System

Abstract

An object of a system according to an embodiment is to propose an optimal storage method by measuring the dimensions of a storage location and categorizing stored items.SOLUTION: A system includes a reception part, an analysis part, a categorization part, and a proposal part. The reception unit receives an image captured by an imaging device. The analysis unit analyzes the image received by the reception unit and measures the dimensions of the storage space. The categorizer categorizes the stored item based on the image analyzed by the analyzer. The proposal unit proposes an appropriate storage method based on the result of categorization by the categorization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately measure the dimensions of a storage space, appropriately categorize stored items, and propose optimal storage methods.

[0005] The system according to the embodiment aims to measure the dimensions of a storage space, categorize stored items, and propose the most suitable storage method. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a categorization unit, and a suggestion unit. The reception unit receives images captured by an imaging device. The analysis unit analyzes the images received by the reception unit and measures the dimensions of the storage location. The categorization unit categorizes the stored items based on the images analyzed by the analysis unit. The suggestion unit suggests an appropriate storage method based on the results of categorization by the categorization unit. [Effects of the Invention]

[0007] The system according to the embodiment can measure the dimensions of a storage space, categorize stored items, and propose the most suitable storage method. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A storage assistance system according to an embodiment of the present invention measures the dimensions of a storage space, categorizes items to be stored, and suggests optimal storage methods based on images taken by a user. The storage assistance system uses a camera to capture images of the space or items the user wants to organize and store. The system then automatically measures the dimensions of the captured image using object recognition technology based on deep learning. For example, it accurately measures the height, width, and depth of the interior of a closet or shelves. Next, the storage assistance system analyzes the captured image and automatically categorizes the stored items. For example, it classifies items into different categories, such as clothing, books, and kitchenware. Finally, the storage assistance system suggests optimal storage methods based on the measurement and categorization results. For example, it suggests specific storage methods, such as whether clothing should be hung on hangers or stored in drawers, and which shelf books should be placed on. This allows the user to efficiently utilize storage space. This allows even those who are not good at storage to easily organize and maximize storage space.

[0029] A storage assistance system according to an embodiment includes a reception unit, an analysis unit, a categorization unit, and a suggestion unit. The reception unit receives images captured by a user using a camera. For example, the reception unit can receive images captured using a smartphone or digital camera. The reception unit can also receive common image formats, such as JPEG, PNG, and TIFF, regardless of the image format. The reception unit can also perform appropriate processing depending on the image resolution and size. The analysis unit uses object recognition technology based on deep learning to analyze the images received by the reception unit and measure the dimensions of the storage space. For example, the analysis unit can accurately measure the height, width, and depth of the interior of a closet or shelves. The analysis unit can also recognize objects in the images and measure their dimensions. Furthermore, the analysis unit can analyze the position and shape of objects in the images using a deep learning model. The categorization unit categorizes stored items based on the images analyzed by the analysis unit. For example, the categorization unit can classify items into different categories, such as clothing, books, and kitchenware. The categorization unit can also use AI to automatically identify objects in images and classify them into appropriate categories. Furthermore, the categorization unit can improve the accuracy of categorization based on user input information and past data. The suggestion unit suggests optimal storage methods based on the categorization results by the categorization unit. For example, the suggestion unit suggests specific storage methods, such as whether clothes should be hung on hangers or stored in drawers, or which shelf books should be placed on. The suggestion unit can also use AI to suggest optimal storage methods based on user needs and storage space characteristics. Furthermore, the suggestion unit can improve the accuracy of suggestions based on user feedback. As a result, the storage assistance system according to the embodiment can measure the dimensions of storage locations, categorize storage items, and suggest optimal storage methods based on images taken by the user.

[0030] The reception unit can analyze the user's past image reception history and select an appropriate reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. For example, the reception unit can analyze the user's past image reception history and select the optimal reception method. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past reception history. For example, the reception unit can select the most efficient reception method based on the user's past reception history. This enables efficient image reception by selecting the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past image reception history data into the generation AI and have the generation AI select the optimal reception method.

[0031] When receiving images, the reception unit may filter the images based on the user's current project or area of ​​interest. For example, the reception unit may preferentially receive images related to a project currently underway by the user. For example, the reception unit may identify the current project or area of ​​interest based on the user's input information and past activity history. The reception unit may also filter and receive highly relevant images based on the user's area of ​​interest. For example, the reception unit may select and receive necessary images according to the progress of the user's project. In this way, by filtering images based on the user's project or area of ​​interest, highly relevant images can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's input information and past activity history data to a generation AI and cause the generation AI to identify the user's current project or area of ​​interest.

[0032] When accepting an image, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user uses voice input, the acceptance unit accepts the image using voice recognition technology. For example, the acceptance unit records the user's voice with a microphone and converts it into text data using voice recognition technology. Furthermore, when the user uses text input, the acceptance unit can also accept the image using text analysis technology. For example, the acceptance unit analyzes the user's text input and accepts an appropriate image. Furthermore, when the user uses image input, the acceptance unit can also accept the image using image recognition technology. For example, the acceptance unit analyzes an image taken by the user and performs appropriate processing. This enables efficient image acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can input the user's voice data or text data into a generation AI and have the generation AI select an appropriate acceptance means.

[0033] When accepting images, the reception unit can prioritize accepting highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting images related to that area. For example, the reception unit acquires the user's geographical location information using GPS data or a location information service. The reception unit can also filter and accept highly relevant images based on the user's current location. For example, the reception unit selects and accepts optimal images based on the user's geographical location information. This allows highly relevant images to be preferentially accepted by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant images.

[0034] When accepting images, the acceptance unit can analyze the user's social media activity and accept related images. The acceptance unit, for example, preferentially accepts images shared by the user on social media. For example, the acceptance unit analyzes the content of the user's social media posts and filters and accepts related images. The acceptance unit can also accept related images with reference to the activity of the user's friends on social media. For example, the acceptance unit selects and accepts optimal images based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially accept highly relevant images. Some or all of the above-described processing by the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's social media activity data into a generation AI and cause the generation AI to select related images.

[0035] When accepting an image, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. The acceptance unit, for example, proposes an optimal acceptance method based on feedback provided by the user in the past. For example, the acceptance unit analyzes the user's past feedback and customizes the acceptance method. The acceptance unit can also improve the acceptance method by reflecting the user's feedback. For example, the acceptance unit selects an optimal acceptance method based on the user's past feedback. In this way, the optimal acceptance method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acceptance method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the storage location. For example, the analysis unit performs a detailed analysis on storage locations with high importance. For example, the analysis unit evaluates the importance based on the frequency of use and storage capacity of the storage location. The analysis unit can also perform a simplified analysis on storage locations with low importance. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the storage location. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the storage location. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input storage location importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the storage location. For example, in the case of a closet, the analysis unit applies an analysis algorithm specialized for clothing. For example, the analysis unit analyzes the types and arrangement of clothing in the closet. In addition, in the case of a kitchen, the analysis unit can also apply an analysis algorithm specialized for kitchen utensils. For example, the analysis unit analyzes the arrangement of cooking utensils and tableware in the kitchen. In addition, in the case of a bookshelf, the analysis unit can also apply an analysis algorithm specialized for books. For example, the analysis unit analyzes the types and arrangement of books. In this way, by applying an analysis algorithm depending on the category of the storage location, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input storage location category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit refers to the user's past analysis data and result evaluations. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and improves the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the storage location was photographed. For example, the analysis unit prioritizes analysis of storage locations that were photographed most recently. For example, the analysis unit obtains the time when the storage location was photographed using a timestamp or calendar data. The analysis unit can also postpone storage locations that were photographed earlier. For example, the analysis unit determines the analysis priority based on the time when the storage location was photographed. This enables efficient analysis by determining the analysis priority based on the time when the storage location was photographed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data on the time when the storage location was photographed into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of storage locations. The analysis unit, for example, prioritizes analysis of highly relevant storage locations. For example, the analysis unit evaluates the relevance based on the physical proximity of the storage locations or the similarity of their intended uses. The analysis unit can also postpone storage locations with low relevance. For example, the analysis unit adjusts the order of analysis based on the relevance of storage locations. This enables efficient analysis by adjusting the order of analysis based on the relevance of storage locations. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input storage location relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit evaluates the user's level of expertise based on the user's qualifications and past experience. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simpler terms. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0042] The categorization unit can improve the accuracy of categorization by taking into account the interrelationships between stored items during categorization. The categorization unit, for example, analyzes the interrelationships between stored items and classifies highly related items into the same category. For example, the categorization unit evaluates the interrelationships based on the frequency of use and relevance of the stored items. The categorization unit can also improve the accuracy of categorization by taking into account the interrelationships between stored items. For example, the categorization unit proposes an optimal categorization method based on the interrelationships between stored items. In this way, the accuracy of categorization can be improved by taking into account the interrelationships between stored items. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input interrelationship data between stored items into a generation AI and cause the generation AI to improve the accuracy of categorization.

[0043] The categorization unit can take into account attribute information of the person who submitted the stored items when categorizing. The categorization unit, for example, proposes an optimal categorization method based on the attribute information of the person who submitted the stored items. For example, the categorization unit adjusts the categorization criteria based on attribute information such as the submitter's age and occupation. The categorization unit can also improve the accuracy of categorization by taking into account the attribute information of the person who submitted the stored items. For example, the categorization unit analyzes the attribute information of the submitter and selects the optimal categorization method. This enables more appropriate categorization by taking into account the attribute information of the person who submitted the stored items. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input attribute information data of the submitter to a generation AI and have the generation AI adjust the categorization criteria.

[0044] The categorization unit can weight the categorization based on the submission frequency of the stored items when categorizing. For example, the categorization unit prioritizes categorization of stored items with a high submission frequency. For example, the categorization unit weights the stored items based on the submission frequency data. The categorization unit can also postpone categorization of stored items with a low submission frequency. For example, the categorization unit weights the categorization based on the submission frequency. This enables efficient categorization by weighting the categorization based on the submission frequency of the stored items. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input the submission frequency data to a generation AI and cause the generation AI to perform categorization weighting.

[0045] The categorization unit can take the geographic distribution of stored items into consideration when categorizing. The categorization unit, for example, proposes an optimal categorization method based on the geographic distribution of stored items. For example, the categorization unit evaluates the geographic distribution of stored items based on distribution by region and location information. The categorization unit can also improve the accuracy of categorization by taking the geographic distribution of stored items into consideration. For example, the categorization unit analyzes the geographic distribution of stored items and selects an optimal categorization method. This enables more appropriate categorization by taking the geographic distribution of stored items into consideration. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input geographic distribution data of stored items to a generation AI and cause the generation AI to improve the accuracy of categorization.

[0046] The categorization unit can improve the accuracy of categorization by referring to literature related to the stored items during categorization. The categorization unit, for example, refers to literature related to the stored items and proposes an optimal categorization method. For example, the categorization unit categorizes the stored items based on related literature such as academic papers and technical reports. The categorization unit can also improve the accuracy of categorization based on the literature related to the stored items. For example, the categorization unit analyzes literature related to the stored items and selects an optimal categorization method. In this way, by referring to the literature related to the stored items, the accuracy of categorization can be improved. Some or all of the above-mentioned processing in the categorization unit may be performed using, or without, AI, for example. For example, the categorization unit can input related literature data into a generation AI and cause the generation AI to improve the accuracy of categorization.

[0047] The categorization unit can take into account the market value of the stored items when categorizing. For example, the categorization unit prioritizes categorization of stored items with high market value. For example, the categorization unit evaluates the market value based on the price and demand of the stored items. The categorization unit can also postpone categorization of stored items with low market value. For example, the categorization unit weights the categorization based on the market value. This allows for more appropriate categorization by taking the market value of the stored items into consideration. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input market value data of the stored items into the generation AI and have the generation AI perform the categorization weighting.

[0048] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the stored item. For example, the proposal unit makes a detailed proposal for a stored item with a high level of importance. For example, the proposal unit evaluates the importance of the stored item based on the frequency of use and value of the stored item. The proposal unit can also make a simplified proposal for a stored item with a low level of importance. For example, the proposal unit adjusts the level of detail of the proposal according to the importance of the stored item. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the stored item. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input importance data of the stored items to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0049] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the stored item. For example, in the case of clothing, the suggestion unit suggests using hangers or drawers. For example, the suggestion unit makes the optimal suggestion based on the type of clothing and the storage method. In addition, in the case of books, the suggestion unit can also suggest shelf arrangement and storage method. For example, the suggestion unit makes the optimal suggestion based on the type and arrangement of books. In addition, in the case of kitchenware, the suggestion unit can also suggest storage locations and organization methods. For example, the suggestion unit makes the optimal suggestion based on the type and frequency of use of kitchenware. In this way, by applying a suggestion algorithm depending on the category of the stored item, more accurate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of the stored items to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0050] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, optimizes the proposal algorithm based on the user's past proposal results. For example, the suggestion unit refers to the user's past proposal data and feedback. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results and improves the accuracy of the proposal. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0051] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the storage item. For example, the proposal unit prioritizes proposals for recently submitted storage items. For example, the proposal unit obtains the submission date of the storage item using a timestamp or calendar data. The proposal unit can also postpone storage items that were submitted earlier. For example, the proposal unit determines the priority of the proposal based on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the storage item. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission date of the storage item to the generation AI and have the generation AI determine the priority of the proposals.

[0052] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the stored items. For example, the suggestion unit prioritizes suggestions for highly relevant stored items. For example, the suggestion unit evaluates the relevance based on the purpose of use of the stored items and physical proximity. The suggestion unit can also postpone suggestions for less relevant stored items. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the stored items. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the stored items. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the stored items to the generation AI and cause the generation AI to adjust the order of suggestions.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, the suggestion unit evaluates the user's level of expertise based on the user's qualifications and past experience. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal in simpler terms. For example, the suggestion unit adjusts the use of technical terminology according to the user's level of expertise. This enables more appropriate proposals by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze the user's past behavioral patterns and predict the optimal timing for accepting images. For example, the reception unit can predict the next timing for accepting images based on the time period and frequency at which the user took images in the past. The reception unit can also refer to the user's calendar information and schedule to suggest the optimal timing. Furthermore, the reception unit can learn the user's daily rhythm and automatically adjust the optimal acceptance timing. This allows for more efficient image acceptance by optimizing the timing for accepting images based on the user's behavioral patterns.

[0056] The reception unit can provide an image reception method that corresponds to the characteristics of each region, taking into account the user's geographical location information. For example, if the user is in an urban area, the reception unit can suggest an image reception method that corresponds to the storage needs unique to urban areas. Also, if the user is in a rural area, the reception unit can suggest an image reception method that corresponds to the storage needs unique to rural areas. Furthermore, the reception unit can provide an image reception method that corresponds to the culture and customs of each region, based on the user's geographical location information. In this way, a more appropriate image reception method can be provided by taking into account the user's geographical location information.

[0057] The analysis unit can improve the accuracy of the analysis by taking into account the environmental conditions of the storage location. For example, the analysis unit can measure the temperature and humidity of the storage location using a sensor and perform analysis according to the environmental conditions. The analysis unit can also adjust the brightness and contrast of the image by taking into account the lighting conditions of the storage location. Furthermore, the analysis unit can measure the noise level of the storage location and correct the analysis results if noise has an effect. In this way, by taking into account the environmental conditions of the storage location, more accurate analysis is possible.

[0058] The categorization unit can determine categorization priorities based on the frequency of use of stored items. For example, frequently used items are given priority in categorization. The categorization unit collects data on the frequency of use of stored items and categorizes them according to frequency. In addition, less frequently used items can be postponed. Furthermore, the categorization unit can also make suggestions for optimizing the placement of stored items based on frequency of use. In this way, efficient storage is possible by determining categorization priorities based on the frequency of use of stored items.

[0059] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion algorithm is optimized based on the user's past suggestion results. The suggestion unit refers to the user's past suggestion data and feedback. The suggestion unit can also improve the accuracy of suggestions by referring to the user's past suggestion results. The suggestion unit analyzes the user's past suggestion results and improves the accuracy of suggestions. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results.

[0060] The proposal unit can determine the priority of proposals taking into account the market value of the stored items. For example, it can give priority to proposing items with high market value. The proposal unit evaluates the market value based on the price and demand of the stored items. It can also postpone storage items with low market value. Furthermore, the proposal unit can make proposals to optimize the placement of stored items based on market value. This makes it possible to make more appropriate proposals by taking into account the market value of the stored items.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives images taken by the user using a photographing device. For example, it can accept images taken with a smartphone or digital camera, and supports common image formats such as JPEG, PNG, and TIFF. It also performs appropriate processing depending on the resolution and size of the image. Step 2: The analysis unit uses deep learning-based object recognition technology to analyze the image received by the reception unit and measure the dimensions of the storage space. For example, it can accurately measure the height, width, and depth of the inside of a closet or shelves, as well as the dimensions of objects in the image. Step 3: The categorization unit categorizes the stored items based on the images analyzed by the analysis unit. For example, they can be classified into different categories such as clothing, books, kitchenware, etc., and AI is used to automatically identify objects in the image and classify them into the appropriate category. The accuracy of categorization is also improved based on user input information and past data. Step 4: The proposal unit suggests the optimal storage method based on the results of categorization by the categorization unit. For example, it suggests specific storage methods, such as whether clothes should be hung on hangers or stored in drawers, or which shelf books should be placed on. Furthermore, it uses AI to suggest the optimal storage method based on the user's needs and the characteristics of the storage space, and improves the accuracy of the suggestions based on user feedback.

[0063] (Example 2) A storage assistance system according to an embodiment of the present invention measures the dimensions of a storage space, categorizes items to be stored, and suggests optimal storage methods based on images taken by a user. The storage assistance system uses a camera to capture images of the space or items the user wants to organize and store. The system then automatically measures the dimensions of the captured image using object recognition technology based on deep learning. For example, it accurately measures the height, width, and depth of the interior of a closet or shelves. Next, the storage assistance system analyzes the captured image and automatically categorizes the stored items. For example, it classifies items into different categories, such as clothing, books, and kitchenware. Finally, the storage assistance system suggests optimal storage methods based on the measurement and categorization results. For example, it suggests specific storage methods, such as whether clothing should be hung on hangers or stored in drawers, and which shelf books should be placed on. This allows the user to efficiently utilize storage space. This allows even those who are not good at storage to easily organize and maximize storage space.

[0064] A storage assistance system according to an embodiment includes a reception unit, an analysis unit, a categorization unit, and a suggestion unit. The reception unit receives images captured by a user using a camera. For example, the reception unit can receive images captured using a smartphone or digital camera. The reception unit can also receive common image formats, such as JPEG, PNG, and TIFF, regardless of the image format. The reception unit can also perform appropriate processing depending on the image resolution and size. The analysis unit uses object recognition technology based on deep learning to analyze the images received by the reception unit and measure the dimensions of the storage space. For example, the analysis unit can accurately measure the height, width, and depth of the interior of a closet or shelves. The analysis unit can also recognize objects in the images and measure their dimensions. Furthermore, the analysis unit can analyze the position and shape of objects in the images using a deep learning model. The categorization unit categorizes stored items based on the images analyzed by the analysis unit. For example, the categorization unit can classify items into different categories, such as clothing, books, and kitchenware. The categorization unit can also use AI to automatically identify objects in images and classify them into appropriate categories. Furthermore, the categorization unit can improve the accuracy of categorization based on user input information and past data. The suggestion unit suggests optimal storage methods based on the categorization results by the categorization unit. For example, the suggestion unit suggests specific storage methods, such as whether clothes should be hung on hangers or stored in drawers, or which shelf books should be placed on. The suggestion unit can also use AI to suggest optimal storage methods based on user needs and storage space characteristics. Furthermore, the suggestion unit can improve the accuracy of suggestions based on user feedback. As a result, the storage assistance system according to the embodiment can measure the dimensions of storage locations, categorize storage items, and suggest optimal storage methods based on images taken by the user.

[0065] The reception unit estimates the user's emotions and adjusts the timing of image reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit encourages the user to accept images at a time when the user can relax. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the reception unit can adjust the image reception speed to accommodate the user's pace. For example, the reception unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is enjoying themselves, the reception unit can accept images at the user's pace. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the image reception timing to be adjusted according to the user's emotions, thereby enabling the image reception at a more appropriate time. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0066] The reception unit can analyze the user's past image reception history and select an appropriate reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. For example, the reception unit can analyze the user's past image reception history and select the optimal reception method. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past reception history. For example, the reception unit can select the most efficient reception method based on the user's past reception history. This enables efficient image reception by selecting the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past image reception history data into the generation AI and have the generation AI select the optimal reception method.

[0067] When receiving images, the reception unit may filter the images based on the user's current project or area of ​​interest. For example, the reception unit may preferentially receive images related to a project currently underway by the user. For example, the reception unit may identify the current project or area of ​​interest based on the user's input information and past activity history. The reception unit may also filter and receive highly relevant images based on the user's area of ​​interest. For example, the reception unit may select and receive necessary images according to the progress of the user's project. In this way, by filtering images based on the user's project or area of ​​interest, highly relevant images can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's input information and past activity history data to a generation AI and cause the generation AI to identify the user's current project or area of ​​interest.

[0068] When accepting an image, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user uses voice input, the acceptance unit accepts the image using voice recognition technology. For example, the acceptance unit records the user's voice with a microphone and converts it into text data using voice recognition technology. Furthermore, when the user uses text input, the acceptance unit can also accept the image using text analysis technology. For example, the acceptance unit analyzes the user's text input and accepts an appropriate image. Furthermore, when the user uses image input, the acceptance unit can also accept the image using image recognition technology. For example, the acceptance unit analyzes an image taken by the user and performs appropriate processing. This enables efficient image acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can input the user's voice data or text data into a generation AI and have the generation AI select an appropriate acceptance means.

[0069] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit postpones images of lower importance. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can prioritize receiving images of higher importance. For example, the reception unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving images that require rapid processing. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thus, by determining the priority of images according to the user's emotions, important images can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0070] When accepting images, the reception unit can prioritize accepting highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting images related to that area. For example, the reception unit acquires the user's geographical location information using GPS data or a location information service. The reception unit can also filter and accept highly relevant images based on the user's current location. For example, the reception unit selects and accepts optimal images based on the user's geographical location information. This allows highly relevant images to be preferentially accepted by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant images.

[0071] When accepting images, the acceptance unit can analyze the user's social media activity and accept related images. The acceptance unit, for example, preferentially accepts images shared by the user on social media. For example, the acceptance unit analyzes the content of the user's social media posts and filters and accepts related images. The acceptance unit can also accept related images with reference to the activity of the user's friends on social media. For example, the acceptance unit selects and accepts optimal images based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially accept highly relevant images. Some or all of the above-described processing by the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's social media activity data into a generation AI and cause the generation AI to select related images.

[0072] When accepting an image, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. The acceptance unit, for example, proposes an optimal acceptance method based on feedback provided by the user in the past. For example, the acceptance unit analyzes the user's past feedback and customizes the acceptance method. The acceptance unit can also improve the acceptance method by reflecting the user's feedback. For example, the acceptance unit selects an optimal acceptance method based on the user's past feedback. In this way, the optimal acceptance method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acceptance method.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit records the user's voice and estimates the emotions using voice analysis technology. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotions using an emotion estimation algorithm. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the storage location. For example, the analysis unit performs a detailed analysis on storage locations with high importance. For example, the analysis unit evaluates the importance based on the frequency of use and storage capacity of the storage location. The analysis unit can also perform a simplified analysis on storage locations with low importance. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the storage location. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the storage location. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input storage location importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the storage location. For example, in the case of a closet, the analysis unit applies an analysis algorithm specialized for clothing. For example, the analysis unit analyzes the types and arrangement of clothing in the closet. In addition, in the case of a kitchen, the analysis unit can also apply an analysis algorithm specialized for kitchen utensils. For example, the analysis unit analyzes the arrangement of cooking utensils and tableware in the kitchen. In addition, in the case of a bookshelf, the analysis unit can also apply an analysis algorithm specialized for books. For example, the analysis unit analyzes the types and arrangement of books. In this way, by applying an analysis algorithm depending on the category of the storage location, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input storage location category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit refers to the user's past analysis data and result evaluations. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and improves the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0078] During analysis, the analysis unit can determine the analysis priority based on the time when the storage location was photographed. For example, the analysis unit prioritizes analysis of storage locations that were photographed most recently. For example, the analysis unit obtains the time when the storage location was photographed using a timestamp or calendar data. The analysis unit can also postpone storage locations that were photographed earlier. For example, the analysis unit determines the analysis priority based on the time when the storage location was photographed. This enables efficient analysis by determining the analysis priority based on the time when the storage location was photographed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data on the time when the storage location was photographed into the generation AI and have the generation AI determine the analysis priority.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of storage locations. The analysis unit, for example, prioritizes analysis of highly relevant storage locations. For example, the analysis unit evaluates the relevance based on the physical proximity of the storage locations or the similarity of their intended uses. The analysis unit can also postpone storage locations with low relevance. For example, the analysis unit adjusts the order of analysis based on the relevance of storage locations. This enables efficient analysis by adjusting the order of analysis based on the relevance of storage locations. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input storage location relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0080] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit evaluates the user's level of expertise based on the user's qualifications and past experience. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simpler terms. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0081] The categorization unit can estimate the user's emotions and adjust the categorization criteria based on the estimated user emotions. For example, if the user is stressed, the categorization unit uses simple categorization criteria. For example, the categorization unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Alternatively, if the user is relaxed, the categorization unit can use more detailed categorization criteria. For example, the categorization unit records the user's voice and estimates the emotion using voice analysis technology. Alternatively, if the user is in a hurry, the categorization unit can use criteria that allow for quick categorization. For example, the categorization unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for more appropriate categorization by adjusting the categorization criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the categorization unit may be performed using AI, or may be performed without using AI. For example, the categorization unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The categorization unit can improve the accuracy of categorization by taking into account the interrelationships between stored items during categorization. The categorization unit, for example, analyzes the interrelationships between stored items and classifies highly related items into the same category. For example, the categorization unit evaluates the interrelationships based on the frequency of use and relevance of the stored items. The categorization unit can also improve the accuracy of categorization by taking into account the interrelationships between stored items. For example, the categorization unit proposes an optimal categorization method based on the interrelationships between stored items. In this way, the accuracy of categorization can be improved by taking into account the interrelationships between stored items. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input interrelationship data between stored items into a generation AI and cause the generation AI to improve the accuracy of categorization.

[0083] The categorization unit can take into account attribute information of the person who submitted the stored items when categorizing. The categorization unit, for example, proposes an optimal categorization method based on the attribute information of the person who submitted the stored items. For example, the categorization unit adjusts the categorization criteria based on attribute information such as the submitter's age and occupation. The categorization unit can also improve the accuracy of categorization by taking into account the attribute information of the person who submitted the stored items. For example, the categorization unit analyzes the attribute information of the submitter and selects the optimal categorization method. This enables more appropriate categorization by taking into account the attribute information of the person who submitted the stored items. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input attribute information data of the submitter to a generation AI and have the generation AI adjust the categorization criteria.

[0084] The categorization unit can weight the categorization based on the submission frequency of the stored items when categorizing. For example, the categorization unit prioritizes categorization of stored items with a high submission frequency. For example, the categorization unit weights the stored items based on the submission frequency data. The categorization unit can also postpone categorization of stored items with a low submission frequency. For example, the categorization unit weights the categorization based on the submission frequency. This enables efficient categorization by weighting the categorization based on the submission frequency of the stored items. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input the submission frequency data to a generation AI and cause the generation AI to perform categorization weighting.

[0085] The categorization unit can estimate the user's emotions and adjust the order in which the categorization results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the categorization unit postpones displaying less important categories. For example, the categorization unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. The categorization unit can also prioritize displaying more important categories when the user is relaxed. For example, the categorization unit records the user's voice and estimates their emotions using voice analysis technology. The categorization unit can also prioritize displaying categories that require quick confirmation when the user is in a hurry. For example, the categorization unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates their emotions using an emotion estimation algorithm. This allows for more appropriate display by adjusting the order in which the categorization results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the categorization unit may be performed using AI, or may be performed without using AI. For example, the categorization unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0086] The categorization unit can take the geographic distribution of stored items into consideration when categorizing. The categorization unit, for example, proposes an optimal categorization method based on the geographic distribution of stored items. For example, the categorization unit evaluates the geographic distribution of stored items based on distribution by region and location information. The categorization unit can also improve the accuracy of categorization by taking the geographic distribution of stored items into consideration. For example, the categorization unit analyzes the geographic distribution of stored items and selects an optimal categorization method. This enables more appropriate categorization by taking the geographic distribution of stored items into consideration. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input geographic distribution data of stored items to a generation AI and cause the generation AI to improve the accuracy of categorization.

[0087] The categorization unit can improve the accuracy of categorization by referring to literature related to the stored items during categorization. The categorization unit, for example, refers to literature related to the stored items and proposes an optimal categorization method. For example, the categorization unit categorizes the stored items based on related literature such as academic papers and technical reports. The categorization unit can also improve the accuracy of categorization based on the literature related to the stored items. For example, the categorization unit analyzes literature related to the stored items and selects an optimal categorization method. In this way, by referring to the literature related to the stored items, the accuracy of categorization can be improved. Some or all of the above-mentioned processing in the categorization unit may be performed using, or without, AI, for example. For example, the categorization unit can input related literature data into a generation AI and cause the generation AI to improve the accuracy of categorization.

[0088] The categorization unit can take into account the market value of the stored items when categorizing. For example, the categorization unit prioritizes categorization of stored items with high market value. For example, the categorization unit evaluates the market value based on the price and demand of the stored items. The categorization unit can also postpone categorization of stored items with low market value. For example, the categorization unit weights the categorization based on the market value. This allows for more appropriate categorization by taking the market value of the stored items into consideration. Some or all of the above-mentioned processing in the categorization unit may be performed using, for example, AI, or may be performed without using AI. For example, the categorization unit can input market value data of the stored items into the generation AI and have the generation AI perform the categorization weighting.

[0089] The suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit provides simple, highly visible suggestions. For example, the suggestion unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, the suggestion unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the key points. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This enables more appropriate suggestions to be provided by adjusting the display method of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0090] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the stored item. For example, the proposal unit makes a detailed proposal for a stored item with a high level of importance. For example, the proposal unit evaluates the importance of the stored item based on the frequency of use and value of the stored item. The proposal unit can also make a simplified proposal for a stored item with a low level of importance. For example, the proposal unit adjusts the level of detail of the proposal according to the importance of the stored item. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the stored item. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input importance data of the stored items to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0091] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the stored item. For example, in the case of clothing, the suggestion unit suggests using hangers or drawers. For example, the suggestion unit makes the optimal suggestion based on the type of clothing and the storage method. In addition, in the case of books, the suggestion unit can also suggest shelf arrangement and storage method. For example, the suggestion unit makes the optimal suggestion based on the type and arrangement of books. In addition, in the case of kitchenware, the suggestion unit can also suggest storage locations and organization methods. For example, the suggestion unit makes the optimal suggestion based on the type and frequency of use of kitchenware. In this way, by applying a suggestion algorithm depending on the category of the stored item, more accurate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of the stored items to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0092] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, optimizes the proposal algorithm based on the user's past proposal results. For example, the suggestion unit refers to the user's past proposal data and feedback. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results and improves the accuracy of the proposal. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0093] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit postpones suggestions of lower importance. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit can prioritize suggestions of higher importance. For example, the suggestion unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions that require quick processing. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This enables more appropriate suggestions to be prioritized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0094] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the storage item. For example, the proposal unit prioritizes proposals for recently submitted storage items. For example, the proposal unit obtains the submission date of the storage item using a timestamp or calendar data. The proposal unit can also postpone storage items that were submitted earlier. For example, the proposal unit determines the priority of the proposal based on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the storage item. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission date of the storage item to the generation AI and have the generation AI determine the priority of the proposals.

[0095] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the stored items. For example, the suggestion unit prioritizes suggestions for highly relevant stored items. For example, the suggestion unit evaluates the relevance based on the purpose of use of the stored items and physical proximity. The suggestion unit can also postpone suggestions for less relevant stored items. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the stored items. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the stored items. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the stored items to the generation AI and cause the generation AI to adjust the order of suggestions.

[0096] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, the suggestion unit evaluates the user's level of expertise based on the user's qualifications and past experience. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal in simpler terms. For example, the suggestion unit adjusts the use of technical terminology according to the user's level of expertise. This enables more appropriate proposals by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, categorization unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can detect the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and estimate the user's emotions using the control unit 46A. The analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and analyzes images and measures dimensions using deep learning. The categorization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and categorizes stored items using AI. The suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and suggests an optimal storage method. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, categorization unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can detect the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and estimate the user's emotions using the control unit 46A. The analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and analyzes images and measures dimensions using deep learning. The categorization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and categorizes stored items using AI. The suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and suggests an optimal storage method. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, categorization unit, and suggestion unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can detect the user's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314 and estimate the user's emotions using the control unit 46A. The analysis unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and analyzes images and measures dimensions using deep learning. The categorization unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and categorizes stored items using AI. The suggestion unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and suggests an optimal storage method. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, categorization unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can detect the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and estimate the user's emotions using the control unit 46A. The analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and analyzes images and measures dimensions using deep learning. The categorization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and categorizes stored items using AI. The suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and suggests an optimal storage method.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the user's past behavioral patterns and predict the optimal timing for accepting images. For example, the reception unit can predict the next timing for accepting images based on the time period and frequency at which the user took images in the past. The reception unit can also refer to the user's calendar information and schedule to suggest the optimal timing. Furthermore, the reception unit can learn the user's daily rhythm and automatically adjust the optimal acceptance timing. This allows for more efficient image acceptance by optimizing the timing for accepting images based on the user's behavioral patterns.

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling stressed, analyses of lower importance can be postponed. The analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, it can prioritize analyses of higher importance. The analysis unit records the user's voice and estimates their emotions using voice analysis technology. Furthermore, if the user is in a hurry, it can prioritize analyses that require rapid processing. The analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates their emotions using an emotion estimation algorithm. This allows for analysis prioritization based on the user's emotions, thereby providing more appropriate analysis results.

[0100] The categorization unit can estimate the user's emotions and adjust the categorization criteria based on the estimated user emotions. For example, if the user is feeling stressed, simple categorization criteria are used. The categorization unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. Alternatively, if the user is relaxed, more detailed categorization criteria can be used. The categorization unit records the user's voice and estimates their emotions using voice analysis technology. Furthermore, if the user is in a hurry, criteria that can quickly categorize the user can be used. The categorization unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates their emotions using an emotion estimation algorithm. This allows the categorization criteria to be adjusted according to the user's emotions, enabling more appropriate categorization.

[0101] The suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible suggestions. The suggestion unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. It can also provide detailed suggestions if the user is relaxed. The suggestion unit records the user's voice and estimates their emotions using voice analysis technology. It can also provide suggestions that focus on the main points if the user is in a hurry. The suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with sensors and estimates their emotions using an emotion estimation algorithm. This enables more appropriate suggestions to be provided by adjusting the display method of suggestions according to the user's emotions.

[0102] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, suggestions with lower importance are postponed. The suggestion unit captures the user's facial expression with a camera and estimates their emotions using an emotion estimation algorithm. Also, if the user is relaxed, it can prioritize suggestions with higher importance. The suggestion unit records the user's voice and estimates their emotions using voice analysis technology. Furthermore, if the user is in a hurry, it can prioritize suggestions that require quick processing. The suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates their emotions using an emotion estimation algorithm. This enables more appropriate suggestions to be made by prioritizing suggestions according to the user's emotions.

[0103] The reception unit can provide an image reception method that corresponds to the characteristics of each region, taking into account the user's geographical location information. For example, if the user is in an urban area, the reception unit can suggest an image reception method that corresponds to the storage needs unique to urban areas. Also, if the user is in a rural area, the reception unit can suggest an image reception method that corresponds to the storage needs unique to rural areas. Furthermore, the reception unit can provide an image reception method that corresponds to the culture and customs of each region, based on the user's geographical location information. In this way, a more appropriate image reception method can be provided by taking into account the user's geographical location information.

[0104] The analysis unit can improve the accuracy of the analysis by taking into account the environmental conditions of the storage location. For example, the analysis unit can measure the temperature and humidity of the storage location using a sensor and perform analysis according to the environmental conditions. The analysis unit can also adjust the brightness and contrast of the image by taking into account the lighting conditions of the storage location. Furthermore, the analysis unit can measure the noise level of the storage location and correct the analysis results if noise has an effect. In this way, by taking into account the environmental conditions of the storage location, more accurate analysis is possible.

[0105] The categorization unit can determine categorization priorities based on the frequency of use of stored items. For example, frequently used items are given priority in categorization. The categorization unit collects data on the frequency of use of stored items and categorizes them according to frequency. In addition, less frequently used items can be postponed. Furthermore, the categorization unit can also make suggestions for optimizing the placement of stored items based on frequency of use. In this way, efficient storage is possible by determining categorization priorities based on the frequency of use of stored items.

[0106] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion algorithm is optimized based on the user's past suggestion results. The suggestion unit refers to the user's past suggestion data and feedback. The suggestion unit can also improve the accuracy of suggestions by referring to the user's past suggestion results. The suggestion unit analyzes the user's past suggestion results and improves the accuracy of suggestions. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results.

[0107] The proposal unit can determine the priority of proposals taking into account the market value of the stored items. For example, it can give priority to proposing items with high market value. The proposal unit evaluates the market value based on the price and demand of the stored items. It can also postpone storage items with low market value. Furthermore, the proposal unit can make proposals to optimize the placement of stored items based on market value. This makes it possible to make more appropriate proposals by taking into account the market value of the stored items.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit receives images taken by the user using a photographing device. For example, it can accept images taken with a smartphone or digital camera, and supports common image formats such as JPEG, PNG, and TIFF. It also performs appropriate processing depending on the resolution and size of the image. Step 2: The analysis unit uses deep learning-based object recognition technology to analyze the image received by the reception unit and measure the dimensions of the storage space. For example, it can accurately measure the height, width, and depth of the inside of a closet or shelves, as well as the dimensions of objects in the image. Step 3: The categorization unit categorizes the stored items based on the images analyzed by the analysis unit. For example, they can be classified into different categories such as clothing, books, kitchenware, etc., and AI is used to automatically identify objects in the image and classify them into the appropriate category. The accuracy of categorization is also improved based on user input information and past data. Step 4: The proposal unit suggests the optimal storage method based on the results of categorization by the categorization unit. For example, it suggests specific storage methods, such as whether clothes should be hung on hangers or stored in drawers, or which shelf books should be placed on. Furthermore, it uses AI to suggest the optimal storage method based on the user's needs and the characteristics of the storage space, and improves the accuracy of the suggestions based on user feedback.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] 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.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an image captured by the imaging device; an analysis unit that analyzes the image received by the reception unit and measures the dimensions of the storage location; a categorization unit that categorizes the stored items based on the image analyzed by the analysis unit; a suggestion unit that suggests an appropriate storage method based on the results of categorization by the categorization unit. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of image acceptance based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past image submission history and select the appropriate submission method.

2. The system of claim 1.

4. The reception unit As images are accepted, they are filtered based on the user's current project or area of ​​interest.

2. The system of claim 1.

5. The reception unit When accepting an image, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the images to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting images, the system prioritizes accepting images that are highly relevant based on the user's geographical location.

2. The system of claim 1.

8. The reception unit When accepting images, analyze the user's social media activity and accept related images.

2. The system of claim 1.

Images