System

Abstract

An object of a system according to an embodiment is to appropriately prepare necessary articles when a person living alone is hospitalized.SOLUTION: A system according to an embodiment includes a use history analysis unit, a proposal unit, and a reorder proposal unit. The use history analysis unit analyzes a use history of a user. The suggestion unit suggests optimal hospitalization necessities based on the data analyzed by the usage history analysis unit. The reorder proposing unit proposes reordering of the hospitalization necessities proposed by the proposing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult for people living alone to properly prepare the necessary items when they are hospitalized.

[0005] The system according to the embodiment aims to properly prepare the necessary items for a person living alone when they are hospitalized. [Means for solving the problem]

[0006] The system according to the embodiment includes a usage history analysis unit, a suggestion unit, and a reorder suggestion unit. The usage history analysis unit analyzes the user's usage history. The suggestion unit suggests optimal hospital necessities based on the data analyzed by the usage history analysis unit. The reorder suggestion unit suggests a reorder of the hospital necessities suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately prepare items necessary for a person living alone when they are hospitalized. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The hospitalization essentials recommendation system according to an embodiment of the present invention delivers necessary items to the hospital when a person living alone and with no family or neighbors nearby is admitted to the hospital. This system is provided as a PayPay mini-app, and a generation AI analyzes PayPay usage history to recommend optimal hospitalization essentials. This allows the hospitalization essentials recommendation system to smoothly prepare the necessary items for a person living alone when they are admitted to the hospital.

[0029] A hospitalization necessities recommendation system according to an embodiment includes a usage history analysis unit, a recommendation unit, and a reorder recommendation unit. The usage history analysis unit analyzes a user's usage history. For example, the generation AI analyzes the user's PayPay usage history to understand the user's preferences and lifestyle. The generation AI also analyzes the products and services the user frequently purchases and recommends items that will be needed during hospitalization. The recommendation unit recommends optimal hospitalization necessities based on the data analyzed by the usage history analysis unit. For example, the generation AI lists toiletries, clothing, groceries, and other items that the user regularly uses and recommends them as items needed during hospitalization. If the user regularly purchases a specific product, the generation AI recommends reordering that product before it runs out. The reorder recommendation unit recommends reordering the hospitalization necessities recommended by the recommendation unit. For example, the generation AI analyzes product data of the user's past orders to predict usage patterns. The generation AI also analyzes the user's length of hospitalization and past orders to optimize the number of necessary items to order. As a result, the hospitalization necessities recommendation system according to the embodiment allows people living alone to smoothly prepare the items they will need when they are hospitalized. For example, the system can suggest toiletries, clothing, food, and other items that a user will need when they are hospitalized in advance, allowing them to easily order them. It also suggests reordering items so that the necessary items can be used without shortages during hospitalization, allowing users to prepare the optimal quantity without ordering unnecessary items.

[0030] The usage history analysis unit can prioritize suggestions of products that are purchased during specific time periods based on the user's health condition and lifestyle. The generation AI, for example, collects the user's health condition data and identifies products that are purchased when the user is in a specific health condition. For example, it can suggest medicines and foods that are purchased when the user has a cold. The generation AI can also analyze the user's lifestyle and prioritize suggestions of products that are purchased during specific time periods. For example, it can suggest relaxation items that are purchased at night. The generation AI can also take into account the user's health condition and lifestyle and suggest items that are necessary for hospitalization. For example, it can list items that correspond to specific health conditions. This makes it possible to suggest hospital necessities at a more appropriate time by taking into account the user's health condition and lifestyle.

[0031] The usage history analysis unit can predict changes in preferences by referencing social media posts. The generation AI, for example, analyzes the content of a user's social media posts and predicts changes in preferences. For example, it extracts the user's interests and concerns from the content of the posts and suggests items necessary for hospitalization. The generation AI also analyzes the user's preferences based on the content of social media posts and suggests products related to specific interests and concerns. For example, it suggests travel goods to a user who posts frequently about travel. The generation AI also uses social media data to track changes in the user's preferences in real time and suggests items necessary for hospitalization. For example, it lists items the user needs based on recent posts. This enables more accurate preference analysis by referencing the content of a user's social media posts.

[0032] The suggestion unit can suggest events or services related to hobbies and interests. For example, the generation AI can suggest events related to the user's hobbies and interests based on the results of an analysis of usage history. For example, it can suggest concert tickets to a user who loves music. The generation AI can also analyze the user's usage history and suggest services related to their interests. For example, it can suggest gym membership to a user who loves fitness. The generation AI can also suggest events and services related to hobbies and interests and list items needed during hospitalization. For example, it can suggest goods related to a specific hobby. This allows the user's hospital stay to be more fulfilling by suggesting events and services related to their hobbies and interests.

[0033] The suggestion unit can suggest region-specific hospital necessities based on location information. For example, the generation AI suggests region-specific hospital necessities based on the user's location information. For example, it suggests warm clothing to a user living in a cold region. The generation AI also takes location information into consideration and suggests hospital necessities that suit the region's climate and culture. For example, it suggests dehumidifying products to a user living in a humid region. The generation AI also uses location information to create a list of region-specific hospital necessities. For example, it suggests products that are popular in a specific region. In this way, region-specific hospital necessities can be suggested by taking the user's location information into consideration.

[0034] The suggestion unit can suggest items that correspond to specific health conditions based on health checkup data. For example, the generation AI collects a user's past health checkup data and suggests items that correspond to specific health conditions. For example, it suggests food items that are suitable for a user with diabetes. The generation AI also lists hospital necessities that correspond to specific health conditions based on the health checkup data. For example, it suggests allergy-friendly foods to a user with allergies. The generation AI also takes health checkup data into consideration and suggests items that are necessary for hospitalization. For example, it lists medicines and supplements that correspond to specific illnesses. In this way, it is possible to suggest items that correspond to specific health conditions by taking the user's health checkup data into consideration.

[0035] The suggestion unit can suggest appropriate food items based on dietary restrictions and allergy information. For example, the generation AI collects information on the user's dietary restrictions and allergies and suggests appropriate food items. For example, it suggests gluten-free foods. The generation AI also lists food items needed during hospitalization based on dietary restrictions and allergy information. For example, it suggests dairy-free foods to a user who is allergic to dairy products. The generation AI also takes into account dietary restrictions and allergy information and suggests appropriate food items as hospital necessities. For example, it suggests low-carbohydrate foods. This makes it possible to suggest appropriate food items by taking into account the user's dietary restrictions and allergy information.

[0036] The suggestion unit can collect user feedback on the proposed hospitalization necessities and reflect it in the next proposal. The generation AI, for example, builds a system that collects user feedback on the proposed hospitalization necessities and reflects it in the next proposal. For example, it improves the proposal content based on user ratings and comments. The generation AI also analyzes user feedback and identifies areas for improvement in the proposed hospitalization necessities. For example, it adjusts the proposal content based on user opinions. The generation AI also collects feedback on the proposed hospitalization necessities and reflects it in the next proposal. For example, it modifies the proposal content based on the user's evaluation score. In this way, by collecting user feedback and reflecting it in the next proposal, it becomes possible to suggest more appropriate hospitalization necessities.

[0037] The suggestion unit can also suggest items necessary for caring for the user's pet. The generation AI, for example, collects information about the user's pet and suggests items needed to care for the pet when hospitalized. For example, it suggests pet food and toilet supplies. The generation AI also lists items needed to care for the pet and suggests them as hospital necessities. For example, it suggests pet medicine and toys. The generation AI also suggests items needed when hospitalized based on the information about the user's pet. For example, it lists goods needed for pet care. In this way, by suggesting items needed to care for the user's pet, the care of the pet during hospitalization can be entrusted to someone with peace of mind.

[0038] The suggestion unit can add a function to share the suggested hospital necessities with friends and family and solicit their opinions. The generation AI, for example, adds a function to share the suggested hospital necessities with the user's friends and family and solicit their opinions. For example, by sending a sharing link and collecting feedback. The generation AI also builds a system to improve the suggested hospital necessities based on opinions from friends and family. For example, it adjusts the suggested content based on the shared opinions. The generation AI also adds a function to share the suggested hospital necessities and collect feedback from friends and family. For example, it collects opinions through a sharing link and improves the suggested content. This makes it possible to suggest more appropriate items by sharing the suggested hospital necessities with friends and family and soliciting their opinions.

[0039] The reorder suggestion unit can suggest items needed for a specific period based on life events. The generation AI, for example, collects life event data of the user and suggests items needed for a specific period. For example, it suggests travel goods needed before a trip. The generation AI also takes life events into consideration and lists items needed for a specific period. For example, it suggests packing materials and cleaning supplies needed before moving. The generation AI also suggests items needed for a specific period based on life event data. For example, it lists baby products needed before giving birth. In this way, it can suggest items needed for a specific period by taking the user's life events into consideration.

[0040] The reorder suggestion unit can monitor fluctuations in purchasing patterns in real time and issue an alert if an abnormal change occurs. The generation AI, for example, builds a system that monitors users' purchasing patterns in real time and issues an alert if an abnormal change occurs. For example, it detects a sudden increase or decrease in purchasing volume and notifies the user. The generation AI also analyzes fluctuations in purchasing patterns in real time and issues an alert if an abnormal change occurs. For example, it notifies the user if a specific product suddenly stops being purchased. The generation AI also develops a system that monitors users' purchasing patterns and issues an alert if an abnormal change occurs. For example, it notifies the user if the pattern deviates significantly from the normal purchasing pattern. In this way, abnormal purchasing behavior can be detected early by monitoring fluctuations in users' purchasing patterns in real time and issuing an alert if an abnormal change occurs.

[0041] The reorder suggestion unit can refer not only to the user's past purchase history, but also to the purchase history of other users living in the same area. For example, the generation AI can refer to the user's past purchase history and the purchase history of other users living in the same area to predict usage patterns. For example, it can suggest products based on local trends. The generation AI can also determine whether a particular product is popular in the area based on the purchase history of other users living in the same area and make suggestions. For example, it can suggest foods and daily necessities that are popular in the area. The generation AI can also use local purchase history data to predict the user's usage patterns and suggest items needed for hospitalization. For example, it can list products that are frequently purchased in the area. This enables more accurate reorder suggestions by referring not only to the user's past purchase history, but also to the purchase history of other users living in the same area.

[0042] The reorder suggestion unit can provide saving advice to the user based on the usage prediction results. The generation AI, for example, builds a system that provides saving advice to the user based on the usage prediction results. For example, it makes suggestions to reduce wasteful purchases. The generation AI also analyzes the user's purchasing patterns and provides saving advice. For example, it makes suggestions to reduce costs by buying regularly purchased items in bulk. The generation AI also provides saving advice to the user based on the usage prediction results. For example, it makes suggestions to reduce costs by adjusting the timing of purchasing specific items. In this way, by providing saving advice to the user based on the usage prediction results, it is possible to reduce wasteful purchases.

[0043] The reorder suggestion unit can suggest eco-friendly products based on the energy consumption data. The generation AI, for example, collects the user's energy consumption data and builds a system that suggests eco-friendly products. For example, it suggests energy-efficient home appliances. The generation AI also suggests eco-friendly products to the user based on the energy consumption data. For example, it suggests products that use renewable energy. The generation AI also takes the user's energy consumption data into consideration and lists eco-friendly products. For example, it suggests low-power home appliances and eco-bags. In this way, it is possible to suggest eco-friendly products by taking the user's energy consumption data into consideration.

[0044] The reorder suggestion unit can make suggestions based on the loyalty of specific brands or products, based on the user's purchase history. The generation AI, for example, makes reorder suggestions based on the user's purchase history, taking into account the loyalty of specific brands or products. For example, it prioritizes suggestions of products from brands that the user frequently purchases. The generation AI also analyzes the purchase history, identifies brands or products to which the user has high loyalty, and suggests reorders. For example, it prioritizes suggestions of products that the user has given high ratings. The generation AI also suggests reorders of specific brands or products based on the user's loyalty data. For example, it prioritizes suggestions of products that the user has purchased many times in the past. This makes it possible to make reorder suggestions that suit the user's preferences by taking into account the loyalty of specific brands or products based on the user's purchase history.

[0045] The reorder suggestion unit can refer to the user's past reviews and ratings and prioritize suggesting products with high satisfaction. The generation AI, for example, prioritizes suggesting products with high satisfaction based on the user's past reviews and ratings. For example, it suggests products that have received high ratings as reorders. The generation AI also analyzes past reviews and ratings to identify products with high user satisfaction and suggest them for reordering. For example, it prioritizes suggesting products that have received high ratings from the user. The generation AI also lists products with high satisfaction based on user review and rating data and suggests them for reordering. For example, it prioritizes suggesting products that have received high ratings from the user. In this way, by referring to the user's past reviews and ratings, it is possible to prioritize suggesting products with high satisfaction.

[0046] The reorder suggestion unit can suggest products related to specific seasons or events based on the user's purchase history. For example, the generation AI suggests products related to specific seasons or events based on the user's purchase history. For example, it suggests cooling products in the summer. The generation AI also analyzes the purchase history and lists products related to specific seasons or events. For example, it suggests gift products before Christmas. The generation AI also takes the user's purchase history into consideration and suggests products related to specific seasons or events. For example, it suggests anti-pollen products in the spring. This makes it possible to suggest products related to specific seasons or events based on the user's purchase history, thereby making it possible to make suggestions that meet the user's needs.

[0047] The reorder suggestion unit can make suggestions based on shared preferences, based on products previously purchased by the user's friends and family. For example, the generation AI suggests reorders based on shared preferences, based on products previously purchased by the user's friends and family. For example, it suggests foods that the whole family likes. The generation AI also analyzes the purchasing history of friends and family to create a list of products based on shared preferences. For example, it suggests products that friends have given high ratings to. The generation AI also suggests reorders based on shared preferences, based on the purchasing data of the user's friends and family. For example, it suggests everyday items that the whole family likes. This makes it possible to make suggestions based on shared preferences by taking into account products previously purchased by the user's friends and family.

[0048] The reorder suggestion unit can suggest products that suit a specific lifestyle based on the user's purchasing history. For example, the generation AI suggests products that suit a specific lifestyle based on the user's purchasing history. For example, it might suggest camping equipment to a user who loves the outdoors. The generation AI also analyzes the purchasing history to list products that suit the user's lifestyle. For example, it might suggest healthcare products to a health-conscious user. The generation AI also suggests reorders that suit a specific lifestyle based on the user's lifestyle data. For example, it might suggest pet supplies to a user who keeps a pet. This makes it possible to suggest products that suit the user's needs by suggesting products that suit a specific lifestyle based on the user's purchasing history.

[0049] The reorder suggestion unit can suggest an appropriate quantity based on the user's past consumption rate. The generation AI, for example, builds a system that suggests an appropriate quantity based on the user's past consumption rate. For example, it analyzes past consumption data and suggests the optimal number to order. The generation AI also takes consumption rate into consideration and suggests an appropriate quantity to the user. For example, it suggests a quantity that does not waste based on past consumption patterns. The generation AI also develops a system that optimizes the number of orders based on the user's consumption rate data. For example, it analyzes past consumption rate and suggests an appropriate quantity. This makes it possible to suggest an appropriate quantity by taking the user's past consumption rate into consideration.

[0050] The reorder suggestion unit can refer to the user's past return history and prevent over-ordering. The generation AI, for example, builds a system that prevents over-ordering based on the user's past return history. For example, it adjusts the quantity of items that are frequently returned to an appropriate quantity. The generation AI also analyzes the return history and makes suggestions to prevent over-ordering. For example, it adjusts the quantity of items that have been frequently returned in the past to an appropriate quantity. The generation AI also develops a system that prevents over-ordering based on the user's return history data. For example, it analyzes past return data and suggests appropriate quantities. In this way, over-ordering can be prevented by referring to the user's past return history.

[0051] The reorder suggestion unit can add a function to incorporate the opinions of the user's friends and family. For example, the generation AI adds a function to incorporate the opinions of the user's friends and family when optimizing the number of orders. For example, it collects opinions through a shared link and suggests the optimal quantity. The generation AI also builds a system that optimizes the number of orders based on the opinions of friends and family. For example, it adjusts the number of orders based on the shared opinions. The generation AI also adds a function to collect the opinions of friends and family when optimizing the number of orders. For example, it collects opinions through a shared link and suggests the optimal quantity. This makes it possible to make more appropriate reorder suggestions by incorporating the opinions of the user's friends and family.

[0052] The reorder suggestion unit can suggest products related to specific seasons or events based on the user's past purchase history. For example, the generation AI suggests products related to specific seasons or events based on the user's past purchase history. For example, it suggests cooling products in the summer. The generation AI also analyzes purchase history and lists products related to specific seasons or events. For example, it suggests gift products before Christmas. The generation AI also takes the user's purchase history into consideration and suggests products related to specific seasons or events. For example, it suggests anti-pollen products in the spring. This makes it possible to suggest products related to specific seasons or events based on the user's past purchase history, thereby making it possible to make suggestions that meet the user's needs.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The suggestion unit can suggest entertainment content that can be enjoyed during hospitalization based on the user's hobbies and interests. For example, the suggestion unit can suggest the latest movies and dramas to a user who loves movies. It can also suggest e-books and audiobooks to a user who loves reading. It can also suggest relaxing music and podcasts to a user who loves music. This allows users to enjoy entertainment that suits their hobbies and interests even while hospitalized, making their hospital stay more comfortable.

[0055] The suggestion unit can suggest rehabilitation equipment needed during hospitalization based on the user's health condition. For example, for a user who needs foot rehabilitation, equipment that supports foot exercise can be suggested. Also, for a user who needs hand rehabilitation, equipment that supports hand exercise can be suggested. Furthermore, for a user who needs whole-body rehabilitation, it is also possible to suggest equipment that supports whole-body exercise. In this way, by suggesting rehabilitation equipment according to the user's health condition, rehabilitation during hospitalization can be effectively supported.

[0056] The suggestion unit can suggest meal plans suitable for the user's hospitalization based on the user's dietary restrictions. For example, a low-carbohydrate meal plan can be suggested for a diabetic user. Also, an allergy-friendly meal plan can be suggested for a user with allergies. Furthermore, it is possible to suggest meal plans that cater to vegetarians and vegans. In this way, it is possible to support dietary management during hospitalization by suggesting an appropriate meal plan according to the user's dietary restrictions.

[0057] The suggestion unit can suggest daily necessities needed during hospitalization based on the user's past purchasing history. For example, it can suggest toiletries and towels that the user has purchased in the past. It can also suggest skin care products and hair care products that the user frequently purchases. It can also suggest stationery and electronic devices that the user uses on a daily basis. This allows the user to smoothly prepare daily necessities needed during hospitalization based on the user's past purchasing history.

[0058] The suggestion unit can suggest local services that can be used during hospitalization based on the user's location information. For example, it can suggest delivery services from nearby restaurants and cafes. It can also suggest online services from local libraries and cultural facilities. It can also suggest local volunteer groups and support services. This makes hospitalization more convenient by suggesting local services that can be used during hospitalization based on the user's location information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The usage history analysis unit analyzes the user's usage history. For example, the generation AI analyzes the user's PayPay usage history to understand the user's preferences and lifestyle. The generation AI also analyzes the products and services the user frequently purchases and suggests items that will be needed during hospitalization. Step 2: The suggestion unit suggests optimal hospitalization necessities based on the data analyzed by the usage history analysis unit. For example, the generation AI may list toiletries, clothing, food, and other items that the user uses on a daily basis and suggest them as items necessary for hospitalization. Step 3: The reorder suggestion unit suggests reordering the hospital necessities suggested by the suggestion unit. For example, the generation AI analyzes product data previously ordered by the user to predict usage patterns. The generation AI also analyzes the user's length of hospitalization and past order details to optimize the number of necessary items to order.

[0061] (Example 2) The hospitalization essentials recommendation system according to an embodiment of the present invention delivers necessary items to the hospital when a person living alone and with no family or neighbors nearby is admitted to the hospital. This system is provided as a PayPay mini-app, and a generation AI analyzes PayPay usage history to recommend optimal hospitalization essentials. This allows the hospitalization essentials recommendation system to smoothly prepare the necessary items for a person living alone when they are admitted to the hospital.

[0062] A hospitalization necessities recommendation system according to an embodiment includes a usage history analysis unit, a recommendation unit, and a reorder recommendation unit. The usage history analysis unit analyzes a user's usage history. For example, the generation AI analyzes the user's PayPay usage history to understand the user's preferences and lifestyle. The generation AI also analyzes the products and services the user frequently purchases and recommends items that will be needed during hospitalization. The recommendation unit recommends optimal hospitalization necessities based on the data analyzed by the usage history analysis unit. For example, the generation AI lists toiletries, clothing, groceries, and other items that the user regularly uses and recommends them as items needed during hospitalization. If the user regularly purchases a specific product, the generation AI recommends reordering that product before it runs out. The reorder recommendation unit recommends reordering the hospitalization necessities recommended by the recommendation unit. For example, the generation AI analyzes product data of the user's past orders to predict usage patterns. The generation AI also analyzes the user's length of hospitalization and past orders to optimize the number of necessary items to order. As a result, the hospitalization necessities recommendation system according to the embodiment allows people living alone to smoothly prepare the items they will need when they are hospitalized. For example, the system can suggest toiletries, clothing, food, and other items that a user will need when they are hospitalized in advance, allowing them to easily order them. It also suggests reordering items so that the necessary items can be used without shortages during hospitalization, allowing users to prepare the optimal quantity without ordering unnecessary items.

[0063] The usage history analysis unit collects user emotional data and can predict changes in preferences based on emotional fluctuations. For example, when analyzing a user's usage history, the generation AI collects the user's emotional data and analyzes emotional fluctuations. For example, it predicts changes in the user's preferences based on the emotional score at the time of purchase. The generation AI also collects the user's emotional data and identifies products that are purchased when the user is in a specific emotional state. For example, it suggests relaxation products that are purchased during times of high stress. The generation AI also predicts changes in the user's preferences based on the emotional data and suggests items needed for hospitalization. For example, it lists products that the user prefers depending on emotional fluctuations. In this way, by collecting the user's emotional data and predicting changes in preferences, it becomes possible to more accurately suggest essential hospital items.

[0064] The usage history analysis unit can prioritize suggestions of products that are purchased during specific time periods based on the user's health condition and lifestyle. The generation AI, for example, collects the user's health condition data and identifies products that are purchased when the user is in a specific health condition. For example, it can suggest medicines and foods that are purchased when the user has a cold. The generation AI can also analyze the user's lifestyle and prioritize suggestions of products that are purchased during specific time periods. For example, it can suggest relaxation items that are purchased at night. The generation AI can also take into account the user's health condition and lifestyle and suggest items that are necessary for hospitalization. For example, it can list items that correspond to specific health conditions. This makes it possible to suggest hospital necessities at a more appropriate time by taking into account the user's health condition and lifestyle.

[0065] The usage history analysis unit can predict changes in preferences by referencing social media posts. The generation AI, for example, analyzes the content of a user's social media posts and predicts changes in preferences. For example, it extracts the user's interests and concerns from the content of the posts and suggests items necessary for hospitalization. The generation AI also analyzes the user's preferences based on the content of social media posts and suggests products related to specific interests and concerns. For example, it suggests travel goods to a user who posts frequently about travel. The generation AI also uses social media data to track changes in the user's preferences in real time and suggests items necessary for hospitalization. For example, it lists items the user needs based on recent posts. This enables more accurate preference analysis by referencing the content of a user's social media posts.

[0066] The suggestion unit can suggest events or services related to hobbies and interests. For example, the generation AI can suggest events related to the user's hobbies and interests based on the results of an analysis of usage history. For example, it can suggest concert tickets to a user who loves music. The generation AI can also analyze the user's usage history and suggest services related to their interests. For example, it can suggest gym membership to a user who loves fitness. The generation AI can also suggest events and services related to hobbies and interests and list items needed during hospitalization. For example, it can suggest goods related to a specific hobby. This allows the user's hospital stay to be more fulfilling by suggesting events and services related to their hobbies and interests.

[0067] The suggestion unit can suggest region-specific hospital necessities based on location information. For example, the generation AI suggests region-specific hospital necessities based on the user's location information. For example, it suggests warm clothing to a user living in a cold region. The generation AI also takes location information into consideration and suggests hospital necessities that suit the region's climate and culture. For example, it suggests dehumidifying products to a user living in a humid region. The generation AI also uses location information to create a list of region-specific hospital necessities. For example, it suggests products that are popular in a specific region. In this way, region-specific hospital necessities can be suggested by taking the user's location information into consideration.

[0068] The suggestion unit can use the emotion estimation function to analyze the emotions a user feels when purchasing a specific product, and prioritize suggesting products that elicit positive emotions. The generation AI, for example, uses the emotion estimation function to analyze the emotions a user feels when purchasing a specific product. For example, it suggests products that elicit positive emotions based on the emotion score at the time of purchase. The generation AI also prioritizes suggesting products that elicit positive emotions based on the user's emotion data. For example, it suggests products that have a relaxing effect. The generation AI also uses the emotion estimation function to list products that the user feels positive emotions when purchasing. For example, it prioritizes suggesting products with a high emotion score. In this way, by analyzing the user's emotions and suggesting products that elicit positive emotions, it is possible to improve user satisfaction.

[0069] The suggestion unit can suggest items that correspond to specific health conditions based on health checkup data. For example, the generation AI collects a user's past health checkup data and suggests items that correspond to specific health conditions. For example, it suggests food items that are suitable for a user with diabetes. The generation AI also lists hospital necessities that correspond to specific health conditions based on the health checkup data. For example, it suggests allergy-friendly foods to a user with allergies. The generation AI also takes health checkup data into consideration and suggests items that are necessary for hospitalization. For example, it lists medicines and supplements that correspond to specific illnesses. In this way, it is possible to suggest items that correspond to specific health conditions by taking the user's health checkup data into consideration.

[0070] The suggestion unit can suggest appropriate food items based on dietary restrictions and allergy information. For example, the generation AI collects information on the user's dietary restrictions and allergies and suggests appropriate food items. For example, it suggests gluten-free foods. The generation AI also lists food items needed during hospitalization based on dietary restrictions and allergy information. For example, it suggests dairy-free foods to a user who is allergic to dairy products. The generation AI also takes into account dietary restrictions and allergy information and suggests appropriate food items as hospital necessities. For example, it suggests low-carbohydrate foods. This makes it possible to suggest appropriate food items by taking into account the user's dietary restrictions and allergy information.

[0071] The suggestion unit can collect user feedback on the proposed hospitalization necessities and reflect it in the next proposal. The generation AI, for example, builds a system that collects user feedback on the proposed hospitalization necessities and reflects it in the next proposal. For example, it improves the proposal content based on user ratings and comments. The generation AI also analyzes user feedback and identifies areas for improvement in the proposed hospitalization necessities. For example, it adjusts the proposal content based on user opinions. The generation AI also collects feedback on the proposed hospitalization necessities and reflects it in the next proposal. For example, it modifies the proposal content based on the user's evaluation score. In this way, by collecting user feedback and reflecting it in the next proposal, it becomes possible to suggest more appropriate hospitalization necessities.

[0072] The suggestion unit can also suggest items necessary for caring for the user's pet. The generation AI, for example, collects information about the user's pet and suggests items needed to care for the pet when hospitalized. For example, it suggests pet food and toilet supplies. The generation AI also lists items needed to care for the pet and suggests them as hospital necessities. For example, it suggests pet medicine and toys. The generation AI also suggests items needed when hospitalized based on the information about the user's pet. For example, it lists goods needed for pet care. In this way, by suggesting items needed to care for the user's pet, the care of the pet during hospitalization can be entrusted to someone with peace of mind.

[0073] The suggestion unit can add a function to share the suggested hospital necessities with friends and family and solicit their opinions. The generation AI, for example, adds a function to share the suggested hospital necessities with the user's friends and family and solicit their opinions. For example, by sending a sharing link and collecting feedback. The generation AI also builds a system to improve the suggested hospital necessities based on opinions from friends and family. For example, it adjusts the suggested content based on the shared opinions. The generation AI also adds a function to share the suggested hospital necessities and collect feedback from friends and family. For example, it collects opinions through a sharing link and improves the suggested content. This makes it possible to suggest more appropriate items by sharing the suggested hospital necessities with friends and family and soliciting their opinions.

[0074] The suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed hospital necessities and prioritize suggesting items that elicit positive emotions. The generation AI, for example, uses the emotion estimation function to analyze the emotions the user feels toward the proposed hospital necessities. For example, it suggests items that elicit positive emotions based on the emotion score. The generation AI also prioritizes suggesting hospital necessities that elicit positive emotions based on the user's emotion data. For example, it suggests products that have a relaxing effect. The generation AI also uses the emotion estimation function to list items that the user feels positive about toward the proposed hospital necessities. For example, it prioritizes suggesting items with a high emotion score. In this way, by analyzing the user's emotions and suggesting items that elicit positive emotions, it is possible to improve user satisfaction.

[0075] The reorder suggestion unit can suggest items needed for a specific period based on life events. The generation AI, for example, collects life event data of the user and suggests items needed for a specific period. For example, it suggests travel goods needed before a trip. The generation AI also takes life events into consideration and lists items needed for a specific period. For example, it suggests packing materials and cleaning supplies needed before moving. The generation AI also suggests items needed for a specific period based on life event data. For example, it lists baby products needed before giving birth. In this way, it can suggest items needed for a specific period by taking the user's life events into consideration.

[0076] The reorder suggestion unit can monitor fluctuations in purchasing patterns in real time and issue an alert if an abnormal change occurs. The generation AI, for example, builds a system that monitors users' purchasing patterns in real time and issues an alert if an abnormal change occurs. For example, it detects a sudden increase or decrease in purchasing volume and notifies the user. The generation AI also analyzes fluctuations in purchasing patterns in real time and issues an alert if an abnormal change occurs. For example, it notifies the user if a specific product suddenly stops being purchased. The generation AI also develops a system that monitors users' purchasing patterns and issues an alert if an abnormal change occurs. For example, it notifies the user if the pattern deviates significantly from the normal purchasing pattern. In this way, abnormal purchasing behavior can be detected early by monitoring fluctuations in users' purchasing patterns in real time and issuing an alert if an abnormal change occurs.

[0077] The reorder suggestion unit can refer not only to the user's past purchase history, but also to the purchase history of other users living in the same area. For example, the generation AI can refer to the user's past purchase history and the purchase history of other users living in the same area to predict usage patterns. For example, it can suggest products based on local trends. The generation AI can also determine whether a particular product is popular in the area based on the purchase history of other users living in the same area and make suggestions. For example, it can suggest foods and daily necessities that are popular in the area. The generation AI can also use local purchase history data to predict the user's usage patterns and suggest items needed for hospitalization. For example, it can list products that are frequently purchased in the area. This enables more accurate reorder suggestions by referring not only to the user's past purchase history, but also to the purchase history of other users living in the same area.

[0078] The reorder suggestion unit can provide saving advice to the user based on the usage prediction results. The generation AI, for example, builds a system that provides saving advice to the user based on the usage prediction results. For example, it makes suggestions to reduce wasteful purchases. The generation AI also analyzes the user's purchasing patterns and provides saving advice. For example, it makes suggestions to reduce costs by buying regularly purchased items in bulk. The generation AI also provides saving advice to the user based on the usage prediction results. For example, it makes suggestions to reduce costs by adjusting the timing of purchasing specific items. In this way, by providing saving advice to the user based on the usage prediction results, it is possible to reduce wasteful purchases.

[0079] The reorder suggestion unit can suggest eco-friendly products based on the energy consumption data. The generation AI, for example, collects the user's energy consumption data and builds a system that suggests eco-friendly products. For example, it suggests energy-efficient home appliances. The generation AI also suggests eco-friendly products to the user based on the energy consumption data. For example, it suggests products that use renewable energy. The generation AI also takes the user's energy consumption data into consideration and lists eco-friendly products. For example, it suggests low-power home appliances and eco-bags. In this way, it is possible to suggest eco-friendly products by taking the user's energy consumption data into consideration.

[0080] The reorder suggestion unit can use the emotion estimation function to analyze the emotion a user feels when reordering a specific product, and suggest reordering at a timing that brings out positive emotions. The generation AI, for example, uses the emotion estimation function to analyze the emotion a user feels when reordering a specific product. For example, it suggests reordering at a timing that brings out positive emotions based on the emotion score. The generation AI also suggests reordering at a timing that brings out positive emotions based on the user's emotion data. For example, it suggests reordering when the user is relaxed. The generation AI also uses the emotion estimation function to identify the timing when the user feels positive emotions when reordering, and suggests reordering. For example, it suggests reordering when the emotion score is high. In this way, by analyzing the user's emotions and suggesting reordering at a timing that brings out positive emotions, it is possible to improve user satisfaction.

[0081] The reorder suggestion unit can make suggestions based on the loyalty of specific brands or products, based on the user's purchase history. The generation AI, for example, makes reorder suggestions based on the user's purchase history, taking into account the loyalty of specific brands or products. For example, it prioritizes suggestions of products from brands that the user frequently purchases. The generation AI also analyzes the purchase history, identifies brands or products to which the user has high loyalty, and suggests reorders. For example, it prioritizes suggestions of products that the user has given high ratings. The generation AI also suggests reorders of specific brands or products based on the user's loyalty data. For example, it prioritizes suggestions of products that the user has purchased many times in the past. This makes it possible to make reorder suggestions that suit the user's preferences by taking into account the loyalty of specific brands or products based on the user's purchase history.

[0082] The reorder suggestion unit can refer to the user's past reviews and ratings and prioritize suggesting products with high satisfaction. The generation AI, for example, prioritizes suggesting products with high satisfaction based on the user's past reviews and ratings. For example, it suggests products that have received high ratings as reorders. The generation AI also analyzes past reviews and ratings to identify products with high user satisfaction and suggest them for reordering. For example, it prioritizes suggesting products that have received high ratings from the user. The generation AI also lists products with high satisfaction based on user review and rating data and suggests them for reordering. For example, it prioritizes suggesting products that have received high ratings from the user. In this way, by referring to the user's past reviews and ratings, it is possible to prioritize suggesting products with high satisfaction.

[0083] The reorder suggestion unit can suggest products related to specific seasons or events based on the user's purchase history. For example, the generation AI suggests products related to specific seasons or events based on the user's purchase history. For example, it suggests cooling products in the summer. The generation AI also analyzes the purchase history and lists products related to specific seasons or events. For example, it suggests gift products before Christmas. The generation AI also takes the user's purchase history into consideration and suggests products related to specific seasons or events. For example, it suggests anti-pollen products in the spring. This makes it possible to suggest products related to specific seasons or events based on the user's purchase history, thereby making it possible to make suggestions that meet the user's needs.

[0084] The reorder suggestion unit can make suggestions based on shared preferences, based on products previously purchased by the user's friends and family. For example, the generation AI suggests reorders based on shared preferences, based on products previously purchased by the user's friends and family. For example, it suggests foods that the whole family likes. The generation AI also analyzes the purchasing history of friends and family to create a list of products based on shared preferences. For example, it suggests products that friends have given high ratings to. The generation AI also suggests reorders based on shared preferences, based on the purchasing data of the user's friends and family. For example, it suggests everyday items that the whole family likes. This makes it possible to make suggestions based on shared preferences by taking into account products previously purchased by the user's friends and family.

[0085] The reorder suggestion unit can suggest products that suit a specific lifestyle based on the user's purchasing history. For example, the generation AI suggests products that suit a specific lifestyle based on the user's purchasing history. For example, it might suggest camping equipment to a user who loves the outdoors. The generation AI also analyzes the purchasing history to list products that suit the user's lifestyle. For example, it might suggest healthcare products to a health-conscious user. The generation AI also suggests reorders that suit a specific lifestyle based on the user's lifestyle data. For example, it might suggest pet supplies to a user who keeps a pet. This makes it possible to suggest products that suit the user's needs by suggesting products that suit a specific lifestyle based on the user's purchasing history.

[0086] The reorder suggestion unit can use the emotion estimation function to analyze the emotions a user has when reordering, and prioritize suggesting products that elicit positive emotions. The generation AI, for example, uses the emotion estimation function to analyze the emotions a user has when reordering. For example, it suggests products that elicit positive emotions based on an emotion score. The generation AI also prioritizes suggesting products that elicit positive emotions based on the user's emotion data. For example, it suggests products that have a relaxing effect. The generation AI also uses the emotion estimation function to list products that will evoke positive emotions when the user reorders. For example, it prioritizes suggesting products with a high emotion score. In this way, by analyzing the user's emotions and suggesting products that elicit positive emotions, it is possible to improve user satisfaction.

[0087] The reorder suggestion unit can suggest an appropriate quantity based on the user's past consumption rate. The generation AI, for example, builds a system that suggests an appropriate quantity based on the user's past consumption rate. For example, it analyzes past consumption data and suggests the optimal number to order. The generation AI also takes consumption rate into consideration and suggests an appropriate quantity to the user. For example, it suggests a quantity that does not waste based on past consumption patterns. The generation AI also develops a system that optimizes the number of orders based on the user's consumption rate data. For example, it analyzes past consumption rate and suggests an appropriate quantity. This makes it possible to suggest an appropriate quantity by taking the user's past consumption rate into consideration.

[0088] The reorder suggestion unit can refer to the user's past return history and prevent over-ordering. The generation AI, for example, builds a system that prevents over-ordering based on the user's past return history. For example, it adjusts the quantity of items that are frequently returned to an appropriate quantity. The generation AI also analyzes the return history and makes suggestions to prevent over-ordering. For example, it adjusts the quantity of items that have been frequently returned in the past to an appropriate quantity. The generation AI also develops a system that prevents over-ordering based on the user's return history data. For example, it analyzes past return data and suggests appropriate quantities. In this way, over-ordering can be prevented by referring to the user's past return history.

[0089] The reorder suggestion unit can add a function to incorporate the opinions of the user's friends and family. For example, the generation AI adds a function to incorporate the opinions of the user's friends and family when optimizing the number of orders. For example, it collects opinions through a shared link and suggests the optimal quantity. The generation AI also builds a system that optimizes the number of orders based on the opinions of friends and family. For example, it adjusts the number of orders based on the shared opinions. The generation AI also adds a function to collect the opinions of friends and family when optimizing the number of orders. For example, it collects opinions through a shared link and suggests the optimal quantity. This makes it possible to make more appropriate reorder suggestions by incorporating the opinions of the user's friends and family.

[0090] The reorder suggestion unit can suggest products related to specific seasons or events based on the user's past purchase history. For example, the generation AI suggests products related to specific seasons or events based on the user's past purchase history. For example, it suggests cooling products in the summer. The generation AI also analyzes purchase history and lists products related to specific seasons or events. For example, it suggests gift products before Christmas. The generation AI also takes the user's purchase history into consideration and suggests products related to specific seasons or events. For example, it suggests anti-pollen products in the spring. This makes it possible to suggest products related to specific seasons or events based on the user's past purchase history, thereby making it possible to make suggestions that meet the user's needs.

[0091] The reorder suggestion unit can use the emotion estimation function to analyze the emotions a user feels when optimizing the order quantity and suggest quantities that elicit positive emotions. The generation AI, for example, uses the emotion estimation function to analyze the emotions a user feels when optimizing the order quantity. For example, it suggests quantities that elicit positive emotions based on the emotion score. The generation AI also suggests quantities that elicit positive emotions based on the user's emotion data. For example, it suggests quantities that will satisfy the user. The generation AI also uses the emotion estimation function to list quantities that evoke positive emotions when the user optimizes the order quantity. For example, it prioritizes suggesting quantities with high emotion scores. In this way, by analyzing the user's emotions and suggesting quantities that elicit positive emotions, it is possible to improve user satisfaction.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The suggestion unit can suggest entertainment content that can be enjoyed during hospitalization based on the user's hobbies and interests. For example, the suggestion unit can suggest the latest movies and dramas to a user who loves movies. It can also suggest e-books and audiobooks to a user who loves reading. It can also suggest relaxing music and podcasts to a user who loves music. This allows users to enjoy entertainment that suits their hobbies and interests even while hospitalized, making their hospital stay more comfortable.

[0094] The suggestion unit can suggest relaxation items that help reduce stress during hospitalization based on the user's emotional data. For example, if the user is feeling stressed, it can suggest an aroma diffuser or relaxing herbal tea. If the user is feeling anxious, it can suggest a meditation app or relaxation music. Furthermore, if the user is feeling tired, it can also suggest massage equipment or a relaxing cushion. In this way, by suggesting relaxation items that correspond to the user's emotions, it is possible to reduce stress during hospitalization and provide a comfortable environment.

[0095] The suggestion unit can suggest rehabilitation equipment needed during hospitalization based on the user's health condition. For example, for a user who needs foot rehabilitation, equipment that supports foot exercise can be suggested. Also, for a user who needs hand rehabilitation, equipment that supports hand exercise can be suggested. Furthermore, for a user who needs whole-body rehabilitation, it is also possible to suggest equipment that supports whole-body exercise. In this way, by suggesting rehabilitation equipment according to the user's health condition, rehabilitation during hospitalization can be effectively supported.

[0096] The suggestion unit can suggest communication tools to reduce loneliness during hospitalization based on the user's emotion data. For example, if the user feels lonely, it can suggest a video call app or online chat tool. If the user feels lonely, it can suggest a social media app or online game. Furthermore, if the user feels anxious, it can also suggest an online counseling service. In this way, by suggesting communication tools according to the user's emotions, it is possible to reduce loneliness during hospitalization and provide psychological support.

[0097] The suggestion unit can suggest meal plans suitable for the user's hospitalization based on the user's dietary restrictions. For example, a low-carbohydrate meal plan can be suggested for a diabetic user. Also, an allergy-friendly meal plan can be suggested for a user with allergies. Furthermore, it is possible to suggest meal plans that cater to vegetarians and vegans. In this way, it is possible to support dietary management during hospitalization by suggesting an appropriate meal plan according to the user's dietary restrictions.

[0098] The suggestion unit can suggest activities that are useful for changing the user's mood during hospitalization based on the user's emotional data. For example, if the user is feeling bored, it can suggest simple exercises or a craft kit. If the user is feeling stressed, it can suggest yoga or meditation activities. Furthermore, if the user is feeling anxious, it can also suggest relaxation activities or art therapy. In this way, by suggesting activities that correspond to the user's emotions, it is possible to support a change of mood during hospitalization and maintain mental health.

[0099] The suggestion unit can suggest daily necessities needed during hospitalization based on the user's past purchasing history. For example, it can suggest toiletries and towels that the user has purchased in the past. It can also suggest skin care products and hair care products that the user frequently purchases. It can also suggest stationery and electronic devices that the user uses on a daily basis. This allows the user to smoothly prepare daily necessities needed during hospitalization based on the user's past purchasing history.

[0100] The suggestion unit can support goal setting to maintain motivation during hospitalization based on the user's emotional data. For example, if the user is losing motivation, it can suggest setting small, achievable goals. Also, if the user is feeling anxious, it can suggest setting goals that will help them relax. Furthermore, if the user is feeling stressed, it can also suggest setting goals to reduce stress. In this way, by supporting goal setting according to the user's emotions, the user can maintain motivation during hospitalization and stay in a positive mood.

[0101] The suggestion unit can suggest local services that can be used during hospitalization based on the user's location information. For example, it can suggest delivery services from nearby restaurants and cafes. It can also suggest online services from local libraries and cultural facilities. It can also suggest local volunteer groups and support services. This makes hospitalization more convenient by suggesting local services that can be used during hospitalization based on the user's location information.

[0102] The suggestion unit can suggest positive messages and encouraging words to improve the user's mood during hospitalization based on the user's emotional data. For example, if the user is feeling down, an encouraging message can be sent. Also, if the user is feeling anxious, a message that gives a sense of security can be sent. Furthermore, if the user is feeling stressed, a message that helps the user relax can be sent. In this way, by suggesting positive messages that correspond to the user's emotions, it is possible to improve the user's mood during hospitalization and provide psychological support.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The usage history analysis unit analyzes the user's usage history. For example, the generation AI analyzes the user's PayPay usage history to understand the user's preferences and lifestyle. The generation AI also analyzes the products and services the user frequently purchases and suggests items that will be needed during hospitalization. Step 2: The suggestion unit suggests optimal hospitalization necessities based on the data analyzed by the usage history analysis unit. For example, the generation AI may list toiletries, clothing, food, and other items that the user uses on a daily basis and suggest them as items necessary for hospitalization. Step 3: The reorder suggestion unit suggests reordering the hospital necessities suggested by the suggestion unit. For example, the generation AI analyzes product data previously ordered by the user to predict usage patterns. The generation AI also analyzes the user's length of hospitalization and past order details to optimize the number of necessary items to order.

[0105] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a usage history analysis unit that analyzes a user's usage history; a suggestion unit that suggests optimal hospitalization necessities based on the data analyzed by the usage history analysis unit; a reorder suggestion unit that suggests a reorder of the hospital necessities suggested by the suggestion unit. A system characterized by:

2. The usage history analysis unit Collect user emotional data and predict changes in preferences based on emotional fluctuations 2. The system of claim 1.

3. The usage history analysis unit Prioritize recommendations for products purchased at specific times based on health status and lifestyle habits 2. The system of claim 1.

4. The usage history analysis unit Predicting changing tastes based on social media posts 2. The system of claim 1.

5. The proposal unit Suggest events or services related to your hobbies and interests 2. The system of claim 1.

6. The proposal unit Based on location information, localized hospital essentials are suggested.

2. The system of claim 1.

7. The proposal unit Analyzing the emotions a user feels when purchasing a specific product, and preferentially suggesting products that evoke positive emotions 2. The system of claim 1.

8. The proposal unit Recommend products for specific health conditions based on health checkup data 2. The system of claim 1.

Images