system

Abstract

The system according to this embodiment aims to streamline the storage of items and the management of unnecessary items. [Solution] The system according to the embodiment comprises a registration unit, a suggestion unit, a navigation unit, and a sales unit. The registration unit registers information about items. The suggestion unit proposes an efficient storage method based on the information registered by the registration unit. The navigation unit displays the storage location of items based on the storage method proposed by the suggestion unit. The sales unit sells unwanted items registered by the registration unit on an online marketplace.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the storage of items and the management of unnecessary items are not efficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of item storage and management of unnecessary items.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a suggestion unit, a navigation unit, and a sales unit. The registration unit registers information about items. The suggestion unit proposes an efficient storage method based on the information registered by the registration unit. The navigation unit displays the storage location of items based on the storage method proposed by the suggestion unit. The sales unit sells unwanted items registered by the registration unit on an online marketplace. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the storage of items and the management of unnecessary items. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The storage efficiency system according to an embodiment of the present invention is a system that optimizes storage and maximizes the use of time and space through the use of digitalization and AI. In this storage efficiency system, the user registers the items to be stored in the system, and the AI ​​analyzes this information to suggest the optimal storage method and location. For example, it suggests storing frequently used items in an easily accessible place, and seasonal items or items that are not used often in the back. Furthermore, when the user retrieves an item, the system displays the storage location of that item and navigates the user to easily retrieve it. For example, the user can use a smartphone app to check the storage location and quickly find the item they need. In addition, unwanted items can be sold through the system on an online marketplace. For example, when a user registers an unwanted item in the system, the AI ​​calculates the appropriate price for that item and lists it on the online marketplace. This allows the user to easily dispose of unwanted items and earn revenue. With this system, the user can efficiently utilize storage space and quickly find the items they need. Also, because unwanted items can be easily disposed of, wasted storage space can be reduced. In this way, the storage efficiency system allows the user to efficiently store items and quickly find the items they need. Additionally, you can easily dispose of unwanted items and even earn some money from them.

[0029] The storage efficiency system according to this embodiment comprises a registration unit, a suggestion unit, a navigation unit, and a sales unit. The registration unit registers information about items to be stored by the user. For example, information such as a photo of the item, a description, and the storage location can be entered. The suggestion unit uses AI to suggest the optimal storage method based on the information registered by the registration unit. For example, it suggests storing frequently used items in an easily accessible place, and seasonal items or items not used often in the back. The navigation unit displays the storage location of items based on the storage method suggested by the suggestion unit. For example, a smartphone app can be used to check the storage location and quickly find the necessary items. The sales unit sells unwanted items registered by the registration unit on an online marketplace. For example, AI calculates the appropriate price for the item and lists it on the online marketplace. As a result, the storage efficiency system according to this embodiment allows users to store items efficiently, quickly find the items they need, and easily dispose of unwanted items and generate revenue.

[0030] The suggestion department can propose efficient storage methods using AI. For example, the suggestion department uses AI to propose the optimal storage method based on information registered by the registration department. For instance, it might suggest storing frequently used items in easily accessible places and seasonal or rarely used items further back. The suggestion department can also use AI to optimize storage methods based on the frequency of use and category of items. For example, it can classify clothing by season and apply an algorithm for storage, classify books by genre and apply an algorithm for storage, and classify kitchenware by frequency of use and apply an algorithm for storage. This allows the suggestion department to improve the accuracy of its suggestions for optimal storage methods by using AI.

[0031] The navigation unit can display the storage location of items using AI. For example, the navigation unit uses AI to display the storage location of items based on the storage method suggested by the suggestion unit. For example, you can use a smartphone app to check the storage location and quickly find the item you need. The navigation unit can also use AI to suggest the optimal storage location based on the frequency of use, size, and shape of items. For example, it may suggest storing frequently used items in an easily accessible location, infrequently used items in a more distant location, large items in an easily accessible location, and small items in a group storage method. In this way, the navigation unit improves the accuracy of displaying item storage locations by using AI.

[0032] The sales department can use AI to calculate the fair price of unwanted items and list them on the online marketplace. For example, the sales department can use AI to calculate the fair price of unwanted items registered by the registration department and list them on the online marketplace. For example, the AI ​​calculates the fair price based on the item's market value, condition, and usage history. The sales department can also use AI to apply different pricing algorithms depending on the item's market value, condition, and usage history. For example, it might suggest a higher price for items in like-new condition and a lower price for items that have been used frequently. It can apply an algorithm to calculate an appropriate price according to the item's condition. As a result, the sales department can improve the accuracy of calculating the fair price of unwanted items by using AI.

[0033] The registration unit can register information such as item photos, descriptions, and storage locations. For example, the registration unit can register information about items that users will store. For example, it can input information such as item photos, descriptions, and storage locations. Item photos include requirements such as resolution and shooting angle. Descriptions include information such as item features and usage instructions. Storage locations include information such as a specific location in a room or a specific position on a shelf. As a result, the accuracy of the system is improved by the registration unit registering detailed information about items.

[0034] The navigation unit allows users to check storage locations using a smartphone app. For example, the navigation unit uses a smartphone app to check the storage location of items based on storage methods suggested by the suggestion unit. This includes features such as user interface and operation instructions. As a result, the navigation unit allows users to easily check storage locations using a smartphone app.

[0035] The registration unit can analyze the user's past registration history when registering an item and suggest an efficient registration method. For example, when registering an item, the registration unit analyzes the user's past registration history and suggests the optimal registration method. For example, it automatically displays information about items the user has frequently registered in the past as suggestions. It prioritizes suggesting registration methods the user has used in the past (voice, text, etc.). Based on the user's past registration history, it predicts and suggests items to be registered at specific times. In this way, by analyzing past registration history, it can suggest the optimal registration method.

[0036] The registration unit can filter items based on the user's current living situation and areas of interest when registering items. For example, it can prioritize registering items related to the user's current areas of interest. It can suggest necessary items according to the user's living situation (e.g., moving). It can automatically filter and display relevant items based on the user's areas of interest. This allows for the registration of highly relevant items by filtering based on the user's current living situation and areas of interest.

[0037] The registration unit can prioritize the registration of highly relevant items by considering the user's geographical location when registering items. For example, when registering items, the registration unit prioritizes the registration of highly relevant items by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize the registration of items related to that region. If the user is traveling, it will prioritize the registration of items related to the travel destination. If the user is participating in a specific event, it will prioritize the registration of items related to that event. In this way, by considering geographical location, it is possible to prioritize the registration of highly relevant items.

[0038] The registration unit can analyze a user's social media activity when registering an item and register relevant items. For example, it can prioritize registering items that the user frequently mentions on social media. It can suggest relevant items based on the content of the user's social media posts. It can prioritize registering items that the user's social media followers are interested in. In this way, relevant items can be registered by analyzing social media activity.

[0039] The proposal department can optimize storage methods based on the frequency of item use when making proposals. For example, when making proposals, the proposal department can optimize storage methods based on the frequency of item use. For example, it can suggest storing frequently used items in easily accessible locations. It can suggest storing less frequently used items further back. For seasonal items, it can suggest storing them further back during the off-season and in easily accessible locations during the season. By optimizing storage methods based on frequency of use, efficient storage becomes possible.

[0040] The proposal function can apply different storage algorithms depending on the item category when making a proposal. For example, it can apply different storage algorithms depending on the item category when making a proposal. For instance, it can classify and store clothing by season, classify and store books by genre, and classify and store kitchenware by frequency of use. This allows for efficient storage by applying storage algorithms appropriate to each category.

[0041] The proposal department can adjust storage methods based on the seasonality of items when making proposals. For example, the proposal department can adjust storage methods based on the seasonality of items when making proposals. For example, it can propose storing seasonal items at the back during the off-season and in an easily accessible location during the season. It can propose methods for classifying and storing items used by each season. It can propose a method of storing off-season items at the back and moving in-season items to an easily accessible location at the change of seasons. By adjusting storage methods based on the season, efficient storage becomes possible.

[0042] The proposal department can customize storage methods based on the relationships between items when making a proposal. For example, the proposal department can customize storage methods based on the relationships between items when making a proposal. For example, it can propose a method for storing related items together. It can propose a method for storing frequently used items together. It can propose a method for storing items of the same category together. By customizing storage methods based on relationships, efficient storage becomes possible.

[0043] The navigation unit can suggest efficient routes based on the frequency of item retrieval during navigation. For example, it suggests storing frequently used items in easily accessible locations, and less frequently used items in the back. For seasonal items, it suggests storing them in the back during the off-season and in easily accessible locations during the season. This enables efficient retrieval by suggesting the optimal route based on item retrieval frequency.

[0044] The navigation unit can apply different navigation algorithms depending on the size and shape of the items during navigation. For example, it might suggest storing large items in easily accessible locations, suggest grouping small items together, or suggest storage locations suitable for items of specific shapes. By applying navigation algorithms tailored to size and shape, efficient retrieval becomes possible.

[0045] The navigation unit can suggest efficient retrieval methods based on the weight of items during navigation. For example, it might suggest storing heavy items in easily accessible locations, or suggest storing light items together. For items of a specific weight, it might suggest a storage location suitable for that weight. This enables efficient retrieval by suggesting the optimal retrieval method based on weight.

[0046] The navigation unit can customize storage locations based on the relationships between items during navigation. For example, it can suggest ways to store related items together, frequently used items together, or items of the same category together. This allows for efficient storage by customizing storage locations based on relationships.

[0047] The sales department can propose an efficient selling price based on the market value of the item at the time of sale. For example, the sales department will propose the optimal selling price based on the market value of the item at the time of sale. For example, a higher price will be proposed for items with high market value, and a lower price for items with low market value. The sales department will propose the optimal selling price considering fluctuations in market value. This enables efficient sales by proposing the optimal selling price based on market value.

[0048] The sales department can apply different pricing algorithms at the time of sale, depending on the item's condition and usage history. For example, the sales department might suggest a higher price for items in like-new condition and a lower price for items that have been used frequently. They apply an algorithm to calculate an appropriate price based on the item's condition. This allows for appropriate pricing by applying pricing algorithms tailored to the item's condition and usage history.

[0049] The sales department can adjust prices based on the seasonality of items at the time of sale. For example, the sales department might suggest higher prices for seasonal items during the season and lower prices for off-season items. They might also suggest adjusting prices at the change of seasons. This allows for more efficient sales by adjusting prices based on seasonality.

[0050] The sales department can optimize pricing by referring to relevant market data for items at the time of sale. For example, the sales department can optimize pricing by referring to relevant market data for items at the time of sale. For example, it can refer to market prices of items in the same category and suggest the optimal price. It can suggest ways to adjust prices considering market supply and demand. It can analyze market trends and suggest the optimal price. In this way, optimal pricing becomes possible by referring to relevant market data.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The proposal department can optimize storage methods based on the frequency of item use when making proposals. For example, they can suggest storing frequently used items in easily accessible locations, and less frequently used items in the back. For seasonal items, they can suggest storing them in the back during the off-season and in easily accessible locations during the season. By optimizing storage methods based on usage frequency, efficient storage becomes possible.

[0053] The navigation unit can suggest efficient routes based on how often items are retrieved during navigation. For example, it can suggest storing frequently used items in easily accessible locations, and less frequently used items further back. For seasonal items, it can suggest storing them further back during the off-season and in easily accessible locations during the season. This allows for efficient retrieval by suggesting the optimal route based on retrieval frequency.

[0054] The sales department can propose an efficient selling price based on the market value of an item at the time of sale. For example, a higher price will be proposed for items with high market value, and a lower price for items with low market value. The optimal selling price will be proposed considering fluctuations in market value. This enables efficient sales by proposing the optimal selling price based on market value.

[0055] The registration function can analyze a user's past registration history when registering items and suggest efficient registration methods. For example, it can automatically display information about items the user has frequently registered in the past as suggestions. It can also prioritize suggesting registration methods the user has used in the past (voice, text, etc.). Based on the user's past registration history, it can predict and suggest items to be registered at specific times. In this way, by analyzing past registration history, it can suggest the optimal registration method.

[0056] The navigation unit can apply different navigation algorithms depending on the size and shape of the items during navigation. For example, it can suggest storing large items in easily accessible locations, suggest methods for storing small items together, and suggest storage locations suitable for items of specific shapes. By applying navigation algorithms tailored to size and shape, efficient retrieval becomes possible.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The registration section allows users to register information about the items they will store. For example, they can enter information such as a photo of the item, a description, and the storage location. Step 2: The suggestion unit uses AI to propose the optimal storage method based on the information registered by the registration unit. For example, it suggests storing frequently used items in easily accessible places, and seasonal items or items not used often in the back. Step 3: The navigation unit displays the storage location of items based on the storage method proposed by the suggestion unit. For example, you can use a smartphone app to check the storage location and quickly find the items you need. Step 4: The sales department sells the unwanted items registered by the registration department on an online marketplace. For example, AI calculates the appropriate price for the item and lists it on the online marketplace.

[0059] (Example of form 2) The storage efficiency system according to an embodiment of the present invention is a system that optimizes storage and maximizes the use of time and space through the use of digitalization and AI. In this storage efficiency system, the user registers the items to be stored in the system, and the AI ​​analyzes this information to suggest the optimal storage method and location. For example, it suggests storing frequently used items in an easily accessible place, and seasonal items or items that are not used often in the back. Furthermore, when the user retrieves an item, the system displays the storage location of that item and navigates the user to easily retrieve it. For example, the user can use a smartphone app to check the storage location and quickly find the item they need. In addition, unwanted items can be sold through the system on an online marketplace. For example, when a user registers an unwanted item in the system, the AI ​​calculates the appropriate price for that item and lists it on the online marketplace. This allows the user to easily dispose of unwanted items and earn revenue. With this system, the user can efficiently utilize storage space and quickly find the items they need. Also, because unwanted items can be easily disposed of, wasted storage space can be reduced. In this way, the storage efficiency system allows the user to efficiently store items and quickly find the items they need. Additionally, you can easily dispose of unwanted items and even earn some money from them.

[0060] The storage efficiency system according to this embodiment comprises a registration unit, a suggestion unit, a navigation unit, and a sales unit. The registration unit registers information about items to be stored by the user. For example, information such as a photo of the item, a description, and the storage location can be entered. The suggestion unit uses AI to suggest the optimal storage method based on the information registered by the registration unit. For example, it suggests storing frequently used items in an easily accessible place, and seasonal items or items not used often in the back. The navigation unit displays the storage location of items based on the storage method suggested by the suggestion unit. For example, a smartphone app can be used to check the storage location and quickly find the necessary items. The sales unit sells unwanted items registered by the registration unit on an online marketplace. For example, AI calculates the appropriate price for the item and lists it on the online marketplace. As a result, the storage efficiency system according to this embodiment allows users to store items efficiently, quickly find the items they need, and easily dispose of unwanted items and generate revenue.

[0061] The suggestion department can propose efficient storage methods using AI. For example, the suggestion department uses AI to propose the optimal storage method based on information registered by the registration department. For instance, it might suggest storing frequently used items in easily accessible places and seasonal or rarely used items further back. The suggestion department can also use AI to optimize storage methods based on the frequency of use and category of items. For example, it can classify clothing by season and apply an algorithm for storage, classify books by genre and apply an algorithm for storage, and classify kitchenware by frequency of use and apply an algorithm for storage. This allows the suggestion department to improve the accuracy of its suggestions for optimal storage methods by using AI.

[0062] The navigation unit can display the storage location of items using AI. For example, the navigation unit uses AI to display the storage location of items based on the storage method suggested by the suggestion unit. For example, you can use a smartphone app to check the storage location and quickly find the item you need. The navigation unit can also use AI to suggest the optimal storage location based on the frequency of use, size, and shape of items. For example, it may suggest storing frequently used items in an easily accessible location, infrequently used items in a more distant location, large items in an easily accessible location, and small items in a group storage method. In this way, the navigation unit improves the accuracy of displaying item storage locations by using AI.

[0063] The sales department can use AI to calculate the fair price of unwanted items and list them on the online marketplace. For example, the sales department can use AI to calculate the fair price of unwanted items registered by the registration department and list them on the online marketplace. For example, the AI ​​calculates the fair price based on the item's market value, condition, and usage history. The sales department can also use AI to apply different pricing algorithms depending on the item's market value, condition, and usage history. For example, it might suggest a higher price for items in like-new condition and a lower price for items that have been used frequently. It can apply an algorithm to calculate an appropriate price according to the item's condition. As a result, the sales department can improve the accuracy of calculating the fair price of unwanted items by using AI.

[0064] The registration unit can register information such as item photos, descriptions, and storage locations. For example, the registration unit can register information about items that users will store. For example, it can input information such as item photos, descriptions, and storage locations. Item photos include requirements such as resolution and shooting angle. Descriptions include information such as item features and usage instructions. Storage locations include information such as a specific location in a room or a specific position on a shelf. As a result, the accuracy of the system is improved by the registration unit registering detailed information about items.

[0065] The navigation unit allows users to check storage locations using a smartphone app. For example, the navigation unit uses a smartphone app to check the storage location of items based on storage methods suggested by the suggestion unit. This includes features such as user interface and operation instructions. As a result, the navigation unit allows users to easily check storage locations using a smartphone app.

[0066] The registration unit can estimate the user's emotions and adjust the timing of item registration based on the estimated emotions. For example, if the user is stressed, the registration procedure may be simplified to allow for quick registration. If the user is relaxed, detailed registration options may be provided, and a customizable registration method may be suggested. If the user is in a hurry, voice input may be prioritized to allow for quick item registration. This allows for item registration at a more appropriate time by adjusting the registration timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The registration unit can analyze the user's past registration history when registering an item and suggest an efficient registration method. For example, when registering an item, the registration unit analyzes the user's past registration history and suggests the optimal registration method. For example, it automatically displays information about items the user has frequently registered in the past as suggestions. It prioritizes suggesting registration methods the user has used in the past (voice, text, etc.). Based on the user's past registration history, it predicts and suggests items to be registered at specific times. In this way, by analyzing past registration history, it can suggest the optimal registration method.

[0068] The registration unit can filter items based on the user's current living situation and areas of interest when registering items. For example, it can prioritize registering items related to the user's current areas of interest. It can suggest necessary items according to the user's living situation (e.g., moving). It can automatically filter and display relevant items based on the user's areas of interest. This allows for the registration of highly relevant items by filtering based on the user's current living situation and areas of interest.

[0069] The registration unit can estimate the user's emotions and determine the priority of items to register based on the estimated emotions. For example, if the user is stressed, important items will be registered first. If the user is relaxed, detailed registration options will be provided and a customizable registration method will be suggested. If the user is in a hurry, voice input will be prioritized to allow for quick item registration. This allows for the priority registration of important items by determining item priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The registration unit can prioritize the registration of highly relevant items by considering the user's geographical location when registering items. For example, when registering items, the registration unit prioritizes the registration of highly relevant items by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize the registration of items related to that region. If the user is traveling, it will prioritize the registration of items related to the travel destination. If the user is participating in a specific event, it will prioritize the registration of items related to that event. In this way, by considering geographical location, it is possible to prioritize the registration of highly relevant items.

[0071] The registration unit can analyze a user's social media activity when registering an item and register relevant items. For example, it can prioritize registering items that the user frequently mentions on social media. It can suggest relevant items based on the content of the user's social media posts. It can prioritize registering items that the user's social media followers are interested in. In this way, relevant items can be registered by analyzing social media activity.

[0072] The suggestion unit can estimate the user's emotions and adjust the suggested storage methods based on those emotions. For example, if the user is relaxed, it might suggest a storage method that proceeds at a leisurely pace. If the user is in a hurry, it might suggest a method that allows for storage in the shortest possible time. If the user is excited, it might suggest a visually stimulating storage method. By adjusting the suggested storage methods according to the user's emotions, a more appropriate storage method can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The proposal department can optimize storage methods based on the frequency of item use when making proposals. For example, when making proposals, the proposal department can optimize storage methods based on the frequency of item use. For example, it can suggest storing frequently used items in easily accessible locations. It can suggest storing less frequently used items further back. For seasonal items, it can suggest storing them further back during the off-season and in easily accessible locations during the season. By optimizing storage methods based on frequency of use, efficient storage becomes possible.

[0074] The proposal function can apply different storage algorithms depending on the item category when making a proposal. For example, it can apply different storage algorithms depending on the item category when making a proposal. For instance, it can classify and store clothing by season, classify and store books by genre, and classify and store kitchenware by frequency of use. This allows for efficient storage by applying storage algorithms appropriate to each category.

[0075] The suggestion function can estimate the user's emotions and determine storage priorities based on those emotions. For example, if the user is stressed, it might suggest prioritizing the storage of important items. If the user is relaxed, it might offer detailed storage options and suggest customizable storage methods. If the user is in a hurry, it might prioritize voice input to allow for quick storage of items. This allows for prioritizing storage of important items by determining storage priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The proposal department can adjust storage methods based on the seasonality of items when making proposals. For example, the proposal department can adjust storage methods based on the seasonality of items when making proposals. For example, it can propose storing seasonal items at the back during the off-season and in an easily accessible location during the season. It can propose methods for classifying and storing items used by each season. It can propose a method of storing off-season items at the back and moving in-season items to an easily accessible location at the change of seasons. By adjusting storage methods based on the season, efficient storage becomes possible.

[0077] The proposal department can customize storage methods based on the relationships between items when making a proposal. For example, the proposal department can customize storage methods based on the relationships between items when making a proposal. For example, it can propose a method for storing related items together. It can propose a method for storing frequently used items together. It can propose a method for storing items of the same category together. By customizing storage methods based on relationships, efficient storage becomes possible.

[0078] The navigation unit can estimate the user's emotions and adjust the display method of storage locations based on the estimated emotions. For example, if the user is nervous, it provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The navigation unit can suggest efficient routes based on the frequency of item retrieval during navigation. For example, it suggests storing frequently used items in easily accessible locations, and less frequently used items in the back. For seasonal items, it suggests storing them in the back during the off-season and in easily accessible locations during the season. This enables efficient retrieval by suggesting the optimal route based on item retrieval frequency.

[0080] The navigation unit can apply different navigation algorithms depending on the size and shape of the items during navigation. For example, it might suggest storing large items in easily accessible locations, suggest grouping small items together, or suggest storage locations suitable for items of specific shapes. By applying navigation algorithms tailored to size and shape, efficient retrieval becomes possible.

[0081] The navigation unit can estimate the user's emotions and determine the display order of storage locations based on the estimated emotions. For example, if the user is stressed, important items will be displayed preferentially. If the user is relaxed, a display method including detailed information will be provided. If the user is in a hurry, a display method that gets straight to the point will be provided. This allows important items to be displayed preferentially by determining the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The navigation unit can suggest efficient retrieval methods based on the weight of items during navigation. For example, it might suggest storing heavy items in easily accessible locations, or suggest storing light items together. For items of a specific weight, it might suggest a storage location suitable for that weight. This enables efficient retrieval by suggesting the optimal retrieval method based on weight.

[0083] The navigation unit can customize storage locations based on the relationships between items during navigation. For example, it can suggest ways to store related items together, frequently used items together, or items of the same category together. This allows for efficient storage by customizing storage locations based on relationships.

[0084] The sales department can estimate the user's emotions and adjust the pricing method based on those emotions. For example, if the user is relaxed, detailed pricing options may be offered. If the user is in a hurry, a quick pricing method may be suggested. If the user is excited, a visually stimulating pricing method may be suggested. This allows for more appropriate pricing by adjusting the pricing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The sales department can propose an efficient selling price based on the market value of the item at the time of sale. For example, the sales department will propose the optimal selling price based on the market value of the item at the time of sale. For example, a higher price will be proposed for items with high market value, and a lower price for items with low market value. The sales department will propose the optimal selling price considering fluctuations in market value. This enables efficient sales by proposing the optimal selling price based on market value.

[0086] The sales department can apply different pricing algorithms at the time of sale, depending on the item's condition and usage history. For example, the sales department might suggest a higher price for items in like-new condition and a lower price for items that have been used frequently. They apply an algorithm to calculate an appropriate price based on the item's condition. This allows for appropriate pricing by applying pricing algorithms tailored to the item's condition and usage history.

[0087] The sales department can estimate user emotions and prioritize sales prices based on those emotions. For example, if a user is relaxed, detailed pricing options may be offered. If a user is in a hurry, a quick pricing method may be suggested. If a user is excited, a visually stimulating pricing method may be suggested. This allows for the priority sale of important items by prioritizing prices according to user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The sales department can adjust prices based on the seasonality of items at the time of sale. For example, the sales department might suggest higher prices for seasonal items during the season and lower prices for off-season items. They might also suggest adjusting prices at the change of seasons. This allows for more efficient sales by adjusting prices based on seasonality.

[0089] The sales department can optimize pricing by referring to relevant market data for items at the time of sale. For example, the sales department can optimize pricing by referring to relevant market data for items at the time of sale. For example, it can refer to market prices of items in the same category and suggest the optimal price. It can suggest ways to adjust prices considering market supply and demand. It can analyze market trends and suggest the optimal price. In this way, optimal pricing becomes possible by referring to relevant market data. === Hard Collateral 1-1 === Each of the multiple elements described above, including the registration unit, proposal unit, navigation unit, and sales unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, allowing the user to input information about an item. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses AI to suggest the optimal storage method. The navigation unit is implemented, for example, by the control unit 46A of the smart device 14, which displays the suggested storage location. The sales unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which lists unwanted items on an online marketplace. === Hard Collateral 1-2 === Each of the multiple elements described above, including the registration unit, suggestion unit, navigation unit, and sales unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input information about an item. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses AI to suggest the optimal storage method. The navigation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which displays the suggested storage location. The sales unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which lists unwanted items on an online marketplace. === Hard Collateral 1-3 === Each of the multiple elements described above, including the registration unit, proposal unit, navigation unit, and sales unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input information about an item. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to suggest the optimal storage method. The navigation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which displays the suggested storage location. The sales unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which lists unwanted items on an online marketplace. === Hard Collateral 1-4 === Each of the multiple elements described above, including the registration unit, proposal unit, navigation unit, and sales unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, allowing the user to input information about an item. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to suggest the optimal storage method. The navigation unit is implemented by, for example, the control unit 46A of the robot 414, which displays the suggested storage location. The sales unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which lists unwanted items on an online marketplace.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The suggestion function can estimate the user's emotions and adjust the suggested storage methods based on those emotions. For example, if the user is relaxed, it will suggest a storage method that proceeds at a leisurely pace. If the user is in a hurry, it will suggest a method that allows for storage in the shortest possible time. If the user is excited, it will suggest a storage method that is visually stimulating. In this way, by adjusting the suggested storage methods according to the user's emotions, a more appropriate storage method can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The navigation unit can estimate the user's emotions and adjust the display method of storage locations based on the estimated emotions. For example, if the user is tense, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that gets straight to the point is provided. This allows for more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The sales department can estimate the user's emotions and adjust the pricing method based on those emotions. For example, if the user is relaxed, detailed pricing options can be offered. If the user is in a hurry, a quick pricing method can be suggested. If the user is excited, a visually stimulating pricing method can be suggested. This allows for more appropriate pricing by adjusting the pricing method according to the user's emotions. Emotion estimation can be achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The registration unit can estimate the user's emotions and adjust the timing of item registration based on the estimated emotions. For example, if the user is stressed, the registration procedure can be simplified to allow for quick registration. If the user is relaxed, detailed registration options can be provided, and a customizable registration method can be suggested. If the user is in a hurry, voice input can be prioritized to allow for quick item registration. This allows for item registration at a more appropriate time by adjusting the registration timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The suggestion function can estimate the user's emotions and determine storage priorities based on those emotions. For example, if the user is stressed, it suggests prioritizing the storage of important items. If the user is relaxed, it provides detailed storage options and suggests customizable storage methods. If the user is in a hurry, it prioritizes voice input to allow for quick item storage. This ensures that important items are stored preferentially by prioritizing storage methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The proposal department can optimize storage methods based on the frequency of item use when making proposals. For example, they can suggest storing frequently used items in easily accessible locations, and less frequently used items in the back. For seasonal items, they can suggest storing them in the back during the off-season and in easily accessible locations during the season. By optimizing storage methods based on usage frequency, efficient storage becomes possible.

[0097] The navigation unit can suggest efficient routes based on how often items are retrieved during navigation. For example, it can suggest storing frequently used items in easily accessible locations, and less frequently used items further back. For seasonal items, it can suggest storing them further back during the off-season and in easily accessible locations during the season. This allows for efficient retrieval by suggesting the optimal route based on retrieval frequency.

[0098] The sales department can propose an efficient selling price based on the market value of an item at the time of sale. For example, a higher price will be proposed for items with high market value, and a lower price for items with low market value. The optimal selling price will be proposed considering fluctuations in market value. This enables efficient sales by proposing the optimal selling price based on market value.

[0099] The registration function can analyze a user's past registration history when registering items and suggest efficient registration methods. For example, it can automatically display information about items the user has frequently registered in the past as suggestions. It can also prioritize suggesting registration methods the user has used in the past (voice, text, etc.). Based on the user's past registration history, it can predict and suggest items to be registered at specific times. In this way, by analyzing past registration history, it can suggest the optimal registration method.

[0100] The navigation unit can apply different navigation algorithms depending on the size and shape of the items during navigation. For example, it can suggest storing large items in easily accessible locations, suggest methods for storing small items together, and suggest storage locations suitable for items of specific shapes. By applying navigation algorithms tailored to size and shape, efficient retrieval becomes possible.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The registration section allows users to register information about the items they will store. For example, they can enter information such as a photo of the item, a description, and the storage location. Step 2: The suggestion unit uses AI to propose the optimal storage method based on the information registered by the registration unit. For example, it suggests storing frequently used items in easily accessible places, and seasonal items or items not used often in the back. Step 3: The navigation unit displays the storage location of items based on the storage method proposed by the suggestion unit. For example, you can use a smartphone app to check the storage location and quickly find the items you need. Step 4: The sales department sells the unwanted items registered by the registration department on an online marketplace. For example, AI calculates the appropriate price for the item and lists it on the online marketplace.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

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

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration section for registering item information, A proposal unit that proposes an efficient storage method based on the information registered by the registration unit, A navigation unit that displays the storage location of an item based on the storage method proposed by the aforementioned proposal unit, The system includes a sales unit that sells unwanted items registered by the registration unit on an online marketplace. A system characterized by the following features.

2. The aforementioned proposal section is, AI proposes efficient storage methods. The system according to feature 1.

3. The aforementioned navigation unit is AI displays the storage location of items. The system according to feature 1.

4. The aforementioned sales department, The AI ​​calculates the fair price for unwanted items and lists them on online marketplaces. The system according to feature 1.

5. The aforementioned registration unit is Register the item's photo, description, and storage location information. The system according to feature 1.

6. The aforementioned navigation unit is Use a smartphone app to check the storage location. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of item registration based on those emotions. The system according to feature 1.

8. The aforementioned registration unit is When registering an item, the system analyzes the user's past registration history and suggests an efficient registration method. The system according to feature 1.

Images