system

Abstract

The system according to the embodiment aims to efficiently acquire information on collection methods for recyclable materials and recycle shops. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an acquisition unit, a display unit, and a second display unit. The reception unit receives photographs of recyclable materials from the user. The analysis unit analyzes the photographs received by the reception unit and identifies the type of recyclable material. Based on the type of recyclable material identified by the analysis unit, the acquisition unit acquires collection rules for each municipality based on the entered address. The display unit displays the method of collecting the recyclable material based on the information acquired by the acquisition unit. The second display unit displays information on nearby recycling shops based on the information displayed by the display unit.

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 techniques have had the problem of making it difficult to efficiently obtain information on collection methods for recyclable materials and information on recycle shops.

[0005] The system according to the embodiment aims to efficiently acquire information on collection methods for recyclable materials and recycle shops. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an acquisition unit, a display unit, and a second display unit. The reception unit receives photos of recyclables from a user. The analysis unit analyzes the photos received by the reception unit and identifies the type of recyclable. The acquisition unit acquires collection rules for each local government based on the input address and the type of recyclable identified by the analysis unit. The display unit displays collection methods for recyclables based on the information acquired by the acquisition unit. The second display unit displays information about nearby recycle shops based on the information displayed by the display unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently acquire information on resource collection methods and recycling shops. [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 of form 1) A recyclable material collection system according to an embodiment of the present invention is a chat-style service that utilizes a generation AI to provide collection methods for recyclable materials generated during daily life or general cleaning. In this recyclable material collection system, a user takes a photo of a recyclable material and sends it via chat. The generation AI analyzes the photo and identifies the type of recyclable material. The system then obtains collection rules for each municipality based on the input address and displays the collection method. Information about nearby recycle shops is also displayed. This system allows users to easily learn how to collect recyclable materials. For example, a user takes a photo of a recyclable material and sends it via chat. The user sends the photo using a specific application, such as uploading a photo taken with a smartphone camera to the application. The generation AI then analyzes the sent photo. The generation AI uses image recognition technology to identify the type of recyclable material in the photo. For example, it can identify recyclable materials such as plastic bottles, paper, and metal. The generation AI then obtains collection rules for each municipality based on the input address. Based on the address information entered by the user, the generation AI retrieves the collection rules for the corresponding municipality from a database. For example, it obtains information such as the type of recyclable material collected on a specific day of the week and the collection method. Finally, it displays the collection method for the recyclable material based on the obtained information. The AI ​​generator displays to the user the collection methods and collection dates for recyclables. It also displays information about nearby recycle shops. This allows the user to know how to properly dispose of recyclables. This allows the recyclable collection system to easily inform the user of how to collect recyclables.

[0029] The resource collection system according to this embodiment comprises a reception unit, an analysis unit, an acquisition unit, a display unit, and a second display unit. The reception unit receives photographs of resources from the user. Photographs of resources taken by the user are, for example, taken with a smartphone camera and transmitted through a specific application. The reception unit can, for example, receive the photographs in digital format and convert them into a format that can be processed within the system. The analysis unit uses a generation AI to analyze the photographs received by the reception unit and identify the types of resources. The generation AI uses, for example, image recognition technology to identify the resources shown in the photographs. For example, it can identify resources such as plastic bottles, paper, and metal. The analysis unit, for example, receives a prompt from the generation AI such as "What is the object in this image?" and identifies the type of resource. The acquisition unit, based on the type of resource identified by the analysis unit, obtains collection rules for each municipality based on the entered address. The acquisition unit obtains collection rules for the relevant municipality from a database based on the address information entered by the user. For example, it obtains information such as the types of resources collected on specific days of the week and the collection methods. The display unit displays the method of resource collection based on the information acquired by the acquisition unit. The display unit displays, for example, the method of resource collection and the collection date to the user. The second display unit displays information about nearby recycling shops based on the information displayed by the display unit. The second display unit displays information about nearby recycling shops based, for example, the user's address information. As a result, the resource collection system according to the embodiment allows the user to easily find out how to collect resources.

[0030] The analysis unit can analyze a photo using the generation AI to identify the type of recyclable material. The generation AI can identify recyclable materials in the photo using, for example, image recognition technology. For example, it can identify recyclable materials such as plastic bottles, paper, and metals. The generation AI analyzes the photo using technologies such as GAN (generative artificial network) and Transformer models. For example, the generation AI can extract features of objects in the photo and identify the type of recyclable material based on those features. The generation AI can also identify the type of recyclable material in the photo using, for example, a machine learning model. For example, the generation AI can learn from image data of a large amount of recyclable materials and analyze new photos based on that knowledge. This allows the generation AI to accurately identify the type of recyclable material. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input a photo into the generation AI and have the generation AI identify the type of recyclable material.

[0031] The acquisition unit can acquire collection rules for each municipality from a database based on the input address. The acquisition unit acquires the collection rules for the corresponding municipality from a database based on, for example, address information entered by a user. The database includes, for example, a relational database or a NoSQL database. The acquisition unit acquires the collection rules for the corresponding municipality using, for example, database search technology. For example, the acquisition unit acquires information such as the type of recyclable material to be collected on a specific day of the week and the collection method from the database. The acquisition unit can also acquire the collection rules for the municipality using, for example, an API call. For example, the acquisition unit calls an API for acquiring collection rules from the official website of the municipality. This allows the acquisition unit to accurately acquire the collection rules for each municipality. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input address information into the generation AI and cause the generation AI to acquire the collection rules.

[0032] The display unit can display the collection method or collection date for recyclables. The display unit, for example, displays the collection method or collection date for recyclables to the user. The collection method includes, for example, a collection location and a collection procedure. The collection date includes, for example, a specific day of the week or a specific day of the month. The display unit, for example, displays the collection rules of the relevant local government based on the user's address information. For example, the display unit displays information such as the type of recyclables collected on a specific day of the week and the collection method. The display unit can also display the collection dates for recyclables to the user in calendar format. This allows the user to easily check the collection method and collection date for recyclables. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input information about the collection rules into the generation AI and have the generation AI generate the display content.

[0033] The second display unit can display information about nearby recycle shops. The second display unit displays information about nearby recycle shops based on, for example, the user's address information. Nearby recycle shops include, for example, recycle shops within a specific distance or recycle shops in a specific area. The second display unit displays information such as the recycle shop's address, business hours, and items sold. This allows the user to easily check information about nearby recycle shops. Some or all of the above-described processing on the second display unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the second display unit can input information about recycle shops into the generation AI and have the generation AI generate the display content.

[0034] The reception unit can analyze the user's past usage history and select the optimal reception method. The reception unit, for example, analyzes the user's past usage history and selects the optimal reception method. The past usage history includes, for example, usage frequency and past behavioral patterns. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. The reception unit can also select and suggest the most efficient reception method from the user's past usage history. Furthermore, the reception unit can analyze the user's past usage history and suggest the optimal reception method for a specific time period. This makes it possible to provide the optimal reception method based on the user's past usage history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input past usage history data into the generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit can filter photos based on the user's current living situation and areas of interest when receiving the photos. For example, the reception unit filters photos based on the user's current living situation and areas of interest when receiving the photos. Examples of current living situations include family structure and daily routines. Examples of areas of interest include hobbies and topics of interest. For example, if the user is busy, simple reception procedures can be provided to quickly receive photos. Alternatively, if the user is relaxed, detailed reception procedures can be provided to carefully receive photos. Furthermore, based on the user's areas of interest, related resources can be prioritized. This enables filtering according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0036] When accepting photos, the acceptance unit can prioritize accepting photos with high relevance, taking into account the user's geographical location information. For example, when accepting photos, the acceptance unit prioritizes accepting photos with high relevance, taking into account the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. For example, if the user is in a specific area, photos of resources related to that area can be prioritized. Also, if the user is traveling, photos with high relevance can be prioritized based on the user's current location. Furthermore, if the user is in a specific location, photos of resources related to that location can be prioritized. This allows photos with high relevance to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the acceptance unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input geographical location information to the generation AI and cause the generation AI to select photos with high relevance.

[0037] The reception unit can analyze the user's social media activity when receiving a photo and receive related photos. For example, the reception unit can analyze the user's social media activity when receiving a photo and receive related photos. Social media activity includes, for example, the content of posts and the number of likes. For example, photos of related resources can be preferentially received based on photos shared by the user on social media. Furthermore, photos of resources of high interest can be preferentially received based on the user's social media activity. Furthermore, photos of related resources can be preferentially received based on photos tagged by the user on social media. This allows related photos to be received based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input social media activity data into a generation AI and cause the generation AI to select related photos.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the resource during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the resource. The importance of a resource includes, for example, the need for recycling and its impact on the environment. For example, a detailed analysis can be performed on resource materials with high importance. Conversely, a simplified analysis can be performed on resource materials with low importance. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the resource. This allows the level of detail of the analysis to be adjusted according to the importance of the resource. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input resource importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the resource during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the resource during analysis. Categories of resource materials include, for example, plastics, paper, and metals. For example, PET bottles can be analyzed as recyclable plastics. Paper can also be analyzed as recyclable paper. Furthermore, metals can be analyzed as recyclable metals. This enables analysis according to the category of the resource material. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input resource category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0040] The analysis unit can determine the priority of analysis based on the submission timing of resource materials during the analysis. For example, the analysis unit can determine the priority of analysis based on the submission timing of resource materials during the analysis. The submission timing of resource materials includes, for example, the submission date and submission time. For example, resource materials submitted earlier can be analyzed preferentially. Resource materials submitted later can be postponed. Furthermore, the analysis priority can be dynamically adjusted based on the submission timing. This allows the analysis priority to be determined based on the submission timing of resource materials. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input submission timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relationships between resources during the analysis. For example, the analysis unit can adjust the order of analysis based on the relationships between resources during the analysis. The relationships between resources include, for example, the degree of content matching and past history. For example, resources with high relevance can be analyzed preferentially. Resources with low relevance can also be postponed. Furthermore, the order of analysis can be dynamically adjusted based on the relationships between resources. This allows the order of analysis to be adjusted based on the relationships between resources. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input resource relationship data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The acquisition unit can predict the current collection rule by referring to past collection rule data at the time of acquisition. For example, the acquisition unit predicts the current collection rule by referring to past collection rule data at the time of acquisition. The past collection rule data includes, for example, past collection dates and collection methods. For example, the current collection rule can be predicted based on the past collection rule data. The past collection rule data can also be analyzed to provide the most appropriate collection rule. Furthermore, the current collection rule can also be dynamically adjusted by referring to the past collection rule data. In this way, the current collection rule can be predicted based on the past collection rule data. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the past collection rule data into the generation AI and cause the generation AI to predict the current collection rule.

[0043] The acquisition unit can apply different acquisition methods to different categories of recyclable materials during acquisition. For example, the acquisition unit applies different acquisition methods to different categories of recyclable materials during acquisition. Recyclable material categories include, for example, plastic, paper, and metal. For example, PET bottles can be acquired as recyclable plastics. Paper can also be acquired as recyclable paper. Metal can also be acquired as recyclable metals. This allows an acquisition method to be applied according to the category of recyclable materials. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input category data of recyclable materials into the generation AI and cause the generation AI to apply the acquisition method.

[0044] The acquisition unit can analyze changes in collection rules based on the submission timing of recyclable materials at the time of acquisition. For example, the acquisition unit can analyze changes in collection rules based on the submission timing of recyclable materials at the time of acquisition. The submission timing of recyclable materials includes, for example, the submission date and time. For example, it can provide the latest collection rules to recyclable materials submitted early. It can also provide past collection rules to recyclable materials submitted late. Furthermore, it can dynamically analyze changes in collection rules based on the submission timing. This allows for analysis of changes in collection rules based on the submission timing of recyclable materials. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input submission timing data into a generating AI and have the generating AI perform the analysis of changes in collection rules.

[0045] The acquisition unit can analyze recovery rules by referring to relevant market data for resources at the time of acquisition. For example, the acquisition unit can analyze recovery rules by referring to relevant market data for resources at the time of acquisition. Relevant market data includes, for example, market prices and demand forecasts. For example, it can provide an optimal recovery rule based on the market value of the resources. It can also dynamically adjust the recovery rule based on the market demand for resources. Furthermore, it can predict changes in the recovery rule by referring to the market data for resources. This allows for the analysis of recovery rules based on the market data for resources. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input relevant market data into a generating AI and have the generating AI perform the analysis of recovery rules.

[0046] The display unit can adjust the level of detail of the display based on the importance of the resource material during display. For example, the display unit can adjust the level of detail of the display based on the importance of the resource material during display. The importance of a resource material includes, for example, the need for recycling and its impact on the environment. For example, a detailed display can be provided for resource materials of high importance. Conversely, a concise display can be provided for resource materials of low importance. Furthermore, the level of detail of the display can be dynamically adjusted according to the importance of the resource material. This allows the level of detail of the display to be adjusted according to the importance of the resource material. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input resource material importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the display.

[0047] The display unit can apply different display algorithms depending on the category of the resource material during display. For example, the display unit can apply different display algorithms depending on the category of the resource material during display. Categories of resource materials include, for example, plastics, paper, and metals. For example, PET bottles can be displayed as recyclable plastics. Paper can be displayed as recyclable paper. Furthermore, metals can be displayed as recyclable metals. This makes it possible to display according to the category of the resource material. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input resource category data into a generation AI and have the generation AI execute the application of the display algorithm.

[0048] The display unit can determine the display priority based on the submission time of the recyclables when displaying. The display unit, for example, determines the display priority based on the submission time of the recyclables when displaying. The submission time of the recyclables includes, for example, the submission date and the submission time. For example, recyclables submitted earlier can be displayed preferentially. Recyclables submitted later can also be postponed. Furthermore, the display priority can be dynamically adjusted based on the submission time. This makes it possible to determine the display priority based on the submission time of the recyclables. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input submission time data into the generation AI and have the generation AI determine the display priority.

[0049] The display unit can adjust the display order based on the relevance of the resource items during display. The display unit, for example, adjusts the display order based on the relevance of the resource items during display. The relevance of the resource items includes, for example, the degree of similarity of content and past history. For example, highly relevant resource items can be displayed preferentially. Also, less relevant resource items can be displayed later. Furthermore, the display order can be dynamically adjusted based on the relevance of the resource items. This makes it possible to adjust the display order based on the relevance of the resource items. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input relevance data of the resource items into the generation AI and have the generation AI adjust the display order.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception department can analyze a user's past usage history and select the most suitable reception method. For example, it can prioritize suggesting reception methods that the user has frequently used in the past. It can also select and suggest the most efficient reception method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and suggest the most suitable reception method for a specific time period. This allows the reception department to provide the most suitable reception method based on the user's past usage history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input past usage history data into a generative AI and have the generative AI select the most suitable reception method.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the resources during the analysis. For example, it can perform a detailed analysis on resources of high importance, and a simplified analysis on resources of low importance. Furthermore, it can dynamically adjust the level of detail of the analysis according to the importance of the resources. This allows the level of detail of the analysis to be adjusted according to the importance of the resources. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input resource importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0053] At the time of acquisition, the acquisition unit can predict the current collection rule by referring to past collection rule data. For example, the current collection rule can be predicted based on the past collection rule data. The past collection rule data can also be analyzed to provide the most appropriate collection rule. Furthermore, the current collection rule can be dynamically adjusted by referring to the past collection rule data. In this way, the current collection rule can be predicted based on the past collection rule data. Some or all of the above-described processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the past collection rule data into the generation AI and cause the generation AI to predict the current collection rule.

[0054] The display unit can adjust the level of detail of the display based on the importance of the resource when displaying. For example, a detailed display can be provided for a resource with a high level of importance. A simple display can also be provided for a resource with a low level of importance. Furthermore, the level of detail of the display can be dynamically adjusted according to the importance of the resource. This allows the level of detail of the display to be adjusted according to the importance of the resource. Some or all of the above-described processing in the display unit can be performed using, or without, the generation AI. For example, the display unit can input the importance data of the resource into the generation AI and have the generation AI adjust the level of detail of the display.

[0055] When displaying, the display unit can determine the display priority based on the submission time of the recyclables. For example, recyclables that were submitted earlier can be displayed preferentially. Recyclables that were submitted later can also be postponed. Furthermore, the display priority can be dynamically adjusted based on the submission time. This allows the display priority to be determined based on the submission time of the recyclables. Some or all of the above-mentioned processing in the display unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the display unit can input submission time data into the generation AI and have the generation AI determine the display priority.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives photos of the resources from the user. The photos of the resources taken by the user may be taken with a smartphone camera, for example, and sent through a specific application. The reception unit receives the photos in digital format and can convert them into a format that can be processed within the system. Step 2: The analysis unit uses the generation AI to analyze the photo received by the reception unit and identify the type of recyclable material. The generation AI uses image recognition technology to identify the recyclable materials in the photo. For example, it can identify recyclable materials such as plastic bottles, paper, and metal. Step 3: The acquisition unit retrieves collection rules for each municipality based on the entered address, using the type of recyclable material identified by the analysis unit. The acquisition unit retrieves the collection rules for the relevant municipality from the database based on the address information entered by the user. For example, it retrieves information such as the types of recyclable materials collected on specific days of the week and the collection methods. Step 4: The display unit displays the collection method for recyclable materials based on the information acquired by the acquisition unit. The display unit displays the collection method and collection date for recyclable materials to the user. Step 5: The second display unit displays information about nearby recycle shops based on the information displayed by the display unit. The second display unit displays information about nearby recycle shops based on the user's address information.

[0058] (Example 2) A recyclable material collection system according to an embodiment of the present invention is a chat-style service that utilizes a generation AI to provide collection methods for recyclable materials generated during daily life or general cleaning. In this recyclable material collection system, a user takes a photo of a recyclable material and sends it via chat. The generation AI analyzes the photo and identifies the type of recyclable material. The system then obtains collection rules for each municipality based on the input address and displays the collection method. Information about nearby recycle shops is also displayed. This system allows users to easily learn how to collect recyclable materials. For example, a user takes a photo of a recyclable material and sends it via chat. The user sends the photo using a specific application, such as uploading a photo taken with a smartphone camera to the application. The generation AI then analyzes the sent photo. The generation AI uses image recognition technology to identify the type of recyclable material in the photo. For example, it can identify recyclable materials such as plastic bottles, paper, and metal. The generation AI then obtains collection rules for each municipality based on the input address. Based on the address information entered by the user, the generation AI retrieves the collection rules for the corresponding municipality from a database. For example, it obtains information such as the type of recyclable material collected on a specific day of the week and the collection method. Finally, it displays the collection method for the recyclable material based on the obtained information. The AI ​​generator displays to the user the collection methods and collection dates for recyclables. It also displays information about nearby recycle shops. This allows the user to know how to properly dispose of recyclables. This allows the recyclable collection system to easily inform the user of how to collect recyclables.

[0059] The resource collection system according to this embodiment comprises a reception unit, an analysis unit, an acquisition unit, a display unit, and a second display unit. The reception unit receives photographs of resources from the user. Photographs of resources taken by the user are, for example, taken with a smartphone camera and transmitted through a specific application. The reception unit can, for example, receive the photographs in digital format and convert them into a format that can be processed within the system. The analysis unit uses a generation AI to analyze the photographs received by the reception unit and identify the types of resources. The generation AI uses, for example, image recognition technology to identify the resources shown in the photographs. For example, it can identify resources such as plastic bottles, paper, and metal. The analysis unit, for example, receives a prompt from the generation AI such as "What is the object in this image?" and identifies the type of resource. The acquisition unit, based on the type of resource identified by the analysis unit, obtains collection rules for each municipality based on the entered address. The acquisition unit obtains collection rules for the relevant municipality from a database based on the address information entered by the user. For example, it obtains information such as the types of resources collected on specific days of the week and the collection methods. The display unit displays the method of resource collection based on the information acquired by the acquisition unit. The display unit displays, for example, the method of resource collection and the collection date to the user. The second display unit displays information about nearby recycling shops based on the information displayed by the display unit. The second display unit displays information about nearby recycling shops based, for example, the user's address information. As a result, the resource collection system according to the embodiment allows the user to easily find out how to collect resources.

[0060] The analysis unit can analyze a photo using the generation AI to identify the type of recyclable material. The generation AI can identify recyclable materials in the photo using, for example, image recognition technology. For example, it can identify recyclable materials such as plastic bottles, paper, and metals. The generation AI analyzes the photo using technologies such as GAN (generative artificial network) and Transformer models. For example, the generation AI can extract features of objects in the photo and identify the type of recyclable material based on those features. The generation AI can also identify the type of recyclable material in the photo using, for example, a machine learning model. For example, the generation AI can learn from image data of a large amount of recyclable materials and analyze new photos based on that knowledge. This allows the generation AI to accurately identify the type of recyclable material. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input a photo into the generation AI and have the generation AI identify the type of recyclable material.

[0061] The acquisition unit can acquire collection rules for each municipality from a database based on the input address. The acquisition unit acquires the collection rules for the corresponding municipality from a database based on, for example, address information entered by a user. The database includes, for example, a relational database or a NoSQL database. The acquisition unit acquires the collection rules for the corresponding municipality using, for example, database search technology. For example, the acquisition unit acquires information such as the type of recyclable material to be collected on a specific day of the week and the collection method from the database. The acquisition unit can also acquire the collection rules for the municipality using, for example, an API call. For example, the acquisition unit calls an API for acquiring collection rules from the official website of the municipality. This allows the acquisition unit to accurately acquire the collection rules for each municipality. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input address information into the generation AI and cause the generation AI to acquire the collection rules.

[0062] The display unit can display the collection method or collection date for recyclables. The display unit, for example, displays the collection method or collection date for recyclables to the user. The collection method includes, for example, a collection location and a collection procedure. The collection date includes, for example, a specific day of the week or a specific day of the month. The display unit, for example, displays the collection rules of the relevant local government based on the user's address information. For example, the display unit displays information such as the type of recyclables collected on a specific day of the week and the collection method. The display unit can also display the collection dates for recyclables to the user in calendar format. This allows the user to easily check the collection method and collection date for recyclables. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input information about the collection rules into the generation AI and have the generation AI generate the display content.

[0063] The second display unit can display information about nearby recycle shops. The second display unit displays information about nearby recycle shops based on, for example, the user's address information. Nearby recycle shops include, for example, recycle shops within a specific distance or recycle shops in a specific area. The second display unit displays information such as the recycle shop's address, business hours, and items sold. This allows the user to easily check information about nearby recycle shops. Some or all of the above-described processing on the second display unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the second display unit can input information about recycle shops into the generation AI and have the generation AI generate the display content.

[0064] The reception unit can estimate the user's emotions and adjust the timing of photo reception based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of photo reception based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is stressed, the timing of photo reception can be delayed and the user can be prompted to accept the photo again when they are relaxed. Also, if the user is in a hurry, the photo can be immediately accepted and processing can be started quickly. Furthermore, if the user is excited, the acceptance of the photo can be temporarily suspended and the user can be prompted to accept it again after they have calmed down. This allows photos to be accepted at an appropriate time depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or without a generation AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0065] The reception unit can analyze the user's past usage history and select the optimal reception method. The reception unit, for example, analyzes the user's past usage history and selects the optimal reception method. The past usage history includes, for example, usage frequency and past behavioral patterns. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. The reception unit can also select and suggest the most efficient reception method from the user's past usage history. Furthermore, the reception unit can analyze the user's past usage history and suggest the optimal reception method for a specific time period. This makes it possible to provide the optimal reception method based on the user's past usage history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input past usage history data into the generation AI and have the generation AI select the optimal reception method.

[0066] The reception unit can filter photos based on the user's current living situation and areas of interest when receiving the photos. For example, the reception unit filters photos based on the user's current living situation and areas of interest when receiving the photos. Examples of current living situations include family structure and daily routines. Examples of areas of interest include hobbies and topics of interest. For example, if the user is busy, simple reception procedures can be provided to quickly receive photos. Alternatively, if the user is relaxed, detailed reception procedures can be provided to carefully receive photos. Furthermore, based on the user's areas of interest, related resources can be prioritized. This enables filtering according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0067] The reception unit can estimate the user's emotions and determine the priority of photos to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of photos to be received based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is stressed, photos of low importance can be postponed and photos of high importance can be received first. Also, if the user is relaxed, all photos can be received equally. Furthermore, if the user is in a hurry, photos of high importance can be immediately received and processed quickly. This allows the priority of photos to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0068] When accepting photos, the acceptance unit can prioritize accepting photos with high relevance, taking into account the user's geographical location information. For example, when accepting photos, the acceptance unit prioritizes accepting photos with high relevance, taking into account the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. For example, if the user is in a specific area, photos of resources related to that area can be prioritized. Also, if the user is traveling, photos with high relevance can be prioritized based on the user's current location. Furthermore, if the user is in a specific location, photos of resources related to that location can be prioritized. This allows photos with high relevance to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the acceptance unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input geographical location information to the generation AI and cause the generation AI to select photos with high relevance.

[0069] The reception unit can analyze the user's social media activity when receiving a photo and receive related photos. For example, the reception unit can analyze the user's social media activity when receiving a photo and receive related photos. Social media activity includes, for example, the content of posts and the number of likes. For example, photos of related resources can be preferentially received based on photos shared by the user on social media. Furthermore, photos of resources of high interest can be preferentially received based on the user's social media activity. Furthermore, photos of related resources can be preferentially received based on photos tagged by the user on social media. This allows related photos to be received based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input social media activity data into a generation AI and cause the generation AI to select related photos.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. This allows the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the resource during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the resource. The importance of a resource includes, for example, the need for recycling and its impact on the environment. For example, a detailed analysis can be performed on resource materials with high importance. Conversely, a simplified analysis can be performed on resource materials with low importance. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the resource. This allows the level of detail of the analysis to be adjusted according to the importance of the resource. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input resource importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of the resource during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the resource during analysis. Categories of resource materials include, for example, plastics, paper, and metals. For example, PET bottles can be analyzed as recyclable plastics. Paper can also be analyzed as recyclable paper. Furthermore, metals can be analyzed as recyclable metals. This enables analysis according to the category of the resource material. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input resource category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. The user's emotions are estimated using techniques such as facial recognition or voice analysis. For example, if the user is in a hurry, a short, concise analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0074] The analysis unit can determine the priority of analysis based on the submission timing of resource materials during the analysis. For example, the analysis unit can determine the priority of analysis based on the submission timing of resource materials during the analysis. The submission timing of resource materials includes, for example, the submission date and submission time. For example, resource materials submitted earlier can be analyzed preferentially. Resource materials submitted later can be postponed. Furthermore, the analysis priority can be dynamically adjusted based on the submission timing. This allows the analysis priority to be determined based on the submission timing of resource materials. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input submission timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0075] The analysis unit can adjust the order of analysis based on the relationships between resources during the analysis. For example, the analysis unit can adjust the order of analysis based on the relationships between resources during the analysis. The relationships between resources include, for example, the degree of content matching and past history. For example, resources with high relevance can be analyzed preferentially. Resources with low relevance can also be postponed. Furthermore, the order of analysis can be dynamically adjusted based on the relationships between resources. This allows the order of analysis to be adjusted based on the relationships between resources. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input resource relationship data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The acquisition unit can estimate the user's emotions and adjust the acquisition method of the collection rules based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the acquisition method of the collection rules based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is relaxed, detailed collection rules can be provided. If the user is in a hurry, concise collection rules can be provided. Furthermore, if the user is excited, visually stimulating collection rules can be provided. This allows the acquisition method of the collection rules to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the acquisition method of the collection rules.

[0077] The acquisition unit can predict the current collection rule by referring to past collection rule data at the time of acquisition. For example, the acquisition unit predicts the current collection rule by referring to past collection rule data at the time of acquisition. The past collection rule data includes, for example, past collection dates and collection methods. For example, the current collection rule can be predicted based on the past collection rule data. The past collection rule data can also be analyzed to provide the most appropriate collection rule. Furthermore, the current collection rule can also be dynamically adjusted by referring to the past collection rule data. In this way, the current collection rule can be predicted based on the past collection rule data. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the past collection rule data into the generation AI and cause the generation AI to predict the current collection rule.

[0078] The acquisition unit can apply different acquisition methods to different categories of recyclable materials during acquisition. For example, the acquisition unit applies different acquisition methods to different categories of recyclable materials during acquisition. Recyclable material categories include, for example, plastic, paper, and metal. For example, PET bottles can be acquired as recyclable plastics. Paper can also be acquired as recyclable paper. Metal can also be acquired as recyclable metals. This allows an acquisition method to be applied according to the category of recyclable materials. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input category data of recyclable materials into the generation AI and cause the generation AI to apply the acquisition method.

[0079] The acquisition unit can estimate the user's emotions and adjust the importance of the collection rules based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the importance of the collection rules based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is relaxed, detailed collection rules can be provided. If the user is in a hurry, concise collection rules can be provided. Furthermore, if the user is excited, visually stimulating collection rules can be provided. This allows the importance of the collection rules to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of the collection rules.

[0080] The acquisition unit can analyze changes in collection rules based on the submission timing of recyclable materials at the time of acquisition. For example, the acquisition unit can analyze changes in collection rules based on the submission timing of recyclable materials at the time of acquisition. The submission timing of recyclable materials includes, for example, the submission date and time. For example, it can provide the latest collection rules to recyclable materials submitted early. It can also provide past collection rules to recyclable materials submitted late. Furthermore, it can dynamically analyze changes in collection rules based on the submission timing. This allows for analysis of changes in collection rules based on the submission timing of recyclable materials. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input submission timing data into a generating AI and have the generating AI perform the analysis of changes in collection rules.

[0081] The acquisition unit can analyze recovery rules by referring to relevant market data for resources at the time of acquisition. For example, the acquisition unit can analyze recovery rules by referring to relevant market data for resources at the time of acquisition. Relevant market data includes, for example, market prices and demand forecasts. For example, it can provide an optimal recovery rule based on the market value of the resources. It can also dynamically adjust the recovery rule based on the market demand for resources. Furthermore, it can predict changes in the recovery rule by referring to the market data for resources. This allows for the analysis of recovery rules based on the market data for resources. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input relevant market data into a generating AI and have the generating AI perform the analysis of recovery rules.

[0082] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and adjusts the display method based on the estimated user's emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is relaxed, a detailed display method can be provided. If the user is in a hurry, a concise display method can be provided. Furthermore, if the user is excited, a visually stimulating display method can be provided. This allows the display method to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0083] The display unit can adjust the level of detail of the display based on the importance of the resource material during display. For example, the display unit can adjust the level of detail of the display based on the importance of the resource material during display. The importance of a resource material includes, for example, the need for recycling and its impact on the environment. For example, a detailed display can be provided for resource materials of high importance. Conversely, a concise display can be provided for resource materials of low importance. Furthermore, the level of detail of the display can be dynamically adjusted according to the importance of the resource material. This allows the level of detail of the display to be adjusted according to the importance of the resource material. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input resource material importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the display.

[0084] The display unit can apply different display algorithms depending on the category of the resource material during display. For example, the display unit can apply different display algorithms depending on the category of the resource material during display. Categories of resource materials include, for example, plastics, paper, and metals. For example, PET bottles can be displayed as recyclable plastics. Paper can be displayed as recyclable paper. Furthermore, metals can be displayed as recyclable metals. This makes it possible to display according to the category of the resource material. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input resource category data into a generation AI and have the generation AI execute the application of the display algorithm.

[0085] The display unit can estimate the user's emotion and adjust the display length based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and adjusts the display length based on the estimated user's emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is in a hurry, a short, to-the-point display can be provided. If the user is relaxed, a detailed display can be provided. Furthermore, if the user is excited, a visually stimulating display can be provided. This allows the display length to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display length.

[0086] The display unit can determine the display priority based on the submission time of the recyclables when displaying. The display unit, for example, determines the display priority based on the submission time of the recyclables when displaying. The submission time of the recyclables includes, for example, the submission date and the submission time. For example, recyclables submitted earlier can be displayed preferentially. Recyclables submitted later can also be postponed. Furthermore, the display priority can be dynamically adjusted based on the submission time. This makes it possible to determine the display priority based on the submission time of the recyclables. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input submission time data into the generation AI and have the generation AI determine the display priority.

[0087] The display unit can adjust the display order based on the relevance of the resource items during display. The display unit, for example, adjusts the display order based on the relevance of the resource items during display. The relevance of the resource items includes, for example, the degree of similarity of content and past history. For example, highly relevant resource items can be displayed preferentially. Also, less relevant resource items can be displayed later. Furthermore, the display order can be dynamically adjusted based on the relevance of the resource items. This makes it possible to adjust the display order based on the relevance of the resource items. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input relevance data of the resource items into the generation AI and have the generation AI adjust the display order. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, display unit, and second display unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the camera 42 and control unit 46A of the smart device 14 and receives photos of recyclables taken by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the photos using a generation AI to identify the type of recyclables. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and acquires collection rules for each municipality based on the input address. The display unit is realized, for example, by the display 40A of the smart device 14 and displays collection methods for recyclables. The second display unit is realized, for example, by the display 40A of the smart device 14 and displays information about nearby recycle shops. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, display unit, and second display unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the camera 42 and control unit 46A of the smart glasses 214 and receives a photo of the recyclable material taken by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the photo using a generation AI to identify the type of recyclable material. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and acquires collection rules for each municipality based on the input address. The display unit is realized, for example, by the display of the smart glasses 214 and displays collection methods for recyclable materials. The second display unit is realized, for example, by the display of the smart glasses 214 and displays information about nearby recycle shops. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, display unit, and second display unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the camera 42 and control unit 46A of the headset terminal 314 and receives a photo of a recyclable item taken by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the photo using a generation AI to identify the type of recyclable item. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and acquires collection rules for each municipality based on the input address. The display unit is realized, for example, by the display 343 of the headset terminal 314 and displays collection methods for recyclable items. The second display unit is realized, for example, by the display 343 of the headset terminal 314 and displays information about nearby recycle shops. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, acquisition unit, display unit, and second display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the camera 42 and control unit 46A of the robot 414 and receives photos of recyclables taken by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the photos using a generative AI to identify the type of recyclables. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and acquires collection rules for each municipality based on the input address. The display unit is realized, for example, by a display of the robot 414 and displays collection methods for recyclables. The second display unit is realized, for example, by a display of the robot 414 and displays information about nearby recycle shops.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception department can analyze a user's past usage history and select the most suitable reception method. For example, it can prioritize suggesting reception methods that the user has frequently used in the past. It can also select and suggest the most efficient reception method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and suggest the most suitable reception method for a specific time period. This allows the reception department to provide the most suitable reception method based on the user's past usage history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input past usage history data into a generative AI and have the generative AI select the most suitable reception method.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the resources during the analysis. For example, it can perform a detailed analysis on resources of high importance, and a simplified analysis on resources of low importance. Furthermore, it can dynamically adjust the level of detail of the analysis according to the importance of the resources. This allows the level of detail of the analysis to be adjusted according to the importance of the resources. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input resource importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0091] At the time of acquisition, the acquisition unit can predict the current collection rule by referring to past collection rule data. For example, the current collection rule can be predicted based on the past collection rule data. The past collection rule data can also be analyzed to provide the most appropriate collection rule. Furthermore, the current collection rule can be dynamically adjusted by referring to the past collection rule data. In this way, the current collection rule can be predicted based on the past collection rule data. Some or all of the above-described processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the past collection rule data into the generation AI and cause the generation AI to predict the current collection rule.

[0092] The display unit can adjust the level of detail of the display based on the importance of the resource when displaying. For example, a detailed display can be provided for a resource with a high level of importance. A simple display can also be provided for a resource with a low level of importance. Furthermore, the level of detail of the display can be dynamically adjusted according to the importance of the resource. This allows the level of detail of the display to be adjusted according to the importance of the resource. Some or all of the above-described processing in the display unit can be performed using, or without, the generation AI. For example, the display unit can input the importance data of the resource into the generation AI and have the generation AI adjust the level of detail of the display.

[0093] When displaying, the display unit can determine the display priority based on the submission time of the recyclables. For example, recyclables that were submitted earlier can be displayed preferentially. Recyclables that were submitted later can also be postponed. Furthermore, the display priority can be dynamically adjusted based on the submission time. This allows the display priority to be determined based on the submission time of the recyclables. Some or all of the above-mentioned processing in the display unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the display unit can input submission time data into the generation AI and have the generation AI determine the display priority.

[0094] The reception unit can estimate the user's emotions and adjust the timing of photo submission based on the estimated emotions. For example, if the user is stressed, the photo submission timing can be delayed, and the user can be prompted to submit again when they are relaxed. If the user is in a hurry, the photo can be submitted immediately, and processing can begin quickly. Furthermore, if the user is agitated, the photo submission can be temporarily suspended, and the user can be prompted to submit again after they have calmed down. This allows for photo submission at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0096] The acquisition unit can estimate the user's emotions and adjust the acquisition method of the collection rules based on the estimated user emotions. For example, if the user is relaxed, detailed collection rules can be provided. If the user is in a hurry, concise collection rules can be provided. Furthermore, if the user is excited, visually stimulating collection rules can be provided. This allows the acquisition method of the collection rules to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the acquisition method of the collection rules.

[0097] 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 relaxed, a detailed display method can be provided. If the user is in a hurry, a concise display method can be provided. Furthermore, if the user is excited, a visually stimulating display method can be provided. This allows the display method to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0098] The reception unit can estimate the user's emotions and determine the priority of photos to receive based on the estimated emotions. For example, if the user is stressed, less important photos can be postponed, and more important photos can be prioritized. If the user is relaxed, all photos can be received equally. Furthermore, if the user is in a hurry, more important photos can be received immediately, and processing can begin quickly. This allows for the prioritization of photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives photos of the resources from the user. The photos of the resources taken by the user may be taken with a smartphone camera, for example, and sent through a specific application. The reception unit receives the photos in digital format and can convert them into a format that can be processed within the system. Step 2: The analysis unit uses the generation AI to analyze the photo received by the reception unit and identify the type of recyclable material. The generation AI uses image recognition technology to identify the recyclable materials in the photo. For example, it can identify recyclable materials such as plastic bottles, paper, and metal. Step 3: The acquisition unit retrieves collection rules for each municipality based on the entered address, using the type of recyclable material identified by the analysis unit. The acquisition unit retrieves the collection rules for the relevant municipality from the database based on the address information entered by the user. For example, it retrieves information such as the types of recyclable materials collected on specific days of the week and the collection methods. Step 4: The display unit displays the collection method for recyclable materials based on the information acquired by the acquisition unit. The display unit displays the collection method and collection date for recyclable materials to the user. Step 5: The second display unit displays information about nearby recycle shops based on the information displayed by the display unit. The second display unit displays information about nearby recycle shops based on the user's address information.

[0101] 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.

[0102] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a photo of a resource from a user; an analysis unit that analyzes the photograph received by the reception unit and identifies the type of recyclable material; an acquisition unit that acquires collection rules for each local government based on the input address and the type of recyclable material identified by the analysis unit; a display unit that displays a collection method for recyclable materials based on the information acquired by the acquisition unit; a second display unit that displays information about nearby recycle shops based on the information displayed by the display unit; Equipped with A system characterized by:

2. The analysis unit Analyze photos using generative AI to identify the type of resource 2. The system of claim 1.

3. The acquisition unit Based on the entered address, the collection rules for each municipality are retrieved from the database.

2. The system of claim 1.

4. The display unit Display collection method or collection date for recyclables 2. The system of claim 1.

5. The display unit Display information about nearby thrift stores 2. The system of claim 1.

6. The reception unit A method for estimating a user's emotion and adjusting the timing of accepting photos based on the estimated user's emotion is also included.

2. The system of claim 1.

7. The reception unit Analyze the user's past usage history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit When accepting photos, filters are performed based on the user's current life situation and interests.

2. The system of claim 1.

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