System

Abstract

An object of the system according to the embodiment is to promote reuse and recycling of unnecessary items.SOLUTION: A system according to an embodiment includes a registration unit, a search unit, a matching unit, and a donation promotion unit. The registration unit receives registration of an unnecessary item. The search unit searches for a business operator or an organization in the area based on the information registered by the registration unit. The matching unit matches the registered item with the business operator or the organization retrieved by the retrieval unit. The donation promotion unit promotes donation to a group or a project that requires a specific item.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently reuse or recycle unwanted items, leaving room for improvement in waste reduction.

[0005] The system according to the embodiment aims to promote the reuse and recycling of unwanted items. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a search unit, a matching unit, and a donation promotion unit. The registration unit accepts registration of unwanted items. The search unit searches for businesses and organizations within the area based on information registered by the registration unit. The matching unit matches the businesses and organizations searched by the search unit with the registered items. The donation promotion unit promotes donations to organizations and projects that need specific items. [Effects of the Invention]

[0007] Embodiments of the system can encourage reuse and recycling of unwanted items. [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) An app service according to an embodiment of the present invention is a system that allows companies to provide unwanted office furniture and electrical appliances to other businesses, thereby promoting reuse and recycling. This system allows users to register unwanted items, search for and match with businesses and organizations in the area that need them, and promote donations to organizations or projects that require specific items. This allows the app service to reduce waste and promote reuse and recycling. For example, companies can donate unwanted office furniture to local educational institutions or public interest organizations instead of discarding it, thereby promoting the effective use of resources. Furthermore, promoting donations and cooperation through the app also leads to support for the local community.

[0029] The app service according to the embodiment includes a registration unit, a search unit, a matching unit, and a donation promotion unit. The registration unit allows a user to register unwanted items. For example, the registration unit lists office furniture and electrical appliances, such as desks, chairs, and computers. The search unit searches for businesses and organizations in the area based on the information registered by the registration unit. For example, the search unit targets educational institutions, public interest organizations, and startup companies. The matching unit matches the businesses and organizations searched by the search unit with the registered items. For example, the matching unit compares the registered items with needed items and proposes optimal matches. The donation promotion unit promotes donations to organizations or projects that need specific items. For example, if an educational institution needs desks and chairs to set up a new classroom, the application provides that information and calls for donations. This allows the app service according to the embodiment to efficiently register, search, match, and promote donations of unwanted items.

[0030] The application service includes a display unit that displays detailed information about an item registered by a user. The display unit displays detailed information about the item registered by the user. For example, the display unit displays the item's status, usage history, provider information, and the like. This allows the user to check detailed information about the item registered.

[0031] The application service includes a notification unit that notifies the user of the matching result. The notification unit notifies the user of the matching result. For example, the notification is sent by email, push notification, SMS, or other methods. This allows the user to quickly check the matching result.

[0032] The registration unit allows a user to list office furniture and electrical appliances, including but not limited to desks, chairs, computers, printers, etc. This allows a user to easily list unwanted items.

[0033] The search unit can search for educational institutions, public interest organizations, and startup companies. Examples of educational institutions, public interest organizations, and startup companies include, but are not limited to, elementary schools, junior high schools, high schools, universities, and non-profit organizations. This allows users to efficiently search for necessary businesses and organizations in the area.

[0034] The matching unit can match registered items with required items and propose matching. Matching methods include, but are not limited to, keyword matching and attribute matching. This allows for efficient reuse by proposing optimal matching.

[0035] The donation promotion department can call for donations to organizations or projects that need specific items. Methods for calling for donations include, but are not limited to, email campaigns, the use of social media, etc. This can promote donations to organizations or projects that need specific items.

[0036] The registration unit can analyze the user's past registration history and select a registration method. For example, the registration unit can automatically display as candidates the types of items that the user has frequently registered in the past. It can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the registration method to be used in a specific time period based on the user's past registration history. This makes it possible to suggest the optimal registration method based on the user's past registration history.

[0037] The registration unit can filter items based on the user's current project or field of interest at the time of registration. For example, the registration unit preferentially registers items related to the user's current project. The registration unit can also automatically filter and display related items based on the user's field of interest. The registration unit can also suggest an optimal registration method based on fields in which the user has shown interest in the past. This allows the optimal items to be registered based on the user's current project or field of interest.

[0038] The registration unit can select a registration means depending on the user's input method during registration. For example, when a user registers an item by voice, the registration unit automatically converts the voice into text using voice recognition technology. When a user registers an item by image, the registration unit can automatically extract detailed information about the item using image recognition technology. When a user registers an item by text, an input completion function can be provided to enable quick registration. This makes it possible to provide the optimal registration means depending on the user's input method.

[0039] The registration unit can prioritize registering items based on the user's geographical location information at the time of registration. For example, the registration unit prioritizes registering items that can be used in locations close to the user's current location. The registration unit can also automatically filter and display related items based on the user's geographical location information. The registration unit can also suggest an optimal registration method by referring to the user's past location information. This allows the optimal items to be registered based on the user's geographical location information.

[0040] The registration unit can analyze the user's social media activity and register items at the time of registration. For example, the registration unit can prioritize registering items related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's social media posts and automatically filter and display related items. It can also suggest related items based on the activity of the user's friends on social media. This allows the most suitable items to be registered based on the user's social media activity.

[0041] The registration unit can customize the registration method by reflecting the user's past feedback at the time of registration. For example, the registration unit can suggest an optimal registration method based on the user's past feedback. The registration unit can also suggest a method to simplify the registration procedure based on the user's past feedback. The registration unit can also provide customizable registration options by referring to the user's past feedback. This makes it possible to provide an optimal registration method based on the user's past feedback.

[0042] The search unit can improve search accuracy by taking into account the interrelationships between items during a search. For example, the search unit analyzes the interrelationships between registered items and preferentially displays related items. The search unit can also propose optimal search results based on the interrelationships between items. The accuracy of search results can also be improved by taking into account the interrelationships between items. This makes it possible to improve search accuracy by taking into account the interrelationships between items. For example, techniques such as co-occurrence networks and relevance scores are used to analyze the interrelationships.

[0043] When searching, the search unit can perform a search based on the attribute information of the item provider. For example, the search unit can preferentially display related items based on the attribute information of the item provider. The search unit can also propose optimal search results taking into account the attribute information of the provider. The accuracy of the search results can also be improved based on the attribute information of the provider. This makes it possible to provide optimal search results based on the attribute information of the item provider. Attribute information includes, for example, age, gender, occupation, etc.

[0044] During a search, the search unit can determine search priorities based on the frequency of item provision. For example, the search unit preferentially displays items with a high frequency of provision. The search unit can also suggest optimal search results based on the frequency of provision. The accuracy of search results can also be improved by taking the frequency of provision into consideration. This makes it possible to provide optimal search results based on the frequency of item provision. The frequency of provision is calculated using, for example, the number of times items have been provided in the past, the interval between provides, etc.

[0045] The search unit can perform a search while taking into account the geographical distribution of items. For example, the search unit can preferentially display items that are available in locations close to the user's current location. The search unit can also suggest optimal search results based on the geographical distribution of items. The accuracy of search results can also be improved by taking into account the geographical distribution of items. This makes it possible to provide optimal search results based on the geographical distribution of items. For example, the number of items per region and distribution bias are used to analyze the geographical distribution.

[0046] The search unit can improve the accuracy of the search by referring to literature related to the item during the search. For example, the search unit proposes optimal search results based on literature related to the item. The search unit can also improve the accuracy of the search results by referring to related literature. Furthermore, it can also preferentially display related items based on literature related to the item. This makes it possible to provide optimal search results based on literature related to the item. Related literature includes, for example, academic papers, patent documents, technical reports, etc.

[0047] The search unit can perform a search based on the market value of an item. For example, the search unit preferentially displays items with high market value. The search unit can also suggest optimal search results based on market value. The accuracy of search results can also be improved by taking market value into consideration. This makes it possible to provide optimal search results based on the market value of an item. For example, the current market price, the balance between supply and demand, etc. are used to evaluate market value.

[0048] The matching unit can improve the accuracy of matching by taking into account the interrelationships between items during matching. For example, the matching unit analyzes the interrelationships between registered items and preferentially displays related items. The matching unit can also propose optimal matching results based on the interrelationships between items. The accuracy of the matching results can also be improved by taking into account the interrelationships between items. This makes it possible to provide optimal matching results based on the interrelationships between items. For example, techniques such as co-occurrence networks and relevance scores are used to analyze the interrelationships.

[0049] The matching unit can perform matching based on attribute information of the item provider when matching. For example, the matching unit can preferentially display related items based on the attribute information of the item provider. The matching unit can also propose optimal matching results taking into account the attribute information of the provider. The accuracy of the matching results can also be improved based on the attribute information of the provider. This makes it possible to provide optimal matching results based on the attribute information of the item provider. The attribute information includes, for example, age, gender, occupation, etc.

[0050] The matching unit can determine the priority of matching based on the frequency of item provision during matching. For example, the matching unit preferentially displays items with a high frequency of provision. The matching unit can also propose optimal matching results based on the frequency of provision. The accuracy of the matching results can also be improved by taking the frequency of provision into consideration. This makes it possible to provide optimal matching results based on the frequency of item provision. The frequency of provision is calculated using, for example, the number of times an item has been provided in the past, the interval between provides, etc.

[0051] The matching unit can perform matching while taking into account the geographical distribution of items. For example, the matching unit preferentially displays items that are available in locations close to the user's current location. The matching unit can also propose optimal matching results based on the geographical distribution of items. The accuracy of the matching results can also be improved by taking into account the geographical distribution of items. This makes it possible to provide optimal matching results based on the geographical distribution of items. For example, the number of items per region, distribution bias, etc. are used to analyze the geographical distribution.

[0052] The matching unit can improve the accuracy of matching by referring to related literature of the item during matching. The matching unit can, for example, propose an optimal matching result based on related literature of the item. The matching unit can also improve the accuracy of the matching result by referring to related literature. The matching unit can also preferentially display related items based on related literature of the item. This makes it possible to provide an optimal matching result based on related literature of the item. Related literature includes, for example, academic papers, patent documents, technical reports, etc.

[0053] The matching unit can perform matching based on the market value of the items when matching. For example, the matching unit preferentially displays items with high market value. The matching unit can also propose optimal matching results based on market value. The accuracy of the matching results can also be improved by taking market value into consideration. This makes it possible to provide optimal matching results based on the market value of the items. For example, the current market price, the balance between supply and demand, etc. are used to evaluate market value.

[0054] When promoting donations, the donation promotion unit can determine the priority of donations based on the importance of the items. For example, the donation promotion unit prioritizes items with high importance as donation targets. The donation priority can also be determined based on the importance of the items. Items with high importance can also be highlighted and displayed to encourage donations. This makes it possible to determine the optimal priority of donations based on the importance of the items. For example, the level of demand, the magnitude of impact, etc. are used to evaluate the importance.

[0055] When promoting donations, the donation promotion unit can apply an appropriate donation promotion method depending on the category of the item. For example, in the case of office furniture, the donation promotion unit can encourage donations by showing specific use examples. In the case of electrical appliances, the donation promotion unit can also explain the significance of donations from the perspective of environmental protection. In addition, the donation promotion unit can suggest the optimal donation promotion method depending on the specific category. In this way, the optimal donation promotion method can be provided depending on the category of the item. Category classifications include, for example, household goods, office supplies, electrical appliances, etc.

[0056] The donation promotion unit can improve the accuracy of donations based on the user's past donation history when promoting donations. The donation promotion unit can, for example, suggest the optimal donation method based on the user's past donation history. The donation promotion unit can also determine donation priorities based on the user's past donation history. The donation promotion unit can also provide customizable donation options based on the user's past donation history. This makes it possible to provide the optimal donation method based on the user's past donation history. The donation history includes, for example, the number of past donations, the type of donation, etc.

[0057] When promoting donations, the donation promotion unit can determine the priority of donations based on the time of submission of the items. For example, the donation promotion unit prioritizes items that have been recently submitted as donation targets. The donation promotion unit can also determine the priority of donations based on the time of submission. It can also highlight and display recently submitted items to encourage donations. This makes it possible to determine the optimal priority of donations based on the time of submission of the items. For example, the frequency of donations and the timing of donations are used to evaluate the time of donation.

[0058] The donation promotion unit can adjust the order of donations based on the relevance of items when promoting donations. For example, the donation promotion unit prioritizes highly related items as donation targets. The donation promotion unit can also determine the order of donations based on the relevance of items. It can also highlight highly related items and call for donations. This makes it possible to provide an optimal donation order based on the relevance of items. For example, a co-occurrence network, a relevance score, etc. are used to evaluate the relevance.

[0059] The donation promotion unit can adjust the use of donation terminology depending on the user's level of knowledge when promoting donations. For example, if the user has specialized knowledge, the donation promotion unit can encourage donations using detailed specialized terminology. If the user does not have specialized knowledge, the donation promotion unit can encourage donations using simple and easy-to-understand language. The donation promotion unit can also suggest an optimal donation message depending on the user's level of expertise. This makes it possible to provide an optimal donation message depending on the user's level of expertise. For example, the level of expertise can be evaluated using qualifications, years of experience, etc.

[0060] The display unit can adjust the level of detail of the display based on the importance of the item when displaying. For example, the display unit displays items with high importance in detail. The display unit can also adjust the level of detail of the display based on the importance of the item. It can also highlight and display items with high importance. This makes it possible to provide an optimal level of detail of the display based on the importance of the item. For example, the level of demand, the magnitude of impact, etc. are used to evaluate the importance.

[0061] The display unit can apply an appropriate display algorithm depending on the category of the item when displaying. For example, in the case of office furniture, the display unit can display specific usage examples. In the case of electrical appliances, the display unit can also explain the significance of the display from the perspective of environmental protection. The display unit can also suggest an optimal display algorithm depending on a specific category. This makes it possible to provide an optimal display algorithm depending on the category of the item. Category classifications include, for example, household goods, office supplies, electrical appliances, etc.

[0062] The display unit can improve the accuracy of the display based on the user's past display history when displaying. The display unit can, for example, suggest an optimal display method based on the user's past display history. The display unit can also determine display priorities based on the user's past display history. The display unit can also provide customizable display options by referring to the user's past display history. This makes it possible to provide an optimal display method based on the user's past display history. The display history includes, for example, the number of past displays, the type of display, etc.

[0063] The display unit can determine the display priority based on the time of providing the items when displaying them. For example, the display unit preferentially displays the most recently submitted items. The display unit can also determine the display priority based on the time of submission. It can also highlight and display the most recently submitted items. This makes it possible to provide the optimal display priority based on the time of submission of the items. For example, the frequency of provision, the timing of provision, etc. are used to evaluate the time of provision.

[0064] The display unit can adjust the display order based on the relevance of the items when displaying them. For example, the display unit preferentially displays highly relevant items. The display order can also be determined based on the relevance of the items. Highly relevant items can also be highlighted when displayed. This makes it possible to provide an optimal display order based on the relevance of the items. For example, a co-occurrence network, a relevance score, etc. are used to evaluate the relevance.

[0065] The display unit can adjust the use of technical terms in the display depending on the user's level of knowledge. For example, if the user has specialized knowledge, the display unit can display the information using detailed technical terms. If the user does not have specialized knowledge, the display unit can also display the information using simple, easy-to-understand words. The display unit can also suggest an optimal display message depending on the user's level of expertise. This makes it possible to provide an optimal display message depending on the user's level of expertise. For example, the level of expertise can be evaluated using qualifications, years of experience, etc.

[0066] The notification unit can adjust the level of detail of the notification based on the importance of the item when notifying. For example, the notification unit notifies items with high importance in detail. The notification unit can also adjust the level of detail of the notification based on the importance of the item. It can also notify items with high importance by emphasizing them. This makes it possible to provide the optimal level of detail of the notification based on the importance of the item. For example, the level of demand, the magnitude of the impact, etc. are used to evaluate the importance.

[0067] The notification unit can apply an appropriate notification algorithm depending on the category of the item when making a notification. For example, in the case of office furniture, the notification unit can provide a notification by showing a specific use example. In the case of electrical appliances, the notification unit can also explain the significance of the notification from the perspective of environmental protection. The notification unit can also suggest an optimal notification algorithm depending on a specific category. This makes it possible to provide an optimal notification algorithm depending on the category of the item. Category classifications include, for example, household goods, office supplies, electrical appliances, etc.

[0068] The notification unit can improve the accuracy of notifications based on the user's past notification history when notifying. The notification unit can, for example, suggest an optimal notification method based on the user's past notification history. The notification unit can also determine notification priorities based on the user's past notification history. The notification unit can also provide customizable notification options by referring to the user's past notification history. This makes it possible to provide an optimal notification method based on the user's past notification history. The notification history includes, for example, the number of past notifications, the type of notification, etc.

[0069] The notification unit can determine the priority of notifications based on the time of item provision at the time of notification. For example, the notification unit gives priority to notifying items that have recently been submitted. The notification unit can also determine the priority of notifications based on the time of submission. It can also highlight recently submitted items when notified. This makes it possible to provide optimal notification priority based on the time of submission of items. For example, the frequency of provision and the timing of provision are used to evaluate the time of provision.

[0070] The notification unit can adjust the order of notifications based on the relevance of items when notifying. For example, the notification unit prioritizes notifying highly relevant items. The notification unit can also determine the order of notifications based on the relevance of items. It can also highlight highly relevant items when notifying. This makes it possible to provide an optimal order of notifications based on the relevance of items. For example, a co-occurrence network, a relevance score, etc. are used to evaluate the relevance.

[0071] The notification unit can adjust the use of technical terms in notifications depending on the user's level of knowledge. For example, if the user has specialized knowledge, the notification unit can use detailed technical terms to notify the user. If the user does not have specialized knowledge, the notification unit can use simple, easy-to-understand language to notify the user. The notification unit can also suggest an optimal notification message depending on the user's level of expertise. This makes it possible to provide an optimal notification message depending on the user's level of expertise. For example, the level of expertise can be evaluated using qualifications, years of experience, etc.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The app service can analyze a user's past donation history and determine donation priorities. For example, it can automatically display the types of items that the user has frequently donated in the past as candidates. It can also prioritize suggestions for organizations and projects to which the user has donated in the past. Furthermore, it can predict and suggest methods for encouraging donations at specific times based on the user's past donation history. This can improve donation efficiency by suggesting the optimal donation method based on the user's past donation history.

[0074] The app service can determine the priority of donations based on the user's geographic location information. For example, items available near the user's current location can be given priority for donation. The app service can also automatically filter and display related items based on the user's geographic location information. Furthermore, the app service can suggest the optimal donation method based on the user's past location information. This can improve the efficiency of donations by providing the optimal donation method based on the user's geographic location information.

[0075] The app service can analyze the user's social media activity and adjust the method of soliciting donations. For example, it can prioritize donations of items related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and automatically filter and display related items. It can also suggest related items based on the activity of the user's friends on social media. This can improve donation efficiency by providing the most appropriate donation method based on the user's social media activity.

[0076] The app service can analyze the user's past notification history and determine notification priorities. For example, it can automatically display the types of items for which the user has frequently received notifications in the past as candidates. It can also suggest the optimal notification timing based on the time periods in which the user received notifications in the past. It can also prioritize specific notification methods (email, push notification, etc.) based on the user's past notification history. This can improve notification efficiency by suggesting the optimal notification method based on the user's past notification history.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The registration unit registers items that the user no longer needs, such as office furniture and electrical appliances such as desks, chairs, and computers. Step 2: The search unit searches for businesses and organizations in the area based on the information registered by the registration unit, such as educational institutions, public interest organizations, and start-up companies. Step 3: The matching unit matches the businesses and organizations found by the search unit with the registered items. For example, it compares the registered items with the required items and proposes the best match. Step 4: The Donation Promotion Department promotes donations to organizations or projects that need specific items. For example, if an educational institution needs desks and chairs to equip a new classroom, they can provide that information through the app and ask for donations.

[0079] (Example 2) An app service according to an embodiment of the present invention is a system that allows companies to provide unwanted office furniture and electrical appliances to other businesses, thereby promoting reuse and recycling. This system allows users to register unwanted items, search for and match with businesses and organizations in the area that need them, and promote donations to organizations or projects that require specific items. This allows the app service to reduce waste and promote reuse and recycling. For example, companies can donate unwanted office furniture to local educational institutions or public interest organizations instead of discarding it, thereby promoting the effective use of resources. Furthermore, promoting donations and cooperation through the app also leads to support for the local community.

[0080] The app service according to the embodiment includes a registration unit, a search unit, a matching unit, and a donation promotion unit. The registration unit allows a user to register unwanted items. For example, the registration unit lists office furniture and electrical appliances, such as desks, chairs, and computers. The search unit searches for businesses and organizations in the area based on the information registered by the registration unit. For example, the search unit targets educational institutions, public interest organizations, and startup companies. The matching unit matches the businesses and organizations searched by the search unit with the registered items. For example, the matching unit compares the registered items with needed items and proposes optimal matches. The donation promotion unit promotes donations to organizations or projects that need specific items. For example, if an educational institution needs desks and chairs to set up a new classroom, the application provides that information and calls for donations. This allows the app service according to the embodiment to efficiently register, search, match, and promote donations of unwanted items.

[0081] The application service includes a display unit that displays detailed information about an item registered by a user. The display unit displays detailed information about the item registered by the user. For example, the display unit displays the item's status, usage history, provider information, and the like. This allows the user to check detailed information about the item registered.

[0082] The application service includes a notification unit that notifies the user of the matching result. The notification unit notifies the user of the matching result. For example, the notification is sent by email, push notification, SMS, or other methods. This allows the user to quickly check the matching result.

[0083] The registration unit allows a user to list office furniture and electrical appliances, including but not limited to desks, chairs, computers, printers, etc. This allows a user to easily list unwanted items.

[0084] The search unit can search for educational institutions, public interest organizations, and startup companies. Examples of educational institutions, public interest organizations, and startup companies include, but are not limited to, elementary schools, junior high schools, high schools, universities, and non-profit organizations. This allows users to efficiently search for necessary businesses and organizations in the area.

[0085] The matching unit can match registered items with required items and propose matching. Matching methods include, but are not limited to, keyword matching and attribute matching. This allows for efficient reuse by proposing optimal matching.

[0086] The donation promotion department can call for donations to organizations or projects that need specific items. Methods for calling for donations include, but are not limited to, email campaigns, the use of social media, etc. This can promote donations to organizations or projects that need specific items.

[0087] The registration unit can estimate the user's emotions and adjust the timing of registration based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can simplify the registration procedure so that it can be completed in a short time. Also, if the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. Also, if the user is in a hurry, the registration unit can prioritize voice input so that items can be registered quickly. In this way, the burden on the user can be reduced by adjusting the timing of registration according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0088] The registration unit can analyze the user's past registration history and select a registration method. For example, the registration unit can automatically display as candidates the types of items that the user has frequently registered in the past. It can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the registration method to be used in a specific time period based on the user's past registration history. This makes it possible to suggest the optimal registration method based on the user's past registration history.

[0089] The registration unit can filter items based on the user's current project or field of interest at the time of registration. For example, the registration unit preferentially registers items related to the user's current project. The registration unit can also automatically filter and display related items based on the user's field of interest. The registration unit can also suggest an optimal registration method based on fields in which the user has shown interest in the past. This allows the optimal items to be registered based on the user's current project or field of interest.

[0090] The registration unit can select a registration means depending on the user's input method during registration. For example, when a user registers an item by voice, the registration unit automatically converts the voice into text using voice recognition technology. When a user registers an item by image, the registration unit can automatically extract detailed information about the item using image recognition technology. When a user registers an item by text, an input completion function can be provided to enable quick registration. This makes it possible to provide the optimal registration means depending on the user's input method.

[0091] The registration unit can estimate the user's emotions and determine the priority of items to be registered based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can prioritize registering items of high importance. Also, if the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. Also, if the user is in a hurry, the registration unit can prioritize displaying items that can be registered quickly. In this way, the priority of items to be registered can be determined according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0092] The registration unit can prioritize registering items based on the user's geographical location information at the time of registration. For example, the registration unit prioritizes registering items that can be used in locations close to the user's current location. The registration unit can also automatically filter and display related items based on the user's geographical location information. The registration unit can also suggest an optimal registration method by referring to the user's past location information. This allows the optimal items to be registered based on the user's geographical location information.

[0093] The registration unit can analyze the user's social media activity and register items at the time of registration. For example, the registration unit can prioritize registering items related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's social media posts and automatically filter and display related items. It can also suggest related items based on the activity of the user's friends on social media. This allows the most suitable items to be registered based on the user's social media activity.

[0094] The registration unit can customize the registration method by reflecting the user's past feedback at the time of registration. For example, the registration unit can suggest an optimal registration method based on the user's past feedback. The registration unit can also suggest a method to simplify the registration procedure based on the user's past feedback. The registration unit can also provide customizable registration options by referring to the user's past feedback. This makes it possible to provide an optimal registration method based on the user's past feedback.

[0095] The search unit can estimate a user's emotions and adjust search criteria based on the estimated user emotions. For example, if the user is feeling stressed, the search unit can display search results concisely and highlight important information. If the user is relaxed, the search unit can provide detailed search options and suggest customizable search methods. If the user is in a hurry, the search unit can quickly display search results and suggest the most suitable items. This allows the search criteria to be adjusted according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0096] The search unit can improve search accuracy by taking into account the interrelationships between items during a search. For example, the search unit analyzes the interrelationships between registered items and preferentially displays related items. The search unit can also propose optimal search results based on the interrelationships between items. The accuracy of search results can also be improved by taking into account the interrelationships between items. This makes it possible to improve search accuracy by taking into account the interrelationships between items. For example, techniques such as co-occurrence networks and relevance scores are used to analyze the interrelationships.

[0097] When searching, the search unit can perform a search based on the attribute information of the item provider. For example, the search unit can preferentially display related items based on the attribute information of the item provider. The search unit can also propose optimal search results taking into account the attribute information of the provider. The accuracy of the search results can also be improved based on the attribute information of the provider. This makes it possible to provide optimal search results based on the attribute information of the item provider. Attribute information includes, for example, age, gender, occupation, etc.

[0098] During a search, the search unit can determine search priorities based on the frequency of item provision. For example, the search unit preferentially displays items with a high frequency of provision. The search unit can also suggest optimal search results based on the frequency of provision. The accuracy of search results can also be improved by taking the frequency of provision into consideration. This makes it possible to provide optimal search results based on the frequency of item provision. The frequency of provision is calculated using, for example, the number of times items have been provided in the past, the interval between provides, etc.

[0099] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the search unit can display important information first. Also, if the user is relaxed, the search unit can display search results including detailed information. Also, if the user is in a hurry, the search unit can quickly display search results and suggest the most suitable items. In this way, the display order of search results can be adjusted according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0100] The search unit can perform a search while taking into account the geographical distribution of items. For example, the search unit can preferentially display items that are available in locations close to the user's current location. The search unit can also suggest optimal search results based on the geographical distribution of items. The accuracy of search results can also be improved by taking into account the geographical distribution of items. This makes it possible to provide optimal search results based on the geographical distribution of items. For example, the number of items per region and distribution bias are used to analyze the geographical distribution.

[0101] The search unit can improve the accuracy of the search by referring to literature related to the item during the search. For example, the search unit proposes optimal search results based on literature related to the item. The search unit can also improve the accuracy of the search results by referring to related literature. Furthermore, it can also preferentially display related items based on literature related to the item. This makes it possible to provide optimal search results based on literature related to the item. Related literature includes, for example, academic papers, patent documents, technical reports, etc.

[0102] The search unit can perform a search based on the market value of an item. For example, the search unit preferentially displays items with high market value. The search unit can also suggest optimal search results based on market value. The accuracy of search results can also be improved by taking market value into consideration. This makes it possible to provide optimal search results based on the market value of an item. For example, the current market price, the balance between supply and demand, etc. are used to evaluate market value.

[0103] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, if the user is feeling stressed, the matching unit can highlight and display important information. If the user is relaxed, the matching unit can provide detailed matching options and suggest a customizable matching method. If the user is in a hurry, the matching unit can quickly display matching results and suggest the most suitable items. This makes it possible to adjust the matching criteria according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0104] The matching unit can improve the accuracy of matching by taking into account the interrelationships between items during matching. For example, the matching unit analyzes the interrelationships between registered items and preferentially displays related items. The matching unit can also propose optimal matching results based on the interrelationships between items. The accuracy of the matching results can also be improved by taking into account the interrelationships between items. This makes it possible to provide optimal matching results based on the interrelationships between items. For example, techniques such as co-occurrence networks and relevance scores are used to analyze the interrelationships.

[0105] The matching unit can perform matching based on attribute information of the item provider when matching. For example, the matching unit can preferentially display related items based on the attribute information of the item provider. The matching unit can also propose optimal matching results taking into account the attribute information of the provider. The accuracy of the matching results can also be improved based on the attribute information of the provider. This makes it possible to provide optimal matching results based on the attribute information of the item provider. The attribute information includes, for example, age, gender, occupation, etc.

[0106] The matching unit can determine the priority of matching based on the frequency of item provision during matching. For example, the matching unit preferentially displays items with a high frequency of provision. The matching unit can also propose optimal matching results based on the frequency of provision. The accuracy of the matching results can also be improved by taking the frequency of provision into consideration. This makes it possible to provide optimal matching results based on the frequency of item provision. The frequency of provision is calculated using, for example, the number of times an item has been provided in the past, the interval between provides, etc.

[0107] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the matching unit can display important information first. Also, if the user is relaxed, the matching unit can display matching results including detailed information. Also, if the user is in a hurry, the matching unit can quickly display matching results and suggest the most suitable items. In this way, the display order of matching results can be adjusted according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0108] The matching unit can perform matching while taking into account the geographical distribution of items. For example, the matching unit preferentially displays items that are available in locations close to the user's current location. The matching unit can also propose optimal matching results based on the geographical distribution of items. The accuracy of the matching results can also be improved by taking into account the geographical distribution of items. This makes it possible to provide optimal matching results based on the geographical distribution of items. For example, the number of items per region, distribution bias, etc. are used to analyze the geographical distribution.

[0109] The matching unit can improve the accuracy of matching by referring to related literature of the item during matching. The matching unit can, for example, propose an optimal matching result based on related literature of the item. The matching unit can also improve the accuracy of the matching result by referring to related literature. The matching unit can also preferentially display related items based on related literature of the item. This makes it possible to provide an optimal matching result based on related literature of the item. Related literature includes, for example, academic papers, patent documents, technical reports, etc.

[0110] The matching unit can perform matching based on the market value of the items when matching. For example, the matching unit preferentially displays items with high market value. The matching unit can also propose optimal matching results based on market value. The accuracy of the matching results can also be improved by taking market value into consideration. This makes it possible to provide optimal matching results based on the market value of the items. For example, the current market price, the balance between supply and demand, etc. are used to evaluate market value.

[0111] The donation promotion unit can estimate the user's emotions and adjust the method of calling for donations based on the estimated user emotions. For example, if the user is emotional, the donation promotion unit can display an emotional message to encourage donations. If the user is relaxed, the donation promotion unit can provide detailed information and explain the significance of donations. If the user is in a hurry, the donation promotion unit can display a concise and to-the-point message to encourage donations. In this way, the method of calling for donations can be adjusted according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0112] When promoting donations, the donation promotion unit can determine the priority of donations based on the importance of the items. For example, the donation promotion unit prioritizes items with high importance as donation targets. The donation priority can also be determined based on the importance of the items. Items with high importance can also be highlighted and displayed to encourage donations. This makes it possible to determine the optimal priority of donations based on the importance of the items. For example, the level of demand, the magnitude of impact, etc. are used to evaluate the importance.

[0113] When promoting donations, the donation promotion unit can apply an appropriate donation promotion method depending on the category of the item. For example, in the case of office furniture, the donation promotion unit can encourage donations by showing specific use examples. In the case of electrical appliances, the donation promotion unit can also explain the significance of donations from the perspective of environmental protection. In addition, the donation promotion unit can suggest the optimal donation promotion method depending on the specific category. In this way, the optimal donation promotion method can be provided depending on the category of the item. Category classifications include, for example, household goods, office supplies, electrical appliances, etc.

[0114] The donation promotion unit can improve the accuracy of donations based on the user's past donation history when promoting donations. The donation promotion unit can, for example, suggest the optimal donation method based on the user's past donation history. The donation promotion unit can also determine donation priorities based on the user's past donation history. The donation promotion unit can also provide customizable donation options based on the user's past donation history. This makes it possible to provide the optimal donation method based on the user's past donation history. The donation history includes, for example, the number of past donations, the type of donation, etc.

[0115] The donation promotion unit can estimate the user's emotions and adjust the length of the donation based on the estimated user emotions. For example, if the user is moved, the donation promotion unit can display a longer, emotional message to encourage donations. If the user is relaxed, the donation promotion unit can display a longer message including detailed information. If the user is in a hurry, the donation promotion unit can display a short, concise, and to-the-point message to encourage donations. In this way, the length of the donation can be adjusted according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0116] When promoting donations, the donation promotion unit can determine the priority of donations based on the time of submission of the items. For example, the donation promotion unit prioritizes items that have been recently submitted as donation targets. The donation promotion unit can also determine the priority of donations based on the time of submission. It can also highlight and display recently submitted items to encourage donations. This makes it possible to determine the optimal priority of donations based on the time of submission of the items. For example, the frequency of donations and the timing of donations are used to evaluate the time of donation.

[0117] The donation promotion unit can adjust the order of donations based on the relevance of items when promoting donations. For example, the donation promotion unit prioritizes highly related items as donation targets. The donation promotion unit can also determine the order of donations based on the relevance of items. It can also highlight highly related items and call for donations. This makes it possible to provide an optimal donation order based on the relevance of items. For example, a co-occurrence network, a relevance score, etc. are used to evaluate the relevance.

[0118] The donation promotion unit can adjust the use of donation terminology depending on the user's level of knowledge when promoting donations. For example, if the user has specialized knowledge, the donation promotion unit can encourage donations using detailed specialized terminology. If the user does not have specialized knowledge, the donation promotion unit can encourage donations using simple and easy-to-understand language. The donation promotion unit can also suggest an optimal donation message depending on the user's level of expertise. This makes it possible to provide an optimal donation message depending on the user's level of expertise. For example, the level of expertise can be evaluated using qualifications, years of experience, etc.

[0119] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, if the user is feeling stressed, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can provide a display method including detailed information. If the user is in a hurry, the display unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0120] The display unit can adjust the level of detail of the display based on the importance of the item when displaying. For example, the display unit displays items with high importance in detail. The display unit can also adjust the level of detail of the display based on the importance of the item. It can also highlight and display items with high importance. This makes it possible to provide an optimal level of detail of the display based on the importance of the item. For example, the level of demand, the magnitude of impact, etc. are used to evaluate the importance.

[0121] The display unit can apply an appropriate display algorithm depending on the category of the item when displaying. For example, in the case of office furniture, the display unit can display specific usage examples. In the case of electrical appliances, the display unit can also explain the significance of the display from the perspective of environmental protection. The display unit can also suggest an optimal display algorithm depending on a specific category. This makes it possible to provide an optimal display algorithm depending on the category of the item. Category classifications include, for example, household goods, office supplies, electrical appliances, etc.

[0122] The display unit can improve the accuracy of the display based on the user's past display history when displaying. The display unit can, for example, suggest an optimal display method based on the user's past display history. The display unit can also determine display priorities based on the user's past display history. The display unit can also provide customizable display options by referring to the user's past display history. This makes it possible to provide an optimal display method based on the user's past display history. The display history includes, for example, the number of past displays, the type of display, etc.

[0123] The display unit can estimate the user's emotions and adjust the length of the display based on the estimated user emotions. For example, if the user is emotional, the display unit can display a longer message that appeals to the user's emotions. If the user is relaxed, the display unit can display a longer message that includes detailed information. If the user is in a hurry, the display unit can display a short message that is concise and to the point. This allows the length of the display to be adjusted according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0124] The display unit can determine the display priority based on the time of providing the items when displaying them. For example, the display unit preferentially displays the most recently submitted items. The display unit can also determine the display priority based on the time of submission. It can also highlight and display the most recently submitted items. This makes it possible to provide the optimal display priority based on the time of submission of the items. For example, the frequency of provision, the timing of provision, etc. are used to evaluate the time of provision.

[0125] The display unit can adjust the display order based on the relevance of the items when displaying them. For example, the display unit preferentially displays highly relevant items. The display order can also be determined based on the relevance of the items. Highly relevant items can also be highlighted when displayed. This makes it possible to provide an optimal display order based on the relevance of the items. For example, a co-occurrence network, a relevance score, etc. are used to evaluate the relevance.

[0126] The display unit can adjust the use of technical terms in the display depending on the user's level of knowledge. For example, if the user has specialized knowledge, the display unit can display the information using detailed technical terms. If the user does not have specialized knowledge, the display unit can also display the information using simple, easy-to-understand words. The display unit can also suggest an optimal display message depending on the user's level of expertise. This makes it possible to provide an optimal display message depending on the user's level of expertise. For example, the level of expertise can be evaluated using qualifications, years of experience, etc.

[0127] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can reduce notifications and notify only important information. Also, if the user is relaxed, the notification unit can provide notifications with detailed information. Also, if the user is in a hurry, the notification unit can provide quick notifications and information that focuses on the main points. This makes it possible to adjust the timing of notifications according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0128] The notification unit can adjust the level of detail of the notification based on the importance of the item when notifying. For example, the notification unit notifies items with high importance in detail. The notification unit can also adjust the level of detail of the notification based on the importance of the item. It can also notify items with high importance by emphasizing them. This makes it possible to provide the optimal level of detail of the notification based on the importance of the item. For example, the level of demand, the magnitude of the impact, etc. are used to evaluate the importance.

[0129] The notification unit can apply an appropriate notification algorithm depending on the category of the item when making a notification. For example, in the case of office furniture, the notification unit can provide a notification by showing a specific use example. In the case of electrical appliances, the notification unit can also explain the significance of the notification from the perspective of environmental protection. The notification unit can also suggest an optimal notification algorithm depending on a specific category. This makes it possible to provide an optimal notification algorithm depending on the category of the item. Category classifications include, for example, household goods, office supplies, electrical appliances, etc.

[0130] The notification unit can improve the accuracy of notifications based on the user's past notification history when notifying. The notification unit can, for example, suggest an optimal notification method based on the user's past notification history. The notification unit can also determine notification priorities based on the user's past notification history. The notification unit can also provide customizable notification options by referring to the user's past notification history. This makes it possible to provide an optimal notification method based on the user's past notification history. The notification history includes, for example, the number of past notifications, the type of notification, etc.

[0131] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user emotions. For example, if the user is emotional, the notification unit can display a longer notification message that appeals to the user's emotions. If the user is relaxed, the notification unit can display a longer notification message that includes detailed information. If the user is in a hurry, the notification unit can display a short notification message that is concise and to the point. This allows the length of the notification to be adjusted according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0132] The notification unit can determine the priority of notifications based on the time of item provision at the time of notification. For example, the notification unit gives priority to notifying items that have recently been submitted. The notification unit can also determine the priority of notifications based on the time of submission. It can also highlight recently submitted items when notified. This makes it possible to provide optimal notification priority based on the time of submission of items. For example, the frequency of provision and the timing of provision are used to evaluate the time of provision.

[0133] The notification unit can adjust the order of notifications based on the relevance of items when notifying. For example, the notification unit prioritizes notifying highly relevant items. The notification unit can also determine the order of notifications based on the relevance of items. It can also highlight highly relevant items when notifying. This makes it possible to provide an optimal order of notifications based on the relevance of items. For example, a co-occurrence network, a relevance score, etc. are used to evaluate the relevance.

[0134] The notification unit can adjust the use of technical terms in notifications depending on the user's level of knowledge. For example, if the user has specialized knowledge, the notification unit can use detailed technical terms to notify the user. If the user does not have specialized knowledge, the notification unit can use simple, easy-to-understand language to notify the user. The notification unit can also suggest an optimal notification message depending on the user's level of expertise. This makes it possible to provide an optimal notification message depending on the user's level of expertise. For example, the level of expertise can be evaluated using qualifications, years of experience, etc. === Hard Collateral 1-1 === Each of the multiple elements, including the registration unit, search unit, matching unit, donation promotion unit, display unit, and notification 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 registration unit is realized by the control unit 46A of the smart device 14 and allows the user to register items they no longer need. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for businesses and organizations within the area based on the registered information. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches the searched businesses and organizations with the registered items. The donation promotion unit is realized by the specific processing unit 290 of the data processing device 12 and promotes donations to organizations and projects that need specific items. The display unit is realized by the control unit 46A of the smart device 14 and displays detailed information about the items registered by the user. The notification unit is realized by the control unit 46A of the smart device 14 and notifies the user of the matching results. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned registration unit, search unit, matching unit, donation promotion unit, display unit, and notification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and allows the user to register items they do not need. The search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for businesses and organizations within the area based on the registered information. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and matches the searched businesses and organizations with the registered items. The donation promotion unit is realized by the identification processing unit 290 of the data processing device 12 and promotes donations to organizations and projects that need specific items. The display unit is realized by the control unit 46A of the smart glasses 214 and displays detailed information about items registered by the user. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the user of the matching results. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned registration unit, search unit, matching unit, donation promotion unit, display unit, and notification unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 and allows the user to register items that they do not need. The search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for businesses and organizations in the area based on the registered information. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and matches the searched businesses and organizations with the registered items. The donation promotion unit is realized by the identification processing unit 290 of the data processing device 12 and promotes donations to organizations and projects that need specific items. The display unit is realized by the control unit 46A of the headset type terminal 314 and displays detailed information about items registered by the user. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user of the matching results. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned registration unit, search unit, matching unit, donation promotion unit, display unit, and notification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and allows the user to register items that they no longer need. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for businesses and organizations in the area based on the registered information. The matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches the searched businesses and organizations with the registered items. The donation promotion unit is realized by the specific processing unit 290 of the data processing device 12 and promotes donations to organizations and projects that need specific items. The display unit is realized by the control unit 46A of the robot 414 and displays detailed information about the items registered by the user. The notification unit is realized by the control unit 46A of the robot 414 and notifies the user of the matching results.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The app service can estimate the user's emotions and adjust the donation request method based on the estimated emotions. For example, if the user is emotional, an emotional message can be displayed to encourage donations. If the user is relaxed, detailed information can be provided to explain the significance of donations. Furthermore, if the user is in a hurry, a concise and to-the-point message can be displayed to encourage donations. In this way, by adjusting the donation request method according to the user's emotions, the success rate of donations can be improved. Emotions can be estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0137] The app service can analyze a user's past donation history and determine donation priorities. For example, it can automatically display the types of items that the user has frequently donated in the past as candidates. It can also prioritize suggestions for organizations and projects to which the user has donated in the past. Furthermore, it can predict and suggest methods for encouraging donations at specific times based on the user's past donation history. This can improve donation efficiency by suggesting the optimal donation method based on the user's past donation history.

[0138] The app service can determine the priority of donations based on the user's geographic location information. For example, items available near the user's current location can be given priority for donation. The app service can also automatically filter and display related items based on the user's geographic location information. Furthermore, the app service can suggest the optimal donation method based on the user's past location information. This can improve the efficiency of donations by providing the optimal donation method based on the user's geographic location information.

[0139] The app service can analyze the user's social media activity and adjust the method of soliciting donations. For example, it can prioritize donations of items related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and automatically filter and display related items. It can also suggest related items based on the activity of the user's friends on social media. This can improve donation efficiency by providing the most appropriate donation method based on the user's social media activity.

[0140] The app service can estimate the user's emotions and adjust the length of the donation based on the estimated emotions. For example, if the user is emotional, a longer, emotional message can be displayed to encourage donations. If the user is relaxed, a longer message with detailed information can be displayed. Furthermore, if the user is in a hurry, a short, concise message that gets to the point can be displayed to encourage donations. In this way, by adjusting the length of donations according to the user's emotions, the success rate of donations can be improved. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0141] The app service can estimate a user's emotions and adjust search criteria based on the estimated emotions. For example, if the user is stressed, it can display search results concisely and highlight important information. Alternatively, if the user is relaxed, it can provide detailed search options and suggest customizable search methods. Furthermore, if the user is in a hurry, it can quickly display search results and suggest the most suitable items. This improves search efficiency by adjusting search criteria according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0142] The app service can estimate a user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if a user is feeling stressed, important information can be displayed first. Alternatively, if a user is relaxed, search results containing more detailed information can be displayed. Furthermore, if a user is in a hurry, search results can be displayed quickly and the most suitable items can be suggested. This improves search efficiency by adjusting the display order of search results according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0143] The app service can estimate a user's emotions and adjust matching criteria based on the estimated emotions. For example, if the user is feeling stressed, important information can be highlighted. If the user is relaxed, detailed matching options can be provided and a customizable matching method can be suggested. Furthermore, if the user is in a hurry, matching results can be quickly displayed and the most suitable items can be suggested. This allows for improved matching efficiency by adjusting matching criteria according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0144] The app service can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling stressed, the app service can reduce notifications and notify only important information. If the user is relaxed, the app service can provide notifications with detailed information. Furthermore, if the user is in a hurry, the app service can provide quick notifications with information that covers the main points. This allows the app service to improve notification efficiency by adjusting the timing of notifications according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0145] The app service can analyze the user's past notification history and determine notification priorities. For example, it can automatically display the types of items for which the user has frequently received notifications in the past as candidates. It can also suggest the optimal notification timing based on the time periods in which the user received notifications in the past. It can also prioritize specific notification methods (email, push notification, etc.) based on the user's past notification history. This can improve notification efficiency by suggesting the optimal notification method based on the user's past notification history.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The registration unit registers items that the user no longer needs, such as office furniture and electrical appliances such as desks, chairs, and computers. Step 2: The search unit searches for businesses and organizations in the area based on the information registered by the registration unit, such as educational institutions, public interest organizations, and start-up companies. Step 3: The matching unit matches the businesses and organizations found by the search unit with the registered items. For example, it compares the registered items with the required items and proposes the best match. Step 4: The Donation Promotion Department promotes donations to organizations or projects that need specific items. For example, if an educational institution needs desks and chairs to equip a new classroom, they can provide that information through the app and ask for donations.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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 registration unit that accepts registration of unnecessary items; a search unit that searches for businesses and organizations within the area based on the information registered by the registration unit; a matching unit that matches businesses or organizations searched by the search unit with registered items; A donation promotion department that promotes donations to organizations or projects in need of specific items. A system characterized by:

2. Equipped with a display unit that displays detailed information about items registered by the user 2. The system of claim 1.

3. Equipped with a notification unit that notifies the matching results 2. The system of claim 1.

4. The registration unit Users list office furniture and appliances 2. The system of claim 1.

5. The search unit Search for educational institutions, public interest organizations, and startups 2. The system of claim 1.

6. The matching unit Match registered items with required items and suggest matching 2. The system of claim 1.

7. The donation promotion department Call for donations to organizations or projects in need of specific items 2. The system of claim 1.

8. The registration unit Estimate the user's emotions and adjust the timing of registration based on the estimated user emotions.

2. The system of claim 1.

9. The registration unit Analyze users' past registration history and select the registration method 2. The system of claim 1.

10. The registration unit Filtering based on users' current projects and interests when registering 2. The system of claim 1.

Images