System

Abstract

An object of the system according to the embodiment is to enable a user to realize infinite ownership and efficiently obtain what the user wants.SOLUTION: A system includes a trunk room use part, a delivery management part, a clothes recommendation part, and an item take-out part. The trunk room use part manages the use and delivery of the trunk room. The delivery management unit delivers the item in the trunk room managed by the trunk room use unit. The clothes recommendation unit recommends clothes based on the past purchase history and preferences of the user. The item taking-out part takes out the item from the trunk room based on the instruction of the user.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 technologies have had the problem of making it difficult for users to realize unlimited ownership and efficiently obtain what they want.

[0005] The system according to the embodiment aims to enable users to realize unlimited possessions and efficiently obtain what they want. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage room utilization unit, a delivery management unit, an outfit recommendation unit, and an item retrieval unit. The storage room utilization unit manages the use and delivery of storage rooms. The delivery management unit delivers items in storage rooms managed by the storage room utilization unit. The outfit recommendation unit recommends outfits based on the user's past purchase history and preferences. The item retrieval unit retrieves items from storage rooms based on the user's instructions. [Effects of the Invention]

[0007] The system according to the embodiment allows users to realize unlimited possessions and efficiently obtain what they want. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 system for providing delivery lockers and e-commerce services according to the embodiment of the present invention is a system that uses a generation AI and a generation AI to enable users to have unlimited possessions and fulfill their desires.As a result, the system for providing delivery lockers and e-commerce services allows users to have unlimited possessions and fulfill their desires.

[0029] A system providing delivery lockers and e-commerce services according to an embodiment includes a storage unit utilization unit, a delivery management unit, an outfit recommendation unit, and an item retrieval unit. The storage unit utilization unit manages storage unit utilization and delivery. For example, the storage unit utilization unit manages storage unit utilization and delivery. For example, the storage unit utilization unit allows a user to use a storage unit for a fixed monthly fee and receive up to three delivery services per month. The storage unit utilization unit can automatically suggest optimal storage locations for items based on the user's lifestyle and usage frequency using a generation AI. For example, the generation AI analyzes the user's lifestyle data and classifies items into frequently used and less frequently used items. The delivery management unit delivers items stored in the storage unit managed by the storage unit utilization unit. For example, the delivery management unit delivers items at a date and time specified by the user. The delivery management unit can also optimize delivery schedules using a generation AI. For example, the generation AI analyzes the user's schedule and suggests optimal delivery dates and times. The outfit recommendation unit recommends outfits based on the user's past purchase history and preferences. For example, the outfit recommendation unit can suggest optimal outfits based on the user's body type and skin color using a generation AI. For example, the generation AI analyzes the user's body data and suggests the most suitable outfit. The item retrieval unit retrieves items from the storage room based on the user's instructions. For example, the item retrieval unit can use the generation AI to optimize the order in which items are retrieved based on the user's instructions. For example, the generation AI analyzes the user's instructions and optimizes the order in which items are retrieved. In this way, the system that provides delivery lockers and e-commerce services allows users to have unlimited possessions and fulfill their desires.

[0030] The storage unit can use the generation AI to automatically suggest optimal storage locations for items based on the user's lifestyle or frequency of use. For example, the generation AI analyzes the user's lifestyle data and classifies items into those used frequently and those used less frequently. For example, items used daily are placed in the front, while seasonal items are placed in the back. The generation AI also predicts how often an item will be used based on the user's past usage history and suggests optimal storage locations. For example, sports equipment is placed in the back during the off-season and in the front during the season. The generation AI also learns the user's lifestyle patterns and suggests storage locations that take into account the ease of access to items. For example, items used during the morning commute are placed in the front, while items used on weekends are placed in the back. This allows the system to suggest optimal storage locations for items based on the user's lifestyle and frequency of use.

[0031] The trunk room usage unit uses a generation AI to analyze the user's past usage history, predict the next item they will need, and prepare it in advance. For example, the generation AI in the trunk room usage unit analyzes the user's past usage history and predicts the next item they will need. For example, it can predict from past data that camping equipment will be needed next weekend and prepare it in advance. The trunk room usage unit also learns the user's usage patterns, and the generation AI automatically retrieves the next item they will need. For example, it can prepare sports equipment used every weekend in advance. The trunk room usage unit also uses a generation AI to analyze the user's schedule and predict the next item they will need. For example, it can prepare items needed for the next trip in advance based on the user's calendar information. This allows the user's past usage history to be analyzed and the next item they will need to be prepared in advance.

[0032] The storage room utilization unit can monitor storage room usage in real time and provide a function to share free space with other users. For example, the storage room utilization unit builds a system to monitor storage room usage in real time and share free space with other users. For example, it provides free space to other users during times when there are fewer users. The storage room utilization unit also uses a generation AI to analyze storage room usage and make suggestions for efficient sharing of free space. For example, items that will not be used for a long period of time can be temporarily shared with other users. The storage room utilization unit also develops a reservation system for sharing free space in a storage room with other users. For example, it displays the available times of free space and allows other users to make reservations. This allows free space in a storage room to be shared efficiently.

[0033] The storage unit can use the generation AI to automatically suggest maintenance or cleaning services for items stored by the user. For example, the generation AI analyzes the items stored by the user and automatically suggests items that require maintenance or cleaning. For example, it suggests cleaning clothes that have not been used for a long time. The storage unit also uses the generation AI to predict and suggest the timing of maintenance or cleaning based on the user's usage history. For example, it suggests maintenance for sports equipment after the season ends. The generation AI also monitors the condition of items and automatically arranges maintenance or cleaning services as needed. For example, it suggests repairs for items that are showing signs of deterioration. This allows the storage unit to automatically suggest maintenance or cleaning services for items stored by the user.

[0034] The clothing recommendation unit can use the generation AI to suggest the most suitable clothing based on the user's body type or skin color. For example, the generation AI analyzes the user's body type data and suggests the most suitable clothing. For example, it suggests clothes with a silhouette and size that suits the body type. The generation AI also suggests clothing with the most suitable colors based on the user's skin color. For example, it suggests a color palette that matches the skin color. The generation AI also comprehensively analyzes the user's body type and skin color and suggests the most suitable coordination. For example, it suggests designs that flatter the body type and accessories that match the skin color. This makes it possible to suggest the most suitable clothing based on the user's body type and skin color.

[0035] The clothing recommendation unit can use the generation AI to suggest appropriate clothing to match the user's schedule or event. For example, the generation AI analyzes the user's schedule and suggests clothing that matches the event. For example, it might suggest formal clothing for a business meeting and relaxed clothing for a casual event. The clothing recommendation unit also uses the generation AI to suggest appropriate clothing based on the user's calendar information. For example, it might suggest clothing that matches the season and weather. The clothing recommendation unit also uses the generation AI to analyze the user's event information and suggest the optimal outfit. For example, it might suggest dresses or suits that are suitable for weddings or parties. This makes it possible to suggest appropriate clothing to match the user's schedule and event.

[0036] The clothing recommendation unit can use the generation AI to analyze the fashion styles of the user's friends or family and make coordination suggestions. For example, the generation AI analyzes the fashion styles of the user's friends and family and suggests coordinations that suit the user. For example, it can suggest matching items with friends. The clothing recommendation unit also analyzes the fashion styles of friends and family based on the user's social media data and makes coordination suggestions. For example, it analyzes photos posted by friends to understand trends. The generation AI also learns the fashion styles of the user's friends and family and suggests coordinations that suit the user. For example, it can suggest outfits that go well with a family event. This makes it possible to analyze the fashion styles of the user's friends and family and make coordination suggestions.

[0037] The clothing recommendation unit uses the generation AI to analyze the user's past fashion mistakes and make suggestions to avoid making the same mistakes. For example, the generation AI analyzes the user's past fashion mistakes and makes suggestions to avoid making the same mistakes. For example, it makes suggestions to avoid outfits that were unpopular in the past. The clothing recommendation unit also analyzes fashion mistakes based on the user's past purchase history and makes suggestions for improvement. For example, it makes suggestions to avoid items that were purchased but not worn in the past. The generation AI also learns the user's past fashion mistakes and makes suggestions for outfits to avoid making the same mistakes. For example, it makes suggestions to avoid colors and designs that did not suit them in the past. This allows the generation AI to analyze the user's past fashion mistakes and make suggestions to avoid making the same mistakes.

[0038] The item retrieval unit can use the generation AI to optimize the order in which items are taken based on the user's instructions. For example, the generation AI in the item retrieval unit analyzes the user's instructions and optimizes the order in which items are taken. For example, it proposes an order for efficiently retrieving multiple items. The item retrieval unit also proposes the optimal order for retrieval based on the user's past retrieval history. For example, it may retrieving frequently used items first. The item retrieval unit also analyzes the user's schedule and prepares the next item that will be needed in advance. For example, it may move items that are used on weekends to a location that is easy to retrieve on weekdays. This allows the order in which items are taken to be retrieved to be optimized based on the user's instructions.

[0039] The item retrieval unit uses the generation AI to analyze the user's past retrieval history, predict the next item that will be needed, and prepare it in advance. For example, the item retrieval unit uses the generation AI to analyze the user's past retrieval history and predict the next item that will be needed. For example, it predicts from past data that camping equipment will be needed next weekend and prepares it in advance. The item retrieval unit also learns the user's usage patterns, and the generation AI automatically retrieves the next item that will be needed. For example, it prepares sports equipment used every weekend in advance. The item retrieval unit also uses the generation AI to analyze the user's schedule and predict the next item that will be needed. For example, it prepares items needed for the next trip in advance based on the user's calendar information. This allows the user's past retrieval history to be analyzed and the next item that will be needed to be prepared in advance.

[0040] The item retrieval unit can use the generation AI to control a robotic arm that automates item retrieval based on user instructions. For example, the generation AI in the item retrieval unit analyzes the user's instructions and controls the robotic arm to automatically retrieve items. For example, when a user issues instructions via smartphone, the robotic arm retrieves the specified item. The generation AI in the item retrieval unit also controls the robotic arm to execute the optimal retrieval order based on the user's past retrieval history. For example, it may retrieve frequently used items first. The generation AI in the item retrieval unit also analyzes the user's schedule and controls the robotic arm to prepare the next item needed in advance. For example, it may move items used on weekends to a location that is easy to retrieve on weekdays. This allows the robotic arm that automates item retrieval to be controlled based on user instructions.

[0041] The item retrieval unit can provide a function that uses the generation AI to cooperate with other users in retrieving items based on the user's instructions. For example, the item retrieval unit constructs a system in which the generation AI analyzes the user's instructions and cooperates with other users to retrieve items. For example, users who use the same storage room cooperate in retrieving items. The item retrieval unit also proposes the optimal retrieval order in cooperation with other users based on the user's past retrieval history. For example, users who retrieve the same item cooperate with each other. The item retrieval unit also analyzes the user's schedule and cooperates with other users to prepare the next item that will be needed in advance. For example, users who are participating in the same event retrieve items. This makes it possible to provide a function that allows other users to cooperate in retrieving items based on the user's instructions.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The trunk room usage unit can monitor the user's health status and suggest health-conscious items. For example, if it detects that the user is not getting enough exercise, it can suggest using exercise equipment. It can also analyze the user's dietary data and suggest storing healthy ingredients. Furthermore, it can also suggest items to create a comfortable sleeping environment based on the user's sleep data.

[0044] The delivery management unit can suggest environmentally friendly delivery options based on the user's eco-consciousness. For example, it can suggest changing the packaging materials used during delivery to recyclable ones. It can also suggest optimizing delivery routes and reducing carbon dioxide emissions. It can also provide an incentive program that allows users to accumulate eco-points.

[0045] The clothing recommendation unit can suggest outfits based on specific themes based on the user's hobbies and interests. For example, if the user is a movie fan, the unit can suggest outfits inspired by movie characters. If the user is planning to attend a music festival, the unit can suggest casual outfits suitable for the festival. Furthermore, if the user is planning a trip, the unit can suggest outfits that match the culture and climate of the destination.

[0046] The item retrieval unit can predict how often items will be retrieved based on the user's past retrieval history and suggest an efficient retrieval order. For example, it can place frequently used items at the front to make them easier to retrieve. It can also arrange items used seasonally so that they can be retrieved at the appropriate time. It can also analyze the user's schedule and prepare the next item they will need in advance.

[0047] The clothing recommendation section can analyze a user's past fashion mistakes and make suggestions to help them avoid the same mistakes. For example, it can make suggestions to avoid outfits that were unpopular in the past. The AI ​​generation can also analyze a user's fashion mistakes based on their past purchase history and make suggestions for improvement. It can also make suggestions to avoid colors or designs that didn't suit them in the past.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The storage unit manages storage unit usage and deliveries. For example, it manages the system so that users can use the storage unit for a fixed monthly fee and receive up to three delivery services per month. It also uses a generation AI to automatically suggest the optimal storage location for items based on the user's lifestyle and frequency of use. The generation AI analyzes the user's lifestyle data and classifies items into those that are used frequently and those that are not. Step 2: The delivery management unit delivers the items in the storage unit managed by the storage unit. For example, it delivers the items at the date and time specified by the user. It also uses a generation AI to optimize the delivery schedule. The generation AI analyzes the user's schedule and proposes the optimal delivery date and time. Step 3: The clothing recommendation unit recommends clothing based on the user's past purchase history and preferences. For example, it uses a generation AI to suggest the most suitable clothing based on the user's body type and skin color. The generation AI analyzes the user's body type data and suggests the most suitable clothing. Step 4: The item retrieval unit retrieves items from the storage room based on the user's instructions. For example, a generation AI is used to optimize the order in which items are retrieved based on the user's instructions. The generation AI analyzes the user's instructions and optimizes the order in which items are retrieved.

[0050] (Example 2) The system for providing delivery lockers and e-commerce services according to the embodiment of the present invention is a system that uses a generation AI and a generation AI to enable users to have unlimited possessions and fulfill their desires.As a result, the system for providing delivery lockers and e-commerce services allows users to have unlimited possessions and fulfill their desires.

[0051] A system providing delivery lockers and e-commerce services according to an embodiment includes a storage unit utilization unit, a delivery management unit, an outfit recommendation unit, and an item retrieval unit. The storage unit utilization unit manages storage unit utilization and delivery. For example, the storage unit utilization unit manages storage unit utilization and delivery. For example, the storage unit utilization unit allows a user to use a storage unit for a fixed monthly fee and receive up to three delivery services per month. The storage unit utilization unit can automatically suggest optimal storage locations for items based on the user's lifestyle and usage frequency using a generation AI. For example, the generation AI analyzes the user's lifestyle data and classifies items into frequently used and less frequently used items. The delivery management unit delivers items stored in the storage unit managed by the storage unit utilization unit. For example, the delivery management unit delivers items at a date and time specified by the user. The delivery management unit can also optimize delivery schedules using a generation AI. For example, the generation AI analyzes the user's schedule and suggests optimal delivery dates and times. The outfit recommendation unit recommends outfits based on the user's past purchase history and preferences. For example, the outfit recommendation unit can suggest optimal outfits based on the user's body type and skin color using a generation AI. For example, the generation AI analyzes the user's body data and suggests the most suitable outfit. The item retrieval unit retrieves items from the storage room based on the user's instructions. For example, the item retrieval unit can use the generation AI to optimize the order in which items are retrieved based on the user's instructions. For example, the generation AI analyzes the user's instructions and optimizes the order in which items are retrieved. In this way, the system that provides delivery lockers and e-commerce services allows users to have unlimited possessions and fulfill their desires.

[0052] The storage unit can use the generation AI to automatically suggest optimal storage locations for items based on the user's lifestyle or frequency of use. For example, the generation AI analyzes the user's lifestyle data and classifies items into those used frequently and those used less frequently. For example, items used daily are placed in the front, while seasonal items are placed in the back. The generation AI also predicts how often an item will be used based on the user's past usage history and suggests optimal storage locations. For example, sports equipment is placed in the back during the off-season and in the front during the season. The generation AI also learns the user's lifestyle patterns and suggests storage locations that take into account the ease of access to items. For example, items used during the morning commute are placed in the front, while items used on weekends are placed in the back. This allows the system to suggest optimal storage locations for items based on the user's lifestyle and frequency of use.

[0053] The trunk room usage unit uses a generation AI to analyze the user's past usage history, predict the next item they will need, and prepare it in advance. For example, the generation AI in the trunk room usage unit analyzes the user's past usage history and predicts the next item they will need. For example, it can predict from past data that camping equipment will be needed next weekend and prepare it in advance. The trunk room usage unit also learns the user's usage patterns, and the generation AI automatically retrieves the next item they will need. For example, it can prepare sports equipment used every weekend in advance. The trunk room usage unit also uses a generation AI to analyze the user's schedule and predict the next item they will need. For example, it can prepare items needed for the next trip in advance based on the user's calendar information. This allows the user's past usage history to be analyzed and the next item they will need to be prepared in advance.

[0054] The storage room usage unit can use the emotion estimation function to analyze the emotions of the user when depositing an item and make customized suggestions to reduce stress. For example, the storage room usage unit can use the emotion estimation function to analyze the emotions of the user when depositing an item in real time and make suggestions to reduce stress. For example, if the user is feeling stressed, the storage room usage unit can make the item deposit with a simple procedure. The storage room usage unit also uses the generation AI to make customized suggestions based on the user's emotion data. For example, if the user is feeling anxious, the storage room usage unit can display a message emphasizing the safety of the deposited item. The storage room usage unit also uses the emotion estimation function to analyze the emotions of the user when depositing an item and provide an interface to reduce stress. For example, the system can provide designs or music that help the user relax. This can reduce stress when the user deposits an item.

[0055] The storage room utilization unit can monitor storage room usage in real time and provide a function to share free space with other users. For example, the storage room utilization unit builds a system to monitor storage room usage in real time and share free space with other users. For example, it provides free space to other users during times when there are fewer users. The storage room utilization unit also uses a generation AI to analyze storage room usage and make suggestions for efficient sharing of free space. For example, items that will not be used for a long period of time can be temporarily shared with other users. The storage room utilization unit also develops a reservation system for sharing free space in a storage room with other users. For example, it displays the available times of free space and allows other users to make reservations. This allows free space in a storage room to be shared efficiently.

[0056] The storage unit can use the generation AI to automatically suggest maintenance or cleaning services for items stored by the user. For example, the generation AI analyzes the items stored by the user and automatically suggests items that require maintenance or cleaning. For example, it suggests cleaning clothes that have not been used for a long time. The storage unit also uses the generation AI to predict and suggest the timing of maintenance or cleaning based on the user's usage history. For example, it suggests maintenance for sports equipment after the season ends. The generation AI also monitors the condition of items and automatically arranges maintenance or cleaning services as needed. For example, it suggests repairs for items that are showing signs of deterioration. This allows the storage unit to automatically suggest maintenance or cleaning services for items stored by the user.

[0057] The trunk room utilization unit can use the emotion estimation function to analyze the emotions of the user when retrieving an item and provide support to provide a positive experience. For example, the trunk room utilization unit can use the emotion estimation function to analyze the emotions of the user when retrieving an item in real time and provide support to provide a positive experience. For example, it can display a message that makes the user feel happy. The trunk room utilization unit also provides support customized by the generation AI based on the user's emotion data. For example, if the user is feeling anxious, it can make suggestions to simplify the retrieval procedure. The trunk room utilization unit also uses the emotion estimation function to analyze the emotions of the user when retrieving an item and provide an interface to provide a positive experience. For example, it can provide designs or music that help the user relax. In this way, it can analyze the emotions of the user when retrieving an item and provide a positive experience.

[0058] The clothing recommendation unit can use the generation AI to suggest the most suitable clothing based on the user's body type or skin color. For example, the generation AI analyzes the user's body type data and suggests the most suitable clothing. For example, it suggests clothes with a silhouette and size that suits the body type. The generation AI also suggests clothing with the most suitable colors based on the user's skin color. For example, it suggests a color palette that matches the skin color. The generation AI also comprehensively analyzes the user's body type and skin color and suggests the most suitable coordination. For example, it suggests designs that flatter the body type and accessories that match the skin color. This makes it possible to suggest the most suitable clothing based on the user's body type and skin color.

[0059] The clothing recommendation unit can use the generation AI to suggest appropriate clothing to match the user's schedule or event. For example, the generation AI analyzes the user's schedule and suggests clothing that matches the event. For example, it might suggest formal clothing for a business meeting and relaxed clothing for a casual event. The clothing recommendation unit also uses the generation AI to suggest appropriate clothing based on the user's calendar information. For example, it might suggest clothing that matches the season and weather. The clothing recommendation unit also uses the generation AI to analyze the user's event information and suggest the optimal outfit. For example, it might suggest dresses or suits that are suitable for weddings or parties. This makes it possible to suggest appropriate clothing to match the user's schedule and event.

[0060] The clothing recommendation unit can use the generation AI to analyze the fashion styles of the user's friends or family and make coordination suggestions. For example, the generation AI analyzes the fashion styles of the user's friends and family and suggests coordinations that suit the user. For example, it can suggest matching items with friends. The clothing recommendation unit also analyzes the fashion styles of friends and family based on the user's social media data and makes coordination suggestions. For example, it analyzes photos posted by friends to understand trends. The generation AI also learns the fashion styles of the user's friends and family and suggests coordinations that suit the user. For example, it can suggest outfits that go well with a family event. This makes it possible to analyze the fashion styles of the user's friends and family and make coordination suggestions.

[0061] The clothing recommendation unit uses the generation AI to analyze the user's past fashion mistakes and make suggestions to avoid making the same mistakes. For example, the generation AI analyzes the user's past fashion mistakes and makes suggestions to avoid making the same mistakes. For example, it makes suggestions to avoid outfits that were unpopular in the past. The clothing recommendation unit also analyzes fashion mistakes based on the user's past purchase history and makes suggestions for improvement. For example, it makes suggestions to avoid items that were purchased but not worn in the past. The generation AI also learns the user's past fashion mistakes and makes suggestions for outfits to avoid making the same mistakes. For example, it makes suggestions to avoid colors and designs that did not suit them in the past. This allows the generation AI to analyze the user's past fashion mistakes and make suggestions to avoid making the same mistakes.

[0062] The clothing recommendation unit uses the emotion estimation function to analyze the emotions of a user when trying on clothes, and can provide an optimal fitting experience. The clothing recommendation unit, for example, uses the emotion estimation function to analyze the emotions of a user when trying on clothes in real time, and can provide an optimal fitting experience. For example, it provides a fitting environment that makes the user feel happy. Furthermore, the clothing recommendation unit uses the generation AI to make customized fitting suggestions based on the user's emotion data. For example, it provides a fitting experience that makes the user feel confident. Furthermore, the clothing recommendation unit uses the emotion estimation function to analyze the emotions of a user when trying on clothes, and provides an interface for providing an optimal fitting experience. For example, it provides designs and music that help the user relax. In this way, it is possible to analyze the emotions of a user when trying on clothes, and can provide an optimal fitting experience.

[0063] The item retrieval unit can use the generation AI to optimize the order in which items are taken based on the user's instructions. For example, the generation AI in the item retrieval unit analyzes the user's instructions and optimizes the order in which items are taken. For example, it proposes an order for efficiently retrieving multiple items. The item retrieval unit also proposes the optimal order for retrieval based on the user's past retrieval history. For example, it may retrieving frequently used items first. The item retrieval unit also analyzes the user's schedule and prepares the next item that will be needed in advance. For example, it may move items that are used on weekends to a location that is easy to retrieve on weekdays. This allows the order in which items are taken to be retrieved to be optimized based on the user's instructions.

[0064] The item retrieval unit uses the generation AI to analyze the user's past retrieval history, predict the next item that will be needed, and prepare it in advance. For example, the item retrieval unit uses the generation AI to analyze the user's past retrieval history and predict the next item that will be needed. For example, it predicts from past data that camping equipment will be needed next weekend and prepares it in advance. The item retrieval unit also learns the user's usage patterns, and the generation AI automatically retrieves the next item that will be needed. For example, it prepares sports equipment used every weekend in advance. The item retrieval unit also uses the generation AI to analyze the user's schedule and predict the next item that will be needed. For example, it prepares items needed for the next trip in advance based on the user's calendar information. This allows the user's past retrieval history to be analyzed and the next item that will be needed to be prepared in advance.

[0065] The item retrieval unit can use the emotion estimation function to analyze the emotion a user feels when retrieving an item and make customized suggestions to reduce stress. For example, the item retrieval unit can use the emotion estimation function to analyze the emotion a user feels when retrieving an item in real time and make suggestions to reduce stress. For example, if the user is feeling stressed, the item retrieval unit can make suggestions to reduce stress through a simple procedure. The item retrieval unit also uses the emotion estimation function to analyze the emotion a user feels when retrieving an item and provide an interface to reduce stress. For example, the item retrieval unit can provide designs or music that help the user relax. This can reduce stress when the user retrieves an item.

[0066] The item retrieval unit can use the generation AI to control a robotic arm that automates item retrieval based on user instructions. For example, the generation AI in the item retrieval unit analyzes the user's instructions and controls the robotic arm to automatically retrieve items. For example, when a user issues instructions via smartphone, the robotic arm retrieves the specified item. The generation AI in the item retrieval unit also controls the robotic arm to execute the optimal retrieval order based on the user's past retrieval history. For example, it may retrieve frequently used items first. The generation AI in the item retrieval unit also analyzes the user's schedule and controls the robotic arm to prepare the next item needed in advance. For example, it may move items used on weekends to a location that is easy to retrieve on weekdays. This allows the robotic arm that automates item retrieval to be controlled based on user instructions.

[0067] The item retrieval unit can provide a function that uses the generation AI to cooperate with other users in retrieving items based on the user's instructions. For example, the item retrieval unit constructs a system in which the generation AI analyzes the user's instructions and cooperates with other users to retrieve items. For example, users who use the same storage room cooperate in retrieving items. The item retrieval unit also proposes the optimal retrieval order in cooperation with other users based on the user's past retrieval history. For example, users who retrieve the same item cooperate with each other. The item retrieval unit also analyzes the user's schedule and cooperates with other users to prepare the next item that will be needed in advance. For example, users who are participating in the same event retrieve items. This makes it possible to provide a function that allows other users to cooperate in retrieving items based on the user's instructions.

[0068] The item retrieval unit can use the emotion estimation function to analyze the emotion a user feels when retrieving an item and provide support to provide a positive experience. For example, the item retrieval unit can use the emotion estimation function to analyze the emotion a user feels when retrieving an item in real time and provide support to provide a positive experience. For example, it can display a message that makes the user feel happy. The item retrieval unit also provides support customized by the generation AI based on the user's emotion data. For example, if the user feels anxious, it can make a suggestion to simplify the retrieval procedure. The item retrieval unit also uses the emotion estimation function to analyze the emotion a user feels when retrieving an item and provide an interface to provide a positive experience. For example, it can provide a design or music that helps the user relax. In this way, it is possible to analyze the emotion a user feels when retrieving an item and provide a positive experience.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The trunk room usage unit can monitor the user's health status and suggest health-conscious items. For example, if it detects that the user is not getting enough exercise, it can suggest using exercise equipment. It can also analyze the user's dietary data and suggest storing healthy ingredients. Furthermore, it can also suggest items to create a comfortable sleeping environment based on the user's sleep data.

[0071] The delivery management unit can suggest environmentally friendly delivery options based on the user's eco-consciousness. For example, it can suggest changing the packaging materials used during delivery to recyclable ones. It can also suggest optimizing delivery routes and reducing carbon dioxide emissions. It can also provide an incentive program that allows users to accumulate eco-points.

[0072] The clothing recommendation unit can suggest outfits based on specific themes based on the user's hobbies and interests. For example, if the user is a movie fan, the unit can suggest outfits inspired by movie characters. If the user is planning to attend a music festival, the unit can suggest casual outfits suitable for the festival. Furthermore, if the user is planning a trip, the unit can suggest outfits that match the culture and climate of the destination.

[0073] The item retrieval unit can predict how often items will be retrieved based on the user's past retrieval history and suggest an efficient retrieval order. For example, it can place frequently used items at the front to make them easier to retrieve. It can also arrange items used seasonally so that they can be retrieved at the appropriate time. It can also analyze the user's schedule and prepare the next item they will need in advance.

[0074] The trunk room usage unit can estimate the user's emotions and make customized suggestions to reduce stress. For example, if the user is feeling stressed, it can suggest using items that will help them relax. If the user is feeling anxious, it can also display a message that gives a sense of security. Furthermore, the generation AI can make customized suggestions based on the user's emotional data.

[0075] The delivery management unit can estimate the user's emotions and make suggestions to reduce stress during delivery. For example, if the user is feeling anxious, it can notify the delivery status in real time. It can also display messages that make the user feel happy. Furthermore, the generative AI can make customized delivery suggestions based on the user's emotional data.

[0076] The clothing recommendation unit can estimate the user's emotions and make suggestions to reduce stress when trying on clothes. For example, if the user is feeling anxious, it can make suggestions to simplify the trying-on process. It can also provide a fitting environment that makes the user feel happy. Furthermore, the generation AI can make customized fitting suggestions based on the user's emotional data.

[0077] The item retrieval unit can estimate the user's emotions and make suggestions to reduce stress when retrieving items. For example, if the user is feeling stressed, it can make the retrieval process easier. Also, if the user is feeling anxious, it can make suggestions to simplify the retrieval procedure. Furthermore, the generation AI can make customized suggestions based on the user's emotional data.

[0078] The trunk room user section can estimate the user's emotions and make suggestions to reduce stress when storing items. For example, if the user is feeling stressed, it can make it easier to store items. If the user is feeling anxious, it can display a message emphasizing the safety of the stored items. Furthermore, the generation AI can make customized suggestions based on the user's emotional data.

[0079] The clothing recommendation section can analyze a user's past fashion mistakes and make suggestions to help them avoid the same mistakes. For example, it can make suggestions to avoid outfits that were unpopular in the past. The AI ​​generation can also analyze a user's fashion mistakes based on their past purchase history and make suggestions for improvement. It can also make suggestions to avoid colors or designs that didn't suit them in the past.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The storage unit manages storage unit usage and deliveries. For example, it manages the system so that users can use the storage unit for a fixed monthly fee and receive up to three delivery services per month. It also uses a generation AI to automatically suggest the optimal storage location for items based on the user's lifestyle and frequency of use. The generation AI analyzes the user's lifestyle data and classifies items into those that are used frequently and those that are not. Step 2: The delivery management unit delivers the items in the storage unit managed by the storage unit. For example, it delivers the items at the date and time specified by the user. It also uses a generation AI to optimize the delivery schedule. The generation AI analyzes the user's schedule and proposes the optimal delivery date and time. Step 3: The clothing recommendation unit recommends clothing based on the user's past purchase history and preferences. For example, it uses a generation AI to suggest the most suitable clothing based on the user's body type and skin color. The generation AI analyzes the user's body type data and suggests the most suitable clothing. Step 4: The item retrieval unit retrieves items from the storage room based on the user's instructions. For example, a generation AI is used to optimize the order in which items are retrieved based on the user's instructions. The generation AI analyzes the user's instructions and optimizes the order in which items are retrieved.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 system that uses generative AI and generative AI to provide delivery lockers and e-commerce services that allow users to realize unlimited ownership and fulfill their desires. A trunk room utilization department that manages the use and delivery of trunk rooms; a delivery management unit that delivers the items in the storage room managed by the storage room utilization unit; an outfit recommendation unit that recommends outfits based on the user's past purchase history and preferences; an item removal unit that removes an item from the trunk room based on an instruction from the user; A system characterized by:

2. The trunk room utilization section includes: Using the generative AI to automatically suggest optimal storage locations for the items based on the user's lifestyle or frequency of use.

2. The system of claim 1.

3. The trunk room utilization section includes: Provide a function to monitor the usage status of the storage room in real time and share the available space with other users 2. The system of claim 1.

4. The clothing recommendation unit The generation AI is used to suggest the most suitable outfit based on the user's body type or skin color.

2. The system of claim 1.

5. The item removal unit includes: Optimizing the order in which the items are taken out based on instructions from the user using the generating AI.

2. The system of claim 1.

6. The trunk room utilization section includes: Analyzing the user's emotions when depositing the item and providing customized suggestions to reduce stress 2. The system of claim 1.

7. The clothing recommendation unit Analyzing the emotions of the user when selecting the clothing and making suggestions that elicit positive emotions 2. The system of claim 1.

8. The item removal unit includes: Analyzing the user's emotions when removing the item and providing customized suggestions to reduce stress 2. The system of claim 1.

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