considering the user's family structure (whether or not they have children, the number of family members, etc.). The learning unit can also predict appropriate products by considering the user's lifestyle (winter type, indoor type, etc.). Furthermore, the learning unit can predict the optimal timing for purchase by considering the user's daily rhythm (busyness on weekdays, how they spend weekends, etc.). By improving prediction accuracy based on the user's family structure and lifestyle, more accurate product prediction becomes possible. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input information about the user's family structure and lifestyle into a generative AI and have the generative AI perform the improvement of prediction accuracy.

[0043] The learning unit can predict region-specific products by considering the user's geographical location information when analyzing purchase history. For example, the learning unit can predict local specialties and seasonal products in the area where the user lives. It can also predict products that the user might purchase while traveling. Furthermore, the learning unit can predict appropriate products based on the climate and culture of the area where the user lives. By predicting region-specific products while considering the user's geographical location information, it is possible to provide products suitable for the region. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI and have the generative AI perform the process of predicting region-specific products.

[0044] The learning unit can analyze a user's social media activity and predict related products when analyzing purchase history. For example, the learning unit can predict products that a user has "liked" or commented on on social media. It can also predict products that have been featured by influencers that a user follows. Furthermore, the learning unit can predict products related to articles and posts that a user has shared on social media. In this way, related products can be predicted by analyzing a user's social media activity. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user social media activity data into a generative AI and have the generative AI perform the prediction of related products.

[0045] The ordering department can learn the user's consumption rate during inventory management and predict the optimal ordering timing. For example, the ordering department can learn the user's consumption rate during inventory management and predict the optimal ordering timing. For example, the ordering department can analyze the rate at which users have consumed products in the past and predict the timing of the next order. Furthermore, if the user's consumption rate is fast, the ordering department can place an order earlier. Conversely, if the user's consumption rate is slow, the ordering department can place an order at the normal time. In this way, by learning the user's consumption rate, the optimal ordering timing can be predicted. Some or all of the above processing in the ordering department may be performed using, for example, a generative AI, or without a generative AI. For example, the ordering department can input user consumption rate data into a generative AI and have the generative AI predict the optimal ordering timing.

[0046] The ordering department can adjust the frequency of orders based on the user's lifestyle during inventory management. For example, if the user is busy on weekdays, the ordering department can place orders on weekends. Conversely, if the user is busy on weekends, the ordering department can place orders on weekdays. The ordering department can also set the optimal order frequency to match the user's lifestyle. By adjusting the order frequency based on the user's lifestyle, products can be ordered at a more appropriate time. Some or all of the above processing in the ordering department may be performed using, for example, a generative AI, or without a generative AI. For example, the ordering department can input user lifestyle data into a generative AI and have the generative AI perform the order frequency adjustment.

[0047] The order department can select the optimal delivery method when managing inventory, taking into account the user's geographical location. For example, the order department can select the optimal delivery method when managing inventory, taking into account the user's geographical location. For example, the order department can select the optimal delivery method by considering the services of delivery companies in the user's area. Furthermore, if the user is traveling, the order department can select a delivery method to their travel destination. The order department can also select the optimal delivery method by considering the traffic conditions in the user's area. This enables efficient delivery by selecting the optimal delivery method while considering the user's geographical location. Some or all of the above processing in the order department may be performed using, for example, a generative AI, or without a generative AI. For example, the order department can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0048] The ordering department can analyze users' social media activity during inventory management and prioritize the management of inventory for related products. For example, the ordering department can prioritize inventory management for products that users have "liked" or commented on on social media. It can also prioritize inventory management for products introduced by influencers that users follow. Furthermore, the ordering department can prioritize inventory management for products related to articles and posts that users have shared on social media. This allows for the prioritization of inventory management for related products by analyzing users' social media activity. Some or all of the above processing in the ordering department may be performed using, for example, a generative AI, or without a generative AI. For example, the ordering department can input user social media activity data into a generative AI and have the generative AI perform inventory management for related products.

[0049] The information provider can provide optimal information by referring to the user's past purchase history when providing product information. For example, the information provider can provide information on related products based on information on products the user has purchased in the past. The information provider can also provide information on products the user will need next based on the user's past purchase history. Furthermore, the information provider can analyze the user's past purchase history and provide information on the most relevant products. In this way, optimal product information can be provided by referring to the user's past purchase history. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without a generation AI. For example, the information provider can input the user's past purchase history into a generation AI and have the generation AI perform the task of providing optimal information.

[0050] The information provider can adjust the level of detail of product information based on the user's areas of interest when providing product information. For example, the information provider can provide detailed product information in areas of interest to the user. It can also provide concise product information in areas of less interest to the user. Furthermore, the information provider can set the optimal level of detail based on the user's areas of interest. By adjusting the level of detail based on the user's areas of interest, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input user area of ​​interest data into a generative AI and have the generative AI perform the adjustment of the level of detail of the information.

[0051] The information provider can provide region-specific information by considering the user's geographical location when providing product information. For example, the information provider can provide local specialties and seasonal products from the area where the user lives. The information provider can also provide information on products that the user might purchase while traveling. Furthermore, the information provider can provide information on appropriate products based on the climate and culture of the area where the user lives. By providing region-specific information while considering the user's geographical location, it becomes possible to provide information that is appropriate for the region. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the user's geographical location information into a generative AI and have the generative AI perform the provision of region-specific information.

[0052] The information provider can analyze the user's social media activity and provide relevant information when providing product information. For example, the information provider can provide information about products that the user has "liked" or commented on on social media. The information provider can also provide information about products introduced by influencers that the user follows. The information provider can also provide information about products related to articles and posts that the user has shared on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the user's social media activity data into a generative AI and have the generative AI perform the provision of relevant information.

[0053] The dialogue unit can provide the most appropriate response during a conversation by referring to the user's past conversation history. For example, the dialogue unit can provide relevant responses based on questions and requests the user has made in the past. The dialogue unit can also provide responses that reflect the user's preferences and interests based on the user's past conversation history. Furthermore, the dialogue unit can continue the conversation by referring to the content of past conversations the user has had. This allows the dialogue unit to provide the most appropriate response by referring to the user's past conversation history. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's past conversation history into a generative AI and have the generative AI perform the task of providing the most appropriate response.

[0054] The dialogue unit can select a dialogue topic based on the user's areas of interest during a conversation. For example, the dialogue unit can select a dialogue topic based on the user's areas of interest during a conversation. For example, the dialogue unit can select a dialogue topic based on the user's areas of interest (hobbies, work, etc.). The dialogue unit can also select a dialogue topic based on topics the user has shown interest in in the past. Furthermore, the dialogue unit can provide relevant information and suggestions during the conversation based on the user's areas of interest. This makes it possible to have a more appropriate conversation by selecting a dialogue topic based on the user's areas of interest. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input user area of ​​interest data into a generative AI and have the generative AI perform the selection of a dialogue topic.

[0055] The dialogue unit can provide region-specific topics during a conversation, taking into account the user's geographical location. For example, the dialogue unit can discuss events and news in the area where the user lives. If the user is traveling, the dialogue unit can discuss tourist information and recommended spots in their travel destination. The dialogue unit can also provide relevant topics based on the climate and culture of the area where the user lives. By providing region-specific topics while considering the user's geographical location, it becomes possible to have a conversation appropriate to the region. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's geographical location information into a generative AI and have the generative AI provide region-specific topics.

[0056] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can discuss topics that the user has "liked" or commented on on social media. It can also discuss topics introduced by influencers that the user follows. Furthermore, the dialogue unit can provide topics related to articles and posts that the user has shared on social media. In this way, relevant topics can be provided by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's social media activity data into a generative AI and have the generative AI provide relevant topics.

[0057] The suggestion unit can make optimal gift suggestions by referring to the user's past gift history. For example, the suggestion unit can suggest relevant gifts based on the user's past gift history. The suggestion unit can also predict the next gift to give based on the user's past gift history. Furthermore, the suggestion unit can analyze the user's past gift history and suggest the most relevant gift. This makes it possible to make optimal gift suggestions by referring to the user's past gift history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past gift history into a generative AI and have the generative AI execute the optimal gift suggestion.

[0058] The suggestion unit can adjust the level of detail of gift suggestions based on the user's areas of interest. For example, the suggestion unit can suggest gifts in detail in areas the user is interested in. It can also suggest gifts in areas the user is not very interested in in a concise manner. Furthermore, the suggestion unit can set the optimal level of detail for suggestions based on the user's areas of interest. This allows for more appropriate gift suggestions by adjusting the level of detail based on the user's areas of interest. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input user area of ​​interest data into a generative AI and have the generative AI adjust the level of detail of the suggestions.

[0059] The suggestion unit can suggest region-specific gifts by considering the user's geographical location when suggesting gifts. For example, the suggestion unit can suggest local specialties or seasonal products from the area where the user lives. It can also suggest gifts that the user might purchase while traveling. Furthermore, the suggestion unit can suggest appropriate gifts based on the climate and culture of the area where the user lives. By suggesting region-specific gifts while considering the user's geographical location, it becomes possible to suggest gifts that are appropriate for the region. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI execute region-specific gift suggestions.

[0060] The suggestion unit can analyze the user's social media activity when suggesting gifts and propose relevant gifts. For example, the suggestion unit can suggest gifts that the user has liked or commented on on social media. It can also suggest gifts that have been introduced by influencers that the user follows. Furthermore, the suggestion unit can suggest gifts related to articles or posts that the user has shared on social media. In this way, relevant gifts can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI and have the generative AI execute the suggestion of relevant gifts.

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

[0062] The personalized automated ordering assistant can also monitor the user's health and suggest health-related products. For example, if the user uses a fitness tracker, it can analyze the data and suggest appropriate supplements or health foods. It can also suggest products that align with the user's specific health goals (e.g., weight loss or muscle building). Furthermore, it can support users in scheduling regular health checkups and medical appointments based on their health status. This helps users manage their health and lead healthier lives.

[0063] A personalized automated ordering assistant can suggest environmentally friendly eco-products based on a user's purchase history. For example, if a user frequently buys plastic products, it can suggest reusable eco-bags or stainless steel bottles. When a user purchases detergent, it can suggest eco-friendly detergents made with environmentally conscious ingredients. Furthermore, when a user purchases food, it can suggest organic or fair trade products. This allows users to contribute to environmental protection through their purchasing behavior.

[0064] A personalized automated ordering assistant can suggest products and services that help users save money based on their purchase history. For example, it can suggest bulk purchase or subscription discounts for items the user frequently buys. It can also suggest energy-efficient products when the user is buying electrical appliances. Furthermore, if the user is planning a trip, it can provide information on discounted flights and accommodations. In this way, it can support users in saving money through their purchasing behavior and help them manage their household finances.

[0065] A personalized automated ordering assistant can suggest products related to a user's hobbies and interests based on their purchase history. For example, if a user frequently buys gardening supplies, it can suggest new plants and gardening tools. If a user is interested in cooking, it can suggest new recipe books and cooking utensils. Furthermore, if a user enjoys sports, it can suggest new sports equipment and fitness goods. This allows for product suggestions tailored to the user's hobbies and interests, enriching their life.

[0066] A personalized automated ordering assistant can suggest products related to home safety based on a user's purchase history. For example, if a user has purchased security cameras, it can suggest additional security devices and services. If a user has young children, it can suggest baby gates and safety locks to protect their children. Furthermore, if a user lives with elderly people, it can suggest emergency call systems and accessibility products. This enhances the safety of the user's home and provides a safer living environment.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The learning unit learns the user's purchase history and preferences. For example, it analyzes products the user has purchased in the past, their frequency of purchase, and their preferences to predict what products will be needed next. If a user purchases detergent on a specific day each month, the learning unit can learn this pattern and predict the timing of the next purchase. It can also generate a list of products that will be needed next based on the user's purchase history. Step 2: The ordering unit manages inventory and automatically places orders for daily necessities based on the information learned by the learning unit. For example, it monitors the user's inventory status and automatically places orders when inventory levels are low. The ordering unit can automatically place an order when toilet paper inventory is low, saving the user time and effort. It can also monitor the user's inventory status in real time and automatically order necessary products. Step 3: The delivery department provides users with information about the products ordered by the ordering department. For example, it provides users with detailed information about the ordered products and their delivery status. The delivery department can provide users with detailed information about the ordered products via email or app notifications. It can also provide users with real-time delivery status of the ordered products. For example, the delivery department can display the delivery status of the ordered products on the app's dashboard. Step 4: The dialogue unit collects new preferences and requests through interaction with the user. For example, it interacts with the user through a chat interface to collect user preferences and requests. The dialogue unit can tell the AI ​​requests such as, "I'd like to add organic detergent to my next order." It can also interact with the user through a voice assistant to collect new preferences and requests. For example, the user can communicate requests such as, "I'd like to add organic detergent to my next order" by voice. Step 5: The suggestion department provides gift suggestions and order support based on the information collected by the dialogue department. For example, it suggests the most suitable gift based on the user's preferences and past gift history. If a user is unsure what to give a friend for their birthday, the suggestion department can suggest a gift that suits the friend's taste and support the order. It can also suggest products that are best suited to the user, taking into account the season and current trends. For example, it might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter.

[0069] (Example of form 2) The personalized automated ordering assistant according to an embodiment of the present invention is an AI solution for reducing the burden of housework for working couples. This personalized automated ordering assistant is an AI agent that learns the user's preferences and purchase history and automatically orders products at the optimal time. The personalized automated ordering assistant has the function of learning the user's purchase history and preferences. The AI ​​analyzes products that the user has purchased in the past, their frequency, and the user's preferences, and predicts the products that will be needed next. For example, if a user purchases detergent on a specific day each month, the AI ​​learns this pattern and predicts the timing of the next purchase. Next, there is an inventory management and automatic ordering function for daily necessities. The AI ​​monitors the user's inventory status and automatically places an order when the inventory is low. This prevents forgetting to buy or running out of stock. For example, if the toilet paper inventory is low, the AI ​​will automatically place an order, saving the user time. Furthermore, there is a dialogue function through a chat interface. The user can communicate their preferences and requests by interacting with the AI. For example, the user can tell the AI, "I would like to add organic detergent to my next order." The AI ​​learns this information and reflects it in the next order. The app also includes gift suggestion and order support features. The AI ​​suggests the optimal gift based on the user's preferences and past gift history. For example, if a user is unsure what to give a friend for their birthday, the AI ​​will suggest a gift that suits their friend's taste and assist with the order. Furthermore, it offers seasonal and trend-based product suggestions. The AI ​​considers the season and current trends to suggest the most suitable products. For instance, it might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. This personalized automated ordering assistant reduces the burden of household chores for working couples, allowing them to manage daily necessities and choose gifts efficiently without being rushed. It also reduces wasteful spending due to unplanned purchases, making household budget management easier. Additionally, understanding user preferences and providing personalized support improves user satisfaction. In summary, the personalized automated ordering assistant reduces the burden of household chores for working couples, enabling them to manage daily necessities and choose gifts efficiently.

[0070] The personalized automated ordering assistant according to this embodiment comprises a learning unit, an ordering unit, a supply unit, a dialogue unit, and a suggestion unit. The learning unit learns the user's purchase history and preferences. For example, the learning unit analyzes products the user has purchased in the past, their frequency, and the user's preferences to predict the next products that will be needed. For example, if the learning unit learns the pattern of purchasing detergent on a specific day each month, it can predict the timing of the next purchase. The learning unit can also generate a list of products that will be needed next based on the user's purchase history. For example, the learning unit analyzes a list of products the user has purchased in the past to predict the timing of the next purchase. The ordering unit manages the inventory of daily necessities and places automatic orders based on the information learned by the learning unit. For example, the ordering unit monitors the user's inventory status and automatically places orders when inventory is low. For example, if the toilet paper inventory is low, the ordering unit can automatically place an order, saving the user time. The ordering unit can also monitor the user's inventory status in real time and automatically order necessary products. For example, the ordering department periodically checks the user's inventory status and automatically places orders when inventory is low. The delivery department provides users with information about the products ordered by the ordering department. For example, the delivery department provides users with detailed information about the ordered products and their delivery status. For example, the delivery department can provide users with detailed information about the ordered products via email or app notifications. The delivery department can also provide users with real-time delivery status of the ordered products. For example, the delivery department displays the delivery status of the ordered products on the app's dashboard. The dialogue department collects new preferences and requests through dialogue with users. For example, the dialogue department interacts with users through a chat interface and collects their preferences and requests. For example, the dialogue department can tell the AI ​​that the user wants to add organic detergent to their next order. The dialogue department can also interact with users through a voice assistant and collect new preferences and requests. For example, the dialogue department can tell the AI ​​that the user wants to add organic detergent to their next order.The suggestion unit provides gift suggestions and order support based on information collected by the dialogue unit. For example, the suggestion unit suggests the most suitable gift based on the user's preferences and past gift history. For instance, if a user is unsure what to give a friend for their birthday, the suggestion unit can suggest a gift that suits the friend's preferences and support the order. The suggestion unit can also suggest products that are best suited to the user, taking into account the season and current trends. For example, in the summer, the suggestion unit might suggest cooling products and sunscreen, and in the winter, it might suggest heating appliances and moisturizing creams. Thus, the personalized automated ordering assistant according to this embodiment can learn the user's purchase history and preferences, manage inventory and place automatic orders for daily necessities, provide product information, collect new preferences and requests through dialogue, and provide gift suggestions and order support.

[0071] The learning unit learns the user's purchase history and preferences. For example, the learning unit analyzes products the user has purchased in the past, their frequency of purchase, and the user's preferences to predict the next products the user will need. Specifically, if a user purchases detergent on a specific day each month, the learning unit can learn this pattern and predict the timing of the next purchase. The learning unit can also generate a list of products the user will need next based on the user's purchase history. For example, the learning unit can analyze a list of products the user has purchased in the past and predict the timing of the next purchase. The learning unit uses AI to analyze the user's purchase patterns and gain a detailed understanding of the user's preferences and purchasing behavior. The AI ​​receives the user's purchase history data as input and analyzes the data using machine learning algorithms. For example, if a user prefers to purchase a particular brand of detergent, the AI ​​will prioritize including that brand of detergent in the list. Also, if a user tends to purchase a particular product during a specific season, the AI ​​will add products appropriate for that season to the list. Furthermore, the learning unit can analyze not only the user's purchase history but also the user's online behavior and social media activity. For example, if a user mentions a specific product on social media, the AI ​​collects that information and incorporates it into the user's preferences. This allows the learning unit to comprehensively analyze the user's purchasing behavior and make more accurate predictions. The learning unit continuously learns the user's purchase history and preferences, generating an optimal product list tailored to the user's needs. This ensures that users don't miss their next purchase opportunity and can obtain the products they need in a timely manner.

[0072] The ordering department manages inventory and automatically places orders for daily necessities based on information learned by the learning department. For example, the ordering department monitors the user's inventory status and automatically places orders when inventory is low. Specifically, the ordering department can monitor the user's inventory status in real time and automatically order necessary products. For example, if the toilet paper inventory is low, the ordering department can automatically place an order, saving the user time and effort. The ordering department also periodically checks the user's inventory status and automatically places orders when inventory is low. The ordering department uses AI to analyze the user's inventory status and place orders at the optimal time. The AI ​​receives the user's inventory data as input and predicts when inventory will be low. For example, if a user uses a certain amount of toilet paper each month, the AI ​​will predict the timing of the next order based on that usage. The ordering department can also use smart sensors to monitor the user's inventory status in real time. The smart sensors detect the user's inventory status and transmit that information to the ordering department. This allows the ordering department to accurately understand the user's inventory status and order necessary products in a timely manner. Furthermore, the ordering department can select the most suitable products by considering the user's purchase history and preferences. For example, if a user prefers a particular brand of toilet paper, the ordering department will prioritize ordering that brand. This allows the ordering department to provide the most suitable products to meet the user's needs and saves the user time and effort.

[0073] The service department provides users with information about products ordered by the ordering department. For example, the service department provides users with detailed information and delivery status of ordered products. Specifically, the service department can provide users with detailed information about ordered products via email or app notifications. The service department can also provide users with real-time delivery status of ordered products. For example, the service department can display the delivery status of ordered products on the app's dashboard. The service department uses AI to provide users with the most relevant information. The AI ​​analyzes the user's purchase history and preferences and prioritizes providing information that is important to the user. For example, if a user is interested in a particular product, the AI ​​will prioritize providing detailed information about that product. The service department can also collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, if a user provides feedback on the information provided, the AI ​​analyzes that feedback and improves the content and format of the information provided. This allows the service department to provide users with the most relevant information and improve user satisfaction. Furthermore, the service department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using a combination of email, app notifications, voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, thereby improving user convenience.

[0074] The dialogue unit collects new preferences and requests through conversations with users. For example, the dialogue unit interacts with users through a chat interface to collect their preferences and requests. Specifically, the dialogue unit can tell the AI ​​that a user would like to add organic detergent to their next order. The dialogue unit can also interact with users through a voice assistant to collect new preferences and requests. For example, the dialogue unit can tell the AI ​​that a user would like to add organic detergent to their next order. The dialogue unit uses AI to analyze conversations with users and understand their preferences and requests in detail. The AI ​​receives user conversation data as input and analyzes the data using natural language processing technology. For example, if a user mentions a specific product, the AI ​​collects that information and reflects it in the user's preferences. Furthermore, the dialogue unit can continuously learn user preferences and requests based on the user's conversation history. This allows the dialogue unit to make optimal suggestions that meet the user's needs. In addition, the dialogue unit can collect user feedback and continuously improve the accuracy and effectiveness of the conversations. For example, when a user provides feedback on a conversation, the AI ​​analyzes that feedback and improves the content and format of the conversation. This allows the conversational unit to provide the most suitable conversation for the user and improve user satisfaction.

[0075] The suggestion department provides gift suggestions and order support based on information collected by the dialogue department. For example, the suggestion department suggests the most suitable gift based on the user's preferences and past gift history. Specifically, if a user is unsure what to give a friend for their birthday, the suggestion department can suggest a gift that suits the friend's preferences and support the order. The suggestion department can also suggest products that are best suited to the user, taking into account the season and current trends. For example, the suggestion department might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. The suggestion department uses AI to analyze the user's preferences and past gift history and suggest the most suitable gift. The AI ​​receives user preference data and past gift history as input and analyzes the data using machine learning algorithms. For example, it analyzes the trends of gifts the user has given in the past and suggests a gift that suits the friend's preferences. The suggestion department can also suggest products that are best suited to the user, taking into account the season and current trends. For example, it might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. This allows the suggestion department to suggest the most suitable gift that meets the user's needs, saving the user time and effort. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user provides feedback on a suggested gift, the AI ​​analyzes that feedback and improves the suggestion. This allows the suggestion department to propose the most suitable gift for the user, thereby increasing user satisfaction.

[0076] The learning unit can analyze a user's past purchase history and preferences to predict the next product they will need. For example, the learning unit can analyze a user's past purchase history to predict the next product they will need. For example, the learning unit can analyze a list of products a user has purchased in the past to predict the timing of their next purchase. The learning unit can also analyze a user's preferences to predict the next product they will need. For example, if a user prefers a particular brand or product category, the learning unit can use that information to predict the next product they will need. In this way, by analyzing a user's past purchase history and preferences, the learning unit can predict the next product they will need. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input a user's past purchase history into a generative AI and have the generative AI predict the next product they will need.

[0077] The ordering department can monitor the user's inventory status and automatically place orders when inventory levels are low. For example, the ordering department can monitor the user's inventory status and automatically place orders when inventory levels are low. For example, if the toilet paper inventory is low, the ordering department can automatically place an order, saving the user time and effort. The ordering department can also monitor the user's inventory status in real time and automatically order necessary products. For example, the ordering department can periodically check the user's inventory status and automatically place orders when inventory levels are low. This prevents users from forgetting to buy items or running out of stock by monitoring their inventory status and automatically placing orders when inventory levels are low. Some or all of the above processes in the ordering department may be performed using, for example, a generation AI, or not using a generation AI. For example, the ordering department can input the user's inventory status into a generation AI and have the generation AI execute automatic orders when inventory levels are low.

[0078] The service provider can provide users with information about ordered products. For example, the service provider can provide users with detailed information about ordered products and their delivery status. For example, the service provider can provide users with detailed information about ordered products via email or app notifications. The service provider can also provide users with real-time delivery status of ordered products. For example, the service provider can display the delivery status of ordered products on the app's dashboard. This allows users to confirm their order details by providing them with information about the ordered products. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input detailed information about ordered products into a generative AI and have the generative AI generate the information to be provided to the user.

[0079] The dialogue unit can collect new preferences and requests through dialogue with the user. For example, the dialogue unit can interact with the user through a chat interface and collect the user's preferences and requests. For example, the dialogue unit can tell the AI ​​that the user wants to add organic detergent to their next order. The dialogue unit can also interact with the user through a voice assistant and collect new preferences and requests. For example, the dialogue unit can tell the AI ​​that the user wants to add organic detergent to their next order by voice. This allows for more personalized support by collecting new preferences and requests through dialogue with the user. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the dialogue unit can input the content of the conversation with the user into a generative AI and have the generative AI collect new preferences and requests.

[0080] The suggestion unit can suggest the most suitable gift based on the user's preferences and past gift history, and support the order process. For example, if a user is unsure what to give a friend for their birthday, the suggestion unit can suggest a gift that suits the friend's taste and support the order. The suggestion unit can also suggest products that are best suited to the user, taking into account the season and current trends. For example, in the summer, the suggestion unit might suggest cooling products and sunscreen, and in the winter, it might suggest heating appliances and moisturizing creams. In this way, by suggesting the most suitable gift based on the user's preferences and past gift history and supporting the order process, the user can choose the appropriate gift. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's preferences and past gift history into a generative AI and have the generative AI suggest the most suitable gift.

[0081] The suggestion unit can suggest the most suitable products to the user, taking into account the season and current trends. For example, the suggestion unit might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter. By suggesting the most suitable products to the user, taking into account the season and current trends, the user can purchase products that are in line with the season and current trends. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the season and current trends into a generative AI and have the generative AI suggest the most suitable products.

[0082] The learning unit can estimate the user's emotions and adjust the purchase history analysis method based on the estimated user emotions. For example, if the user is stressed, the learning unit can use a simple analysis method to extract only the important purchase patterns. If the user is relaxed, the learning unit can use a detailed analysis method to consider even the smallest purchase patterns. If the user is in a hurry, the learning unit can perform a rapid analysis and prioritize the extraction of the main purchase patterns. By adjusting the purchase history analysis method based on the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI adjust the method for analyzing purchase history.

[0083] The learning unit can analyze a user's past purchase history and predict purchasing patterns related to specific events or seasons. For example, the learning unit can analyze products a user has purchased during the Christmas season in the past and predict what they will need for the next Christmas. It can also analyze products a user has purchased during the summer in the past and predict what they will need for the next summer. Furthermore, it can analyze products a user has purchased for specific events (birthdays, anniversaries, etc.) in the past and predict what they will need for similar events in the future. This allows users to purchase necessary items at the right time by predicting purchasing patterns related to specific events or seasons. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input the user's past purchase history into a generative AI and have the generative AI predict purchasing patterns related to specific events or seasons.

[0084] The learning unit can improve prediction accuracy based on the user's family structure and lifestyle when analyzing purchase history. For example, the learning unit can improve prediction accuracy based on the user's family structure and lifestyle when analyzing purchase history. For example, the learning unit can predict the amount of product needed by considering the user's family structure (whether or not they have children, the number of family members, etc.). The learning unit can also predict appropriate products by considering the user's lifestyle (winter type, indoor type, etc.). Furthermore, the learning unit can predict the optimal timing for purchase by considering the user's daily rhythm (busyness on weekdays, how they spend weekends, etc.). By improving prediction accuracy based on the user's family structure and lifestyle, more accurate product prediction becomes possible. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input information about the user's family structure and lifestyle into a generative AI and have the generative AI perform the improvement of prediction accuracy.

[0085] The learning unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of the next items the user will need. For example, if the user is stressed, the learning unit may prioritize items with a relaxing effect. If the user is relaxed, the learning unit may prioritize items that are needed on a daily basis. If the user is in a hurry, the learning unit may prioritize items that are needed immediately. In this way, by determining the priority of the next items the user will need based on their emotions, products that meet the user's needs can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI determine the priority of the next items the user will need.

[0086] The learning unit can predict region-specific products by considering the user's geographical location information when analyzing purchase history. For example, the learning unit can predict local specialties and seasonal products in the area where the user lives. It can also predict products that the user might purchase while traveling. Furthermore, the learning unit can predict appropriate products based on the climate and culture of the area where the user lives. By predicting region-specific products while considering the user's geographical location information, it is possible to provide products suitable for the region. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI and have the generative AI perform the process of predicting region-specific products.

[0087] The learning unit can analyze a user's social media activity and predict related products when analyzing purchase history. For example, the learning unit can predict products that a user has "liked" or commented on on social media. It can also predict products that have been featured by influencers that a user follows. Furthermore, the learning unit can predict products related to articles and posts that a user has shared on social media. In this way, related products can be predicted by analyzing a user's social media activity. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user social media activity data into a generative AI and have the generative AI perform the prediction of related products.

[0088] The ordering system can estimate the user's emotions and adjust the timing of automated orders based on those emotions. For example, if the user is stressed, the ordering system can place an automated order earlier to provide reassurance. If the user is relaxed, the ordering system can place an automated order at the normal time. If the user is in a hurry, the ordering system can place an automated order quickly to deliver the necessary goods sooner. By adjusting the timing of automated orders based on the user's emotions, products can be ordered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using or without a generative AI. For example, the ordering system can input user emotion data into a generative AI and have the generative AI adjust the timing of automated orders.

[0089] The ordering department can learn the user's consumption rate during inventory management and predict the optimal ordering timing. For example, the ordering department can learn the user's consumption rate during inventory management and predict the optimal ordering timing. For example, the ordering department can analyze the rate at which users have consumed products in the past and predict the timing of the next order. Furthermore, if the user's consumption rate is fast, the ordering department can place an order earlier. Conversely, if the user's consumption rate is slow, the ordering department can place an order at the normal time. In this way, by learning the user's consumption rate, the optimal ordering timing can be predicted. Some or all of the above processing in the ordering department may be performed using, for example, a generative AI, or without a generative AI. For example, the ordering department can input user consumption rate data into a generative AI and have the generative AI predict the optimal ordering timing.

[0090] The ordering department can adjust the frequency of orders based on the user's lifestyle during inventory management. For example, if the user is busy on weekdays, the ordering department can place orders on weekends. Conversely, if the user is busy on weekends, the ordering department can place orders on weekdays. The ordering department can also set the optimal order frequency to match the user's lifestyle. By adjusting the order frequency based on the user's lifestyle, products can be ordered at a more appropriate time. Some or all of the above processing in the ordering department may be performed using, for example, a generative AI, or without a generative AI. For example, the ordering department can input user lifestyle data into a generative AI and have the generative AI perform the order frequency adjustment.

[0091] The ordering system can estimate the user's emotions and determine the priority of automated orders based on those emotions. For example, if the user is stressed, the ordering system will prioritize orders for important items. If the user is relaxed, the ordering system can place orders with normal priority. If the user is in a hurry, the ordering system can also prioritize orders for items needed immediately. This allows for prioritizing orders based on the user's emotions, ensuring that products that meet the user's needs are prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using, for example, generative AI, or not. For example, the ordering system can input user emotion data into a generative AI and have the generative AI determine the priority of automated orders.

[0092] The order department can select the optimal delivery method when managing inventory, taking into account the user's geographical location. For example, the order department can select the optimal delivery method when managing inventory, taking into account the user's geographical location. For example, the order department can select the optimal delivery method by considering the services of delivery companies in the user's area. Furthermore, if the user is traveling, the order department can select a delivery method to their travel destination. The order department can also select the optimal delivery method by considering the traffic conditions in the user's area. This enables efficient delivery by selecting the optimal delivery method while considering the user's geographical location. Some or all of the above processing in the order department may be performed using, for example, a generative AI, or without a generative AI. For example, the order department can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0093] The ordering department can analyze users' social media activity during inventory management and prioritize the management of inventory for related products. For example, the ordering department can prioritize inventory management for products that users have "liked" or commented on on social media. It can also prioritize inventory management for products introduced by influencers that users follow. Furthermore, the ordering department can prioritize inventory management for products related to articles and posts that users have shared on social media. This allows for the prioritization of inventory management for related products by analyzing users' social media activity. Some or all of the above processing in the ordering department may be performed using, for example, a generative AI, or without a generative AI. For example, the ordering department can input user social media activity data into a generative AI and have the generative AI perform inventory management for related products.

[0094] The service provider can estimate the user's emotions and adjust the way product information is presented based on those emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible information presentation. If the user is relaxed, the service provider can provide a more detailed information presentation. If the user is in a hurry, the service provider can provide a concise information presentation. By adjusting the product information presentation based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the information presentation method.

[0095] The information provider can provide optimal information by referring to the user's past purchase history when providing product information. For example, the information provider can provide information on related products based on information on products the user has purchased in the past. The information provider can also provide information on products the user will need next based on the user's past purchase history. Furthermore, the information provider can analyze the user's past purchase history and provide information on the most relevant products. In this way, optimal product information can be provided by referring to the user's past purchase history. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without a generation AI. For example, the information provider can input the user's past purchase history into a generation AI and have the generation AI perform the task of providing optimal information.

[0096] The information provider can adjust the level of detail of product information based on the user's areas of interest when providing product information. For example, the information provider can provide detailed product information in areas of interest to the user. It can also provide concise product information in areas of less interest to the user. Furthermore, the information provider can set the optimal level of detail based on the user's areas of interest. By adjusting the level of detail based on the user's areas of interest, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input user area of ​​interest data into a generative AI and have the generative AI perform the adjustment of the level of detail of the information.

[0097] The service provider can estimate the user's emotions and determine the priority of product information provision based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing information on important products. If the user is relaxed, the service provider can provide information with normal priorities. If the user is in a hurry, the service provider can also prioritize providing information on products that are immediately needed. This allows for information provision tailored to the user's needs by determining the priority of product information provision based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of information provision.

[0098] The information provider can provide region-specific information by considering the user's geographical location when providing product information. For example, the information provider can provide local specialties and seasonal products from the area where the user lives. The information provider can also provide information on products that the user might purchase while traveling. Furthermore, the information provider can provide information on appropriate products based on the climate and culture of the area where the user lives. By providing region-specific information while considering the user's geographical location, it becomes possible to provide information that is appropriate for the region. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the user's geographical location information into a generative AI and have the generative AI perform the provision of region-specific information.

[0099] The information provider can analyze the user's social media activity and provide relevant information when providing product information. For example, the information provider can provide information about products that the user has "liked" or commented on on social media. The information provider can also provide information about products introduced by influencers that the user follows. The information provider can also provide information about products related to articles and posts that the user has shared on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the user's social media activity data into a generative AI and have the generative AI perform the provision of relevant information.

[0100] The dialogue unit can estimate the user's emotions and adjust the content and tone of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit can use a calm tone. If the user is relaxed, the dialogue unit can use a bright tone. If the user is in a hurry, the dialogue unit can use a quick and concise tone. By adjusting the content and tone of the dialogue based on the user's emotions, a more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI adjust the content and tone of the dialogue.

[0101] The dialogue unit can provide the most appropriate response during a conversation by referring to the user's past conversation history. For example, the dialogue unit can provide relevant responses based on questions and requests the user has made in the past. The dialogue unit can also provide responses that reflect the user's preferences and interests based on the user's past conversation history. Furthermore, the dialogue unit can continue the conversation by referring to the content of past conversations the user has had. This allows the dialogue unit to provide the most appropriate response by referring to the user's past conversation history. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's past conversation history into a generative AI and have the generative AI perform the task of providing the most appropriate response.

[0102] The dialogue unit can select a dialogue topic based on the user's areas of interest during a conversation. For example, the dialogue unit can select a dialogue topic based on the user's areas of interest during a conversation. For example, the dialogue unit can select a dialogue topic based on the user's areas of interest (hobbies, work, etc.). The dialogue unit can also select a dialogue topic based on topics the user has shown interest in in the past. Furthermore, the dialogue unit can provide relevant information and suggestions during the conversation based on the user's areas of interest. This makes it possible to have a more appropriate conversation by selecting a dialogue topic based on the user's areas of interest. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input user area of ​​interest data into a generative AI and have the generative AI perform the selection of a dialogue topic.

[0103] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit will prioritize important dialogues. If the user is relaxed, the dialogue unit can proceed with dialogues at normal priority. If the user is in a hurry, the dialogue unit can also proceed quickly and provide necessary information. This enables dialogues that meet the user's needs by determining the priority of dialogues based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI determine the priority of the dialogues.

[0104] The dialogue unit can provide region-specific topics during a conversation, taking into account the user's geographical location. For example, the dialogue unit can discuss events and news in the area where the user lives. If the user is traveling, the dialogue unit can discuss tourist information and recommended spots in their travel destination. The dialogue unit can also provide relevant topics based on the climate and culture of the area where the user lives. By providing region-specific topics while considering the user's geographical location, it becomes possible to have a conversation appropriate to the region. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's geographical location information into a generative AI and have the generative AI provide region-specific topics.

[0105] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can discuss topics that the user has "liked" or commented on on social media. It can also discuss topics introduced by influencers that the user follows. Furthermore, the dialogue unit can provide topics related to articles and posts that the user has shared on social media. In this way, relevant topics can be provided by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's social media activity data into a generative AI and have the generative AI provide relevant topics.

[0106] The suggestion unit can estimate the user's emotions and adjust the gift suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a simple and visually clear gift suggestion method. If the user is relaxed, the suggestion unit can provide a gift suggestion method that includes detailed information. If the user is in a hurry, the suggestion unit can provide a concise gift suggestion method. By adjusting the gift suggestion method based on the user's emotions, more appropriate gift suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the gift suggestion method.

[0107] The suggestion unit can make optimal gift suggestions by referring to the user's past gift history. For example, the suggestion unit can suggest relevant gifts based on the user's past gift history. The suggestion unit can also predict the next gift to give based on the user's past gift history. Furthermore, the suggestion unit can analyze the user's past gift history and suggest the most relevant gift. This makes it possible to make optimal gift suggestions by referring to the user's past gift history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past gift history into a generative AI and have the generative AI execute the optimal gift suggestion.

[0108] The suggestion unit can adjust the level of detail of gift suggestions based on the user's areas of interest. For example, the suggestion unit can suggest gifts in detail in areas the user is interested in. It can also suggest gifts in areas the user is not very interested in in a concise manner. Furthermore, the suggestion unit can set the optimal level of detail for suggestions based on the user's areas of interest. This allows for more appropriate gift suggestions by adjusting the level of detail based on the user's areas of interest. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input user area of ​​interest data into a generative AI and have the generative AI adjust the level of detail of the suggestions.

[0109] The suggestion unit can estimate the user's emotions and determine the priority of gift suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important gift suggestions. If the user is relaxed, the suggestion unit can offer gift suggestions with normal priority. If the user is in a hurry, the suggestion unit can also prioritize gifts that are needed immediately. This allows for gift suggestions tailored to the user's needs by prioritizing gift suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of gift suggestions.

[0110] The suggestion unit can suggest region-specific gifts by considering the user's geographical location when suggesting gifts. For example, the suggestion unit can suggest local specialties or seasonal products from the area where the user lives. It can also suggest gifts that the user might purchase while traveling. Furthermore, the suggestion unit can suggest appropriate gifts based on the climate and culture of the area where the user lives. By suggesting region-specific gifts while considering the user's geographical location, it becomes possible to suggest gifts that are appropriate for the region. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI execute region-specific gift suggestions.

[0111] The suggestion unit can analyze the user's social media activity when suggesting gifts and propose relevant gifts. For example, the suggestion unit can suggest gifts that the user has liked or commented on on social media. It can also suggest gifts that have been introduced by influencers that the user follows. Furthermore, the suggestion unit can suggest gifts related to articles or posts that the user has shared on social media. In this way, relevant gifts can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI and have the generative AI execute the suggestion of relevant gifts.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The personalized automated ordering assistant can also monitor the user's health and suggest health-related products. For example, if the user uses a fitness tracker, it can analyze the data and suggest appropriate supplements or health foods. It can also suggest products that align with the user's specific health goals (e.g., weight loss or muscle building). Furthermore, it can support users in scheduling regular health checkups and medical appointments based on their health status. This helps users manage their health and lead healthier lives.

[0114] A personalized automated ordering assistant can estimate a user's emotions and suggest relaxing products based on those emotions. For example, if a user is stressed, it can suggest relaxing aromatherapy oils or bath salts. If a user is tired, it can suggest refreshing beverages or supplements. Furthermore, if a user is relaxed, it can suggest yoga mats or meditation equipment to maintain that relaxation. This allows for product suggestions tailored to the user's emotions, supporting their mental and physical well-being.

[0115] A personalized automated ordering assistant can suggest environmentally friendly eco-products based on a user's purchase history. For example, if a user frequently buys plastic products, it can suggest reusable eco-bags or stainless steel bottles. When a user purchases detergent, it can suggest eco-friendly detergents made with environmentally conscious ingredients. Furthermore, when a user purchases food, it can suggest organic or fair trade products. This allows users to contribute to environmental protection through their purchasing behavior.

[0116] A personalized automated ordering assistant can estimate a user's emotions and suggest entertainment-related products based on those emotions. For example, if a user is feeling stressed, it can suggest relaxing movies or music. If a user is bored, it can suggest products related to new hobbies or activities. Furthermore, if a user is having fun, it can suggest games or event tickets to further enhance that enjoyment. This allows for entertainment suggestions tailored to the user's emotions, enriching their lives.

[0117] A personalized automated ordering assistant can suggest products and services that help users save money based on their purchase history. For example, it can suggest bulk purchase or subscription discounts for items the user frequently buys. It can also suggest energy-efficient products when the user is buying electrical appliances. Furthermore, if the user is planning a trip, it can provide information on discounted flights and accommodations. In this way, it can support users in saving money through their purchasing behavior and help them manage their household finances.

[0118] A personalized automated ordering assistant can estimate a user's emotions and suggest communication-related products based on those emotions. For example, if a user is feeling lonely, it can suggest online communities or social networking services. If a user wants to strengthen their connections with friends and family, it can suggest video calling devices and apps. Furthermore, if a user wants to express gratitude, it can suggest gift cards or stationery for writing letters. This allows for communication suggestions tailored to the user's emotions, supporting their relationships.

[0119] A personalized automated ordering assistant can suggest products related to a user's hobbies and interests based on their purchase history. For example, if a user frequently buys gardening supplies, it can suggest new plants and gardening tools. If a user is interested in cooking, it can suggest new recipe books and cooking utensils. Furthermore, if a user enjoys sports, it can suggest new sports equipment and fitness goods. This allows for product suggestions tailored to the user's hobbies and interests, enriching their life.

[0120] A personalized automated ordering assistant can estimate a user's emotions and, based on those emotions, suggest products related to learning and self-improvement. For example, if a user is feeling motivated, it can suggest online courses or books to learn new skills. If a user is seeking personal growth, it can also provide information on self-help books and seminars. Furthermore, if a user is relaxed, it can suggest hobby classes or workshops where they can learn while relaxing. This allows for learning and self-improvement suggestions tailored to the user's emotions, supporting their personal growth.

[0121] A personalized automated ordering assistant can suggest products related to home safety based on a user's purchase history. For example, if a user has purchased security cameras, it can suggest additional security devices and services. If a user has young children, it can suggest baby gates and safety locks to protect their children. Furthermore, if a user lives with elderly people, it can suggest emergency call systems and accessibility products. This enhances the safety of the user's home and provides a safer living environment.

[0122] A personalized automated ordering assistant can estimate a user's emotions and suggest travel and leisure-related products based on those emotions. For example, if a user is feeling stressed, it can suggest relaxing travel destinations or resorts. If a user is seeking adventure, it can suggest activity-packed travel plans or outdoor equipment. Furthermore, if a user wants to enjoy time with family, it can suggest family-friendly travel packages or tickets to leisure facilities. This allows for travel and leisure suggestions tailored to the user's emotions, enriching their lives.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The learning unit learns the user's purchase history and preferences. For example, it analyzes products the user has purchased in the past, their frequency of purchase, and their preferences to predict what products will be needed next. If a user purchases detergent on a specific day each month, the learning unit can learn this pattern and predict the timing of the next purchase. It can also generate a list of products that will be needed next based on the user's purchase history. Step 2: The ordering unit manages inventory and automatically places orders for daily necessities based on the information learned by the learning unit. For example, it monitors the user's inventory status and automatically places orders when inventory levels are low. The ordering unit can automatically place an order when toilet paper inventory is low, saving the user time and effort. It can also monitor the user's inventory status in real time and automatically order necessary products. Step 3: The delivery department provides users with information about the products ordered by the ordering department. For example, it provides users with detailed information about the ordered products and their delivery status. The delivery department can provide users with detailed information about the ordered products via email or app notifications. It can also provide users with real-time delivery status of the ordered products. For example, the delivery department can display the delivery status of the ordered products on the app's dashboard. Step 4: The dialogue unit collects new preferences and requests through interaction with the user. For example, it interacts with the user through a chat interface to collect user preferences and requests. The dialogue unit can tell the AI ​​requests such as, "I'd like to add organic detergent to my next order." It can also interact with the user through a voice assistant to collect new preferences and requests. For example, the user can communicate requests such as, "I'd like to add organic detergent to my next order" by voice. Step 5: The suggestion department provides gift suggestions and order support based on the information collected by the dialogue department. For example, it suggests the most suitable gift based on the user's preferences and past gift history. If a user is unsure what to give a friend for their birthday, the suggestion department can suggest a gift that suits the friend's taste and support the order. It can also suggest products that are best suited to the user, taking into account the season and current trends. For example, it might suggest cooling products and sunscreen in the summer, and heating appliances and moisturizing creams in the winter.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the learning unit, ordering unit, supply unit, dialogue unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit learns the user's purchase history and preferences using the control unit 46A of the smart device 14 and predicts the next product needed using the identification processing unit 290 of the data processing unit 12. The ordering unit performs inventory management and automatic ordering using the identification processing unit 290 of the data processing unit 12 and monitors the user's inventory status using the control unit 46A of the smart device 14. The supply unit provides the user with information on ordered products using the control unit 46A of the smart device 14. The dialogue unit interacts with the user through the chat interface of the smart device 14, for example, and the suggestion unit provides gift suggestions and order support using the identification processing unit 290 of the data processing unit 12, for example. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the learning unit, ordering unit, supply unit, dialogue unit, and suggestion unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit learns the user's purchase history and preferences using the control unit 46A of the smart glasses 214 and predicts the next product needed using the identification processing unit 290 of the data processing unit 12. The ordering unit performs inventory management and automatic ordering using the identification processing unit 290 of the data processing unit 12 and monitors the user's inventory status using the control unit 46A of the smart glasses 214. The supply unit provides the user with information on ordered products using the control unit 46A of the smart glasses 214. The dialogue unit interacts with the user, for example, through the chat interface of the smart glasses 214, and the suggestion unit provides gift suggestions and order support using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the learning unit, ordering unit, supply unit, dialogue unit, and suggestion unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit learns the user's purchase history and preferences using the control unit 46A of the headset terminal 314 and predicts the next product needed using the identification processing unit 290 of the data processing unit 12. The ordering unit performs inventory management and automatic ordering using the identification processing unit 290 of the data processing unit 12 and monitors the user's inventory status using the control unit 46A of the headset terminal 314. The supply unit provides the user with information on ordered products using the control unit 46A of the headset terminal 314. The dialogue unit interacts with the user, for example, through the chat interface of the headset terminal 314, and the suggestion unit provides gift suggestions and order support using the identification processing unit 290 of the data processing unit 12. 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.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the learning unit, ordering unit, supply unit, dialogue unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit learns the user's purchase history and preferences by the control unit 46A of the robot 414 and predicts the next product needed by the identification processing unit 290 of the data processing unit 12. The ordering unit performs inventory management and automatic ordering by, for example, the identification processing unit 290 of the data processing unit 12 and monitors the user's inventory status by the control unit 46A of the robot 414. The supply unit provides the user with information on ordered products by, for example, the control unit 46A of the robot 414. The dialogue unit interacts with the user by, for example, the chat interface of the robot 414, and the suggestion unit provides gift suggestions and order support by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A learning unit that learns the user's purchase history and preferences, An ordering unit that manages the inventory of daily necessities and automatically places orders based on the information learned by the aforementioned learning unit, A supply unit that provides users with information regarding products ordered by the order unit, The dialogue department collects new preferences and requests through conversations with users, The system includes a suggestion unit that provides gift suggestions and order support based on information collected by the aforementioned dialogue unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, By analyzing the user's past purchase history and preferences, we predict the next product they will need. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned ordering section is, The system monitors the user's inventory status and automatically places orders when inventory levels become low. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the user with information about the ordered items. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, Gather new preferences and requests through dialogue with users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest the perfect gift based on the user's preferences and past gift-giving history, and support their order. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose the most suitable products to users, taking into account the season and current trends. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, We estimate the user's emotions and adjust the purchase history analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, By analyzing users' past purchase history, we predict purchasing patterns related to specific events and seasons. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, When analyzing purchase history, we improve predictive accuracy based on the user's family structure and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, It estimates the user's emotions and, based on those emotions, determines the priority of the next products the user will need. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, When analyzing purchase history, the system predicts region-specific products by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, When analyzing purchase history, we analyze users' social media activity and predict related products. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned ordering section is, It estimates the user's emotions and adjusts the timing of automated orders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned ordering section is, During inventory management, the system learns the user's consumption rate and predicts the optimal timing for ordering. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned ordering section is, During inventory management, the frequency of orders is adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned ordering section is, It estimates the user's emotions and determines the priority of automated orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned ordering section is, When managing inventory, the system selects the optimal shipping method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned ordering section is, During inventory management, analyze users' social media activity and prioritize inventory management for related products. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we provide product information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing product information, we refer to the user's past purchase history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing product information, adjust the level of detail based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates user emotions and prioritizes the provision of product information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing product information, we will provide region-specific information that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing product information, we analyze users' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the content and tone of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During conversations, the system provides the most appropriate response by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, During the conversation, the topic of the conversation is selected based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During conversations, the system takes the user's geographical location into consideration and provides region-specific topics. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and provides relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, It estimates the user's emotions and adjusts the gift suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When suggesting gifts, the system refers to the user's past gift history to make the most appropriate suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When suggesting gifts, adjust the level of detail based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, The system estimates the user's emotions and prioritizes gift suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When suggesting gifts, the system takes the user's geographical location into consideration to suggest region-specific gifts. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When suggesting gifts, we analyze the user's social media activity and suggest relevant gifts. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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 learning unit that learns the user's purchase history and preferences, An ordering unit that manages the inventory of daily necessities and automatically places orders based on the information learned by the aforementioned learning unit, A supply unit that provides users with information regarding products ordered by the order unit, The dialogue department collects new preferences and requests through conversations with users, The system includes a suggestion unit that provides gift suggestions and order support based on information collected by the aforementioned dialogue unit. A system characterized by the following features.

2. The aforementioned learning unit, By analyzing the user's past purchase history and preferences, we predict the next product they will need. The system according to feature 1.

3. The aforementioned ordering section is, The system monitors the user's inventory status and automatically places orders when inventory levels become low. The system according to feature 1.

4. The aforementioned supply unit is, Provide the user with information about the ordered items. The system according to feature 1.

5. The aforementioned dialogue unit, Gather new preferences and requests through dialogue with users. The system according to feature 1.

6. The aforementioned proposal section is, We suggest the perfect gift based on the user's preferences and past gift-giving history, and support their order. The system according to feature 1.

7. The aforementioned proposal section is, We propose the most suitable products to users, taking into account the season and current trends. The system according to feature 1.

8. The aforementioned learning unit, We estimate the user's emotions and adjust the purchase history analysis method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned learning unit, By analyzing users' past purchase history, we predict purchasing patterns related to specific events and seasons. The system according to feature 1.

10. The aforementioned learning unit, When analyzing purchase history, we improve predictive accuracy based on the user's family structure and lifestyle. The system according to feature 1.

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