System

Abstract

To provide a system that confirms proper recycling of an object to be recycled and imparts an incentive to a user.SOLUTION: A system comprises a reception part, an analysis part, an imparting part, and a tracking part. The reception part photographs an object to be recycled. The analysis part analyzes the photograph taken by the reception part to determine whether the object to be recycled is properly recycled. The imparting part imparts an incentive based upon the result of determination by the analysis part. The tracking part uses block chain technology to track an actual result of recycling.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of checking whether items to be recycled are being recycled appropriately is complicated, making it difficult to maintain users' motivation.

[0005] The system according to the embodiment aims to confirm proper recycling of recyclable items and provide incentives to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a granting unit, and a tracking unit. The reception unit takes a photo of the recycling object. The analysis unit analyzes the photo taken by the reception unit and determines whether the recycling object has been appropriately recycled. The granting unit grants an incentive based on the result determined by the analysis unit. The tracking unit tracks the recycling performance using blockchain technology. Effect of the Invention

[0007] The system according to the embodiment can confirm proper recycling of recyclable items and provide incentives to users. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The recycling support system according to the embodiment of the present invention is a system that takes a photo of an object to be recycled, analyzes the photo by a generation AI, judges the accuracy of the recycling, grants incentives, and tracks the recycling performance using blockchain technology. In this recycling support system, a user takes a photo of an object to be recycled, and a generation AI analyzes the photo to judge the type and condition of the item. Based on the analysis result, an incentive is granted in the form of points or coupons. Furthermore, the recycling performance is tracked using blockchain technology and managed transparently and safely. For example, the system judges whether plastic bottles are correctly separated or paper is appropriately recycled, and grants incentives to users to promote recycling activities. As a result, the recycling support system allows users to obtain concrete benefits through recycling activities, and motivation for recycling is improved. In addition, by using blockchain technology, the recycling performance can be managed reliably, contributing to environmental protection.

[0029] The recycling support system according to the embodiment includes a reception unit, an analysis unit, an assignment unit, and a tracking unit. The reception unit takes a photo of the recycling object. The recycling object includes, for example, plastic bottles, paper, metal, and the like, but is not limited to such examples. The reception unit takes a photo of the recycling object using, for example, a smartphone camera. The reception unit can also take high-resolution photos using a digital camera. Furthermore, the reception unit can evaluate the quality of the photo using AI and select the optimal photo. For example, the reception unit adjusts the brightness and contrast of the photo using AI to select a photo in which the recycling object is clearly shown. The analysis unit uses the generation AI to analyze the photo taken by the reception unit and determine whether the recycling object has been properly recycled. The generation AI analyzes the type and condition of the item using, for example, deep learning or a neural network. For example, the generation AI analyzes the shape and color of the plastic bottle and determines whether it has been properly separated. The generation AI can also analyze the condition of the paper and determine whether it has been properly recycled. The assignment unit assigns an incentive based on the result determined by the analysis unit. The incentive is given in the form of, for example, points or coupons, but is not limited to such examples. For example, the giving unit gives points according to the accuracy of recycling. The giving unit can also issue coupons and allow the user to use them at specific stores. The tracking unit tracks the recycling results using blockchain technology. The blockchain technology is implemented using, for example, a public blockchain or a private blockchain. For example, the tracking unit records the recycling results in the blockchain and manages them transparently and safely. The tracking unit can also enable the user to check the recycling history. As a result, the recycling support system according to the embodiment allows the user to obtain concrete benefits through recycling activities, and improves motivation for recycling. Furthermore, by using blockchain technology, the recycling results can be managed reliably, which can contribute to environmental protection.

[0030] The reception unit takes a photo of the recycling object. Examples of the recycling object include, but are not limited to, plastic bottles, paper, and metal. The reception unit takes a photo of the recycling object using, for example, a smartphone camera. The reception unit can also take high-resolution photos using a digital camera. Furthermore, the reception unit can evaluate the quality of the photo using AI and select the optimal photo. For example, the reception unit adjusts the brightness and contrast of the photo using AI to select a photo in which the recycling object is clearly shown. Specifically, when using a smartphone camera, the user launches a dedicated app and points the camera at the recycling object to take a photo. The app uses an AI algorithm to evaluate the quality of the photo taken in real time and automatically adjusts the brightness and contrast as necessary. This allows the user to easily take high-quality photos. When using a digital camera, the reception unit takes high-resolution photos and can clearly capture the details of the recycling object. This allows the analysis unit to provide data for more accurately determining the recycling object. Furthermore, the reception unit selects the optimal one from among multiple photos taken using AI. For example, the AI ​​evaluates the brightness, contrast, and focus of the photos to select the photos in which the recyclable items are most clearly visible, providing the analysis unit with data to analyze the recyclable items with high accuracy.

[0031] The analysis unit uses the generation AI to analyze the photos taken by the reception unit and judges whether the recyclable items have been recycled appropriately. The generation AI analyzes the type and condition of the items using, for example, deep learning or neural networks. Specifically, the generation AI analyzes the shape and color of the plastic bottle and judges whether it has been properly separated. The generation AI can also analyze the condition of the paper and judge whether it has been properly recycled. The generation AI first receives the photograph as input data and identifies the type of the recyclable item using image recognition technology. For example, in the case of a plastic bottle, the generation AI analyzes the shape, color, presence or absence of a label, etc. of the bottle and judges whether it has been properly separated. In the case of paper, the generation AI analyzes the condition, color, presence or absence of dirt, etc. of the paper and judges whether it has been properly recycled. Furthermore, the generation AI evaluates the condition of the recyclable item and judges whether it is in a recyclable state. For example, if a plastic bottle is dirty, the generation AI evaluates the degree of dirt and judges whether it is recyclable. Also, if the paper is torn, the generation AI evaluates the degree of tearing and judges whether it is recyclable. This enables the analysis unit to determine with high accuracy whether the recycling object has been recycled appropriately.

[0032] The granting unit grants an incentive based on the result determined by the analysis unit. The incentive is granted in the form of, for example, points or coupons, but is not limited to such examples. For example, the granting unit grants points according to the accuracy of recycling. The granting unit can also issue coupons so that the user can use them at a specific store. Specifically, when the analysis unit determines that the recycling of the recycling object is appropriate, the granting unit grants points to the user. These points are accumulated every time the user performs a recycling activity, and when a certain number of points are accumulated, they can be exchanged for coupons or gift cards that can be used at a specific store. For example, when a user correctly recycles a plastic bottle, the granting unit grants points for the recycling activity and records them in the user's account. The user can check his or her point balance through the app and use the accumulated points to obtain coupons or gift cards. Furthermore, the granting unit can provide additional incentives by participating in a specific recycling campaign or event. For example, a campaign can be implemented in which more points than usual are granted to users who perform recycling activities during a specific period. This increases the motivation of users for recycling activities and allows them to actively engage in recycling.

[0033] The tracking unit tracks the recycling results using blockchain technology. The blockchain technology is implemented using, for example, a public blockchain or a private blockchain. For example, the tracking unit records the recycling results in the blockchain and manages them transparently and safely. The tracking unit can also enable users to check the recycling history. Specifically, each time a recycling activity is performed, the results are recorded in the blockchain. The blockchain uses distributed ledger technology to safely manage data and prevent tampering and fraud. This allows the recycling results to be managed reliably. Users can check their own recycling history through a dedicated app. For example, users can check the recycling history of plastic bottles and paper that they have recycled in the past and understand what incentives were given. Furthermore, the tracking unit can provide statistical information based on the recycling results. For example, it is possible to analyze trends in recycling activities in a specific area or period and evaluate the effectiveness of recycling. This makes it possible to find improvements and effective measures in recycling activities and use them to promote recycling.

[0034] The analysis unit can use the generation AI to analyze the type and condition of the item and determine whether it has been recycled appropriately. The generation AI can analyze the type and condition of the item using, for example, deep learning or a neural network. For example, the generation AI can analyze the shape and color of a plastic bottle to determine whether it has been properly separated. The generation AI can also analyze the condition of paper to determine whether it has been properly recycled. The generation AI can, for example, detect scratches and stains on the surface of the item and evaluate the appropriateness of recycling. The generation AI can also analyze the material of the item and propose a method suitable for the recycling process. As a result, by using the generation AI, the type and condition of the item can be accurately analyzed and the accuracy of recycling can be improved. The generation AI, for example, inputs image data of the item and outputs the type and condition of the item. The generation AI analyzes the image data of the item and extracts the characteristics of the item. For example, the generation AI analyzes the characteristics of the item, such as the shape, color, and material, to determine the type and condition of the item. The generation AI evaluates the appropriateness of recycling based on the characteristics of the item. For example, the generation AI determines whether the shape and color of the item comply with the recycling standards. Generative AI can also evaluate whether the material of an item is suitable for the recycling process, which allows for accurate analysis of the type and condition of an item and improves the accuracy of recycling.

[0035] The granting unit can grant an incentive. The incentive is granted in the form of, for example, points or coupons, but is not limited to such examples. For example, the granting unit grants points according to the accuracy of recycling. In addition, the granting unit can also issue coupons and allow the user to use them at a specific store. For example, the granting unit grants 100 points to the user based on the accuracy of recycling. In addition, the granting unit can also issue a 10% discount coupon to the user based on the accuracy of recycling. Furthermore, the granting unit can grant cash to the user based on the accuracy of recycling. For example, the granting unit grants 500 yen to the user based on the accuracy of recycling. As a result, by granting an incentive in the form of points or coupons, it is possible to promote the recycling activity of the user. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can grant an incentive using an AI model that automatically calculates an incentive based on the accuracy of recycling and grants it to the user.

[0036] The reception unit can analyze the user's past recycling history and select an appropriate shooting method. The reception unit, for example, suggests an optimal shooting method based on a recycling method that the user has been successful in the past. The reception unit can also adjust the shooting method from the user's past recycling history so as to avoid a failed method. Furthermore, the reception unit can analyze the user's recycling history and select the most efficient shooting method. For example, the reception unit suggests an optimal shooting method based on a recycling method that the user has been successful in the past. The reception unit can also adjust the shooting method from the user's past recycling history so as to avoid a failed method. Furthermore, the reception unit can analyze the user's recycling history and select the most efficient shooting method. As a result, by analyzing the past recycling history, the optimal shooting method can be selected and the success rate of recycling can be improved. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past recycling history data to the generation AI and cause the generation AI to select the optimal shooting method.

[0037] The reception unit can perform filtering based on the user's environment and situation when taking a photo. For example, when the user is outdoors, the reception unit takes an optimal photo using natural light. In addition, when the user is indoors, the reception unit can also perform filtering taking into account lighting conditions. Furthermore, when the user takes a photo at night, the reception unit can perform filtering so that the photo can be taken clearly even in a dark place. For example, the reception unit detects the brightness of the user's surroundings with a sensor and automatically adjusts the optimal exposure setting. In addition, the reception unit can perform filtering according to the weather and time of day based on the user's location information. For example, when the user takes a photo outdoors on a sunny day, the reception unit performs filtering to make the most of natural light. In addition, when the user takes a photo outdoors on a cloudy day, the reception unit can perform filtering to compensate for the lack of light. As a result, the optimal photo can be taken by performing filtering according to the user's environment and situation. Part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's environmental data to the generation AI and cause the generation AI to execute the optimal filtering setting.

[0038] The reception unit can take the user's geographical location information into consideration when taking a photograph and preferentially photograph objects that are highly relevant. For example, when the user is in a specific area, the reception unit preferentially photographs objects that are recommended to be recycled in that area. In addition, when the user is traveling, the reception unit can also preferentially photograph objects that need to be recycled at the travel destination. In addition, when the user is at home, the reception unit can also preferentially photograph objects that need to be recycled at home. For example, the reception unit acquires the user's geographical location information using GPS data and identifies highly relevant recyclable objects based on the information. In addition, the reception unit can also use the user's location information service to identify the user's current location and suggest the optimal recyclable object. In this way, by considering the user's geographical location information, it is possible to preferentially photograph highly relevant recyclable objects. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to identify highly relevant recyclable objects.

[0039] The reception unit can analyze the user's social media activity at the time of taking a photograph and photograph related objects. The reception unit photographs related objects based on, for example, recycling activities shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest objects that need to be recycled. Furthermore, the reception unit can photograph objects with reference to recycling-related accounts that the user follows on social media. For example, the reception unit can analyze the content of the user's social media posts using AI and identify objects that need to be recycled. The reception unit can also determine recycling priorities based on the number of followers and likes of the user's social media. In this way, related recycling objects can be photographed by analyzing the user's social media activities. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to identify related recycling objects.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the recycling object during the analysis. For example, the analysis unit performs a detailed analysis in the case of an important recycling object. In addition, the analysis unit can also perform a standard analysis in the case of a general recycling object. Furthermore, the analysis unit can perform a simplified analysis in the case of a recycling object with low importance. For example, the analysis unit evaluates the importance of the recycling object and adjusts the level of detail of the analysis according to the importance. In addition, the analysis unit can adjust the level of detail of the analysis based on the impact of the recycling object on the environment and the difficulty of recycling. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the recycling object. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the importance data of the recycling object to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the recyclable object during analysis. For example, in the case of a plastic product, the analysis unit applies an analysis algorithm dedicated to plastic. In addition, in the case of a paper product, the analysis unit can also apply an analysis algorithm dedicated to paper. In addition, in the case of a metal product, the analysis unit can also apply an analysis algorithm dedicated to metal. For example, the analysis unit evaluates the category of the recyclable object and selects an optimal analysis algorithm depending on the category. In addition, the analysis unit can adjust the analysis algorithm based on the material and shape of the recyclable object. Thereby, by applying different analysis algorithms depending on the category of the recyclable object, the accuracy of the analysis is improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the recyclable object to the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0042] The analysis unit can determine the priority of the analysis based on the submission time of the recyclable object during the analysis. For example, the analysis unit gives priority to the analysis of the recyclable object that has just been submitted. The analysis unit can also postpone the analysis of the recyclable object that has been submitted a long time ago. Furthermore, the analysis unit can dynamically adjust the priority of the analysis based on the submission time. For example, the analysis unit determines the priority based on the submission date and time of the recyclable object. The analysis unit can also adjust the priority of the analysis taking into account the frequency of submission of the recyclable object. In this way, efficient analysis is possible by determining the priority of the analysis based on the submission time of the recyclable object. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time data of the recyclable object to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the recyclable objects during analysis. For example, the analysis unit prioritizes analysis of recyclable objects with high relevance. The analysis unit can also postpone analysis of recyclable objects with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance. For example, the analysis unit evaluates the relevance based on the type of recyclable object and information of the submitter. The analysis unit can also evaluate the relevance taking into account the frequency of submission of the recyclable object and past analysis results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the recyclable object. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input relevance data of the recyclable object to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] The granting unit can select an optimal granting method by analyzing the user's past recycling behavior when granting an incentive. For example, the granting unit grants an optimal incentive based on the user's successful recycling behavior in the past. The granting unit can also adjust the incentive from the user's past recycling behavior so as to avoid unsuccessful behavior. Furthermore, the granting unit can analyze the user's recycling behavior and select the most effective incentive. For example, the granting unit grants an optimal incentive based on the user's successful recycling behavior in the past. The granting unit can also adjust the incentive from the user's past recycling behavior so as to avoid unsuccessful behavior. Furthermore, the granting unit can analyze the user's recycling behavior and select the most effective incentive. In this way, the optimal incentive can be provided by analyzing the user's past recycling behavior. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's past recycling behavior data to the generation AI and cause the generation AI to select the optimal incentive.

[0045] The granting unit can customize the granting means based on the user's current living situation when granting the incentive. For example, if the user is a student, the granting unit grants a student discount coupon. In addition, if the user is a working person, the granting unit can grant points that are useful for work. Furthermore, if the user is an elderly person, the granting unit can grant a health-related coupon. For example, the granting unit acquires the user's living situation using a questionnaire or a database, and selects an optimal incentive based on the information. In addition, the granting unit can customize the incentive taking into account the user's income and family structure. As a result, a more effective incentive can be provided by customizing the incentive according to the user's living situation. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's living situation data into the generation AI and cause the generation AI to select the optimal incentive.

[0046] The granting unit can select the optimal granting method in consideration of the geographical location information of the user when granting the incentive. For example, when the user is in an urban area, the granting unit grants a coupon that can be used in the urban area. Also, when the user is in a rural area, the granting unit can grant points that can be used in the rural area. Furthermore, the granting unit can select the optimal incentive based on the geographical location information of the user. For example, the granting unit acquires the geographical location information of the user using GPS data, and selects the optimal incentive based on the information. Also, the granting unit can identify the current location of the user using a user's location information service, and propose the optimal incentive. In this way, the optimal incentive can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the geographical location information data of the user to the generation AI, and cause the generation AI to select the optimal incentive.

[0047] The granting unit can analyze the user's social media activity and suggest a means of granting the incentive when granting the incentive. The granting unit grants a related coupon based on, for example, the recycling activity shared by the user on social media. The granting unit can also analyze the content of the user's social media posts and suggest an optimal incentive. Furthermore, the granting unit can grant the incentive with reference to recycling-related accounts that the user follows on social media. For example, the granting unit analyzes the content of the user's social media posts using AI and suggests an optimal incentive. The granting unit can also determine the priority of the incentive based on the number of followers and likes of the user's social media. In this way, the optimal incentive can be provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the granting unit may be performed using, for example, AI, or may be performed without using AI. For example, the granting unit can input the user's social media data to the generation AI and cause the generation AI to execute the proposal of the optimal incentive.

[0048] The tracking unit can optimize the tracking algorithm by referring to past tracking data during tracking. The tracking unit, for example, selects an optimal tracking algorithm based on past tracking data. The tracking unit can also apply an algorithm that improves tracking accuracy from past tracking data. The tracking unit can also analyze past tracking data and dynamically optimize the tracking algorithm. For example, the tracking unit selects an optimal tracking algorithm based on past recycling history and tracking results. The tracking unit can also apply an algorithm that improves tracking accuracy based on past tracking data. As a result, the tracking algorithm can be optimized and accuracy can be improved by referring to past tracking data. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input past tracking data to the generation AI and cause the generation AI to optimize the tracking algorithm.

[0049] The tracking unit can apply different tracking methods for each category of the recyclable object during tracking. For example, in the case of a plastic product, the tracking unit applies a tracking method dedicated to plastic. In addition, in the case of a paper product, the tracking unit can also apply a tracking method dedicated to paper. In addition, in the case of a metal product, the tracking unit can also apply a tracking method dedicated to metal. For example, the tracking unit evaluates the category of the recyclable object and selects an optimal tracking method according to the category. In addition, the tracking unit can adjust the tracking method based on the material and shape of the recyclable object. In this way, the accuracy of tracking is improved by applying different tracking methods according to the category of the recyclable object. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input category data of the recyclable object to the generation AI and cause the generation AI to select the optimal tracking method.

[0050] The tracking unit can weight the tracking data based on the submission time of the recyclable object during tracking. For example, the tracking unit can weight the tracking data of a recyclable object that has just been submitted highly. The tracking unit can also weight the tracking data of a recyclable object that has been submitted a long time ago less. Furthermore, the tracking unit can dynamically adjust the weighting of the tracking data based on the submission time. For example, the tracking unit determines the weighting based on the submission date and time of the recyclable object. The tracking unit can also adjust the weighting taking into account the frequency of submission of the recyclable object. This allows efficient tracking by weighting the tracking data based on the submission time of the recyclable object. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the submission time data of the recyclable object to the generation AI and cause the generation AI to execute the weighting of the tracking data.

[0051] The tracking unit can perform tracking by referring to market data related to the recyclable object during tracking. The tracking unit determines a tracking priority based on, for example, the market value of the recyclable object. The tracking unit can also optimize tracking of the recyclable object by referring to the market data. Furthermore, the tracking unit can adjust a tracking method for the recyclable object based on the market data. For example, the tracking unit determines a tracking priority based on the market price and demand forecast data of the recyclable object. The tracking unit can also adjust a tracking method for the recyclable object based on the market data. Thereby, the accuracy of tracking is improved by referring to the market data related to the recyclable object. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input market data of the recyclable object to the generation AI and cause the generation AI to adjust the tracking method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can analyze the frequency of the user's recycling activities and suggest the optimal timing for taking a photo. For example, if the user frequently engages in recycling activities, the reception unit can send regular reminders to encourage the user to take a photo. In addition, if the user does not engage in recycling activities very often, the reception unit can also suggest taking a photo at a specific time period. Furthermore, the reception unit can select the most effective timing for taking a photo based on the user's recycling activity history. This can improve the success rate of recycling by suggesting the optimal timing for taking a photo depending on the frequency of the user's recycling activities.

[0054] The analysis unit can evaluate the environmental impact of the items to be recycled and determine the priority of the analysis based on the evaluation results. For example, the analysis unit can evaluate the environmental impact of plastic products and prioritize the analysis. The analysis unit can also evaluate the environmental impact of recycling paper products and perform appropriate analysis. Furthermore, the analysis unit can evaluate the environmental impact of recycling metal products and adjust the priority of the analysis. This allows for efficient analysis by taking into account the environmental impact of items to be recycled.

[0055] The granting unit can evaluate the social impact of the user's recycling activity and grant an incentive based on the evaluation result. For example, the granting unit grants a special incentive if the user contributes to the local community through the recycling activity. The granting unit can also grant additional points if the user contributes to environmental protection through the recycling activity. Furthermore, the granting unit can evaluate the impact of the user's recycling activity on other users and adjust the incentive based on the impact. In this way, more effective incentives can be provided by evaluating the social impact of the user's recycling activity.

[0056] The reception unit can analyze the user's past recycling history and suggest an optimal shooting environment. For example, the optimal shooting environment can be suggested based on the recycling method that the user has been successful in the past. Also, the shooting environment can be adjusted based on the user's past recycling history so as to avoid unsuccessful methods. Furthermore, the user's recycling history can be analyzed to select the most efficient shooting environment. In this way, the optimal shooting environment can be selected by analyzing the past recycling history, and the success rate of recycling can be improved.

[0057] The reception unit can automatically switch the shooting mode based on the user's environment and situation when taking a photo. For example, when the user is outdoors, the shooting mode can be switched to one that uses natural light. Also, when the user is indoors, the shooting mode can be switched to one that takes into account the lighting conditions. Furthermore, when the user takes a photo at night, the mode can be switched to one that allows clear shooting even in dark places. This allows more appropriate photos to be taken by automatically selecting the optimal shooting mode according to the user's environment and situation.

[0058] When taking a photograph, the reception unit can suggest the optimal shooting location by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, locations where recycling is recommended in that area can be suggested. Also, when the user is traveling, locations where recycling is necessary at the travel destination can be suggested. Furthermore, when the user is at home, locations where recycling is necessary within the home can be suggested. In this way, by taking into consideration the geographical location information of the user, the optimal shooting location can be suggested and recycling activities can be promoted.

[0059] When a photograph is taken, the reception unit can analyze the user's social media activity and suggest related recyclable items. For example, related recyclable items can be suggested based on recycling activities shared by the user on social media. The reception unit can also analyze the content of the user's social media posts to suggest items that need to be recycled. Furthermore, recyclable items can be suggested based on recycling-related accounts that the user follows on social media. In this way, by analyzing the user's social media activity, related recyclable items can be suggested and recycling activities can be promoted.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit takes a photo of the item to be recycled. Examples of the item to be recycled include, but are not limited to, plastic bottles, paper, and metal. The reception unit takes a photo of the item to be recycled using, for example, a smartphone camera. The reception unit can also take high-resolution photos using a digital camera. Furthermore, the reception unit can use AI to evaluate the quality of the photo and select the most suitable photo. For example, the reception unit uses AI to adjust the brightness and contrast of the photo and select a photo in which the item to be recycled is clearly visible. Step 2: The analysis unit uses the generation AI to analyze the photos taken by the reception unit and determine whether the items to be recycled have been recycled appropriately. The generation AI analyzes the type and condition of the items, for example, using deep learning or neural networks. For example, the generation AI analyzes the shape and color of plastic bottles to determine whether they have been properly sorted. The generation AI can also analyze the condition of paper to determine whether it has been properly recycled. Step 3: The granting unit grants an incentive based on the result determined by the analysis unit. The incentive is granted in the form of, for example, points or coupons, but is not limited to such examples. The granting unit grants points according to the accuracy of recycling, for example. The granting unit can also issue coupons so that the user can use them at specific stores. Step 4: The tracking unit tracks the recycling results using blockchain technology. The blockchain technology is implemented using, for example, a public blockchain or a private blockchain. For example, the tracking unit records the recycling results in the blockchain and manages them transparently and securely. The tracking unit can also enable users to check the recycling history.

[0062] (Example 2) The recycling support system according to the embodiment of the present invention is a system that takes a photo of an object to be recycled, analyzes the photo by a generation AI, judges the accuracy of the recycling, grants incentives, and tracks the recycling performance using blockchain technology. In this recycling support system, a user takes a photo of an object to be recycled, and a generation AI analyzes the photo to judge the type and condition of the item. Based on the analysis result, an incentive is granted in the form of points or coupons. Furthermore, the recycling performance is tracked using blockchain technology and managed transparently and safely. For example, the system judges whether plastic bottles are correctly separated or paper is appropriately recycled, and grants incentives to users to promote recycling activities. As a result, the recycling support system allows users to obtain concrete benefits through recycling activities, and motivation for recycling is improved. In addition, by using blockchain technology, the recycling performance can be managed reliably, contributing to environmental protection.

[0063] The recycling support system according to the embodiment includes a reception unit, an analysis unit, an assignment unit, and a tracking unit. The reception unit takes a photo of the recycling object. The recycling object includes, for example, plastic bottles, paper, metal, and the like, but is not limited to such examples. The reception unit takes a photo of the recycling object using, for example, a smartphone camera. The reception unit can also take high-resolution photos using a digital camera. Furthermore, the reception unit can evaluate the quality of the photo using AI and select the optimal photo. For example, the reception unit adjusts the brightness and contrast of the photo using AI to select a photo in which the recycling object is clearly shown. The analysis unit uses the generation AI to analyze the photo taken by the reception unit and determine whether the recycling object has been properly recycled. The generation AI analyzes the type and condition of the item using, for example, deep learning or a neural network. For example, the generation AI analyzes the shape and color of the plastic bottle and determines whether it has been properly separated. The generation AI can also analyze the condition of the paper and determine whether it has been properly recycled. The assignment unit assigns an incentive based on the result determined by the analysis unit. The incentive is given in the form of, for example, points or coupons, but is not limited to such examples. For example, the giving unit gives points according to the accuracy of recycling. The giving unit can also issue coupons and allow the user to use them at specific stores. The tracking unit tracks the recycling results using blockchain technology. The blockchain technology is implemented using, for example, a public blockchain or a private blockchain. For example, the tracking unit records the recycling results in the blockchain and manages them transparently and safely. The tracking unit can also enable the user to check the recycling history. As a result, the recycling support system according to the embodiment allows the user to obtain concrete benefits through recycling activities, and improves motivation for recycling. Furthermore, by using blockchain technology, the recycling results can be managed reliably, which can contribute to environmental protection.

[0064] The reception unit takes a photo of the recycling object. Examples of the recycling object include, but are not limited to, plastic bottles, paper, and metal. The reception unit takes a photo of the recycling object using, for example, a smartphone camera. The reception unit can also take high-resolution photos using a digital camera. Furthermore, the reception unit can evaluate the quality of the photo using AI and select the optimal photo. For example, the reception unit adjusts the brightness and contrast of the photo using AI to select a photo in which the recycling object is clearly shown. Specifically, when using a smartphone camera, the user launches a dedicated app and points the camera at the recycling object to take a photo. The app uses an AI algorithm to evaluate the quality of the photo taken in real time and automatically adjusts the brightness and contrast as necessary. This allows the user to easily take high-quality photos. When using a digital camera, the reception unit takes high-resolution photos and can clearly capture the details of the recycling object. This allows the analysis unit to provide data for more accurately determining the recycling object. Furthermore, the reception unit selects the optimal one from among multiple photos taken using AI. For example, the AI ​​evaluates the brightness, contrast, and focus of the photos to select the photos in which the recyclable items are most clearly visible, providing the analysis unit with data to analyze the recyclable items with high accuracy.

[0065] The analysis unit uses the generation AI to analyze the photos taken by the reception unit and judges whether the recyclable items have been recycled appropriately. The generation AI analyzes the type and condition of the items using, for example, deep learning or neural networks. Specifically, the generation AI analyzes the shape and color of the plastic bottle and judges whether it has been properly separated. The generation AI can also analyze the condition of the paper and judge whether it has been properly recycled. The generation AI first receives the photograph as input data and identifies the type of the recyclable item using image recognition technology. For example, in the case of a plastic bottle, the generation AI analyzes the shape, color, presence or absence of a label, etc. of the bottle and judges whether it has been properly separated. In the case of paper, the generation AI analyzes the condition, color, presence or absence of dirt, etc. of the paper and judges whether it has been properly recycled. Furthermore, the generation AI evaluates the condition of the recyclable item and judges whether it is in a recyclable state. For example, if a plastic bottle is dirty, the generation AI evaluates the degree of dirt and judges whether it is recyclable. Also, if the paper is torn, the generation AI evaluates the degree of tearing and judges whether it is recyclable. This enables the analysis unit to determine with high accuracy whether the recycling object has been recycled appropriately.

[0066] The granting unit grants an incentive based on the result determined by the analysis unit. The incentive is granted in the form of, for example, points or coupons, but is not limited to such examples. For example, the granting unit grants points according to the accuracy of recycling. The granting unit can also issue coupons so that the user can use them at a specific store. Specifically, when the analysis unit determines that the recycling of the recycling object is appropriate, the granting unit grants points to the user. These points are accumulated every time the user performs a recycling activity, and when a certain number of points are accumulated, they can be exchanged for coupons or gift cards that can be used at a specific store. For example, when a user correctly recycles a plastic bottle, the granting unit grants points for the recycling activity and records them in the user's account. The user can check his or her point balance through the app and use the accumulated points to obtain coupons or gift cards. Furthermore, the granting unit can provide additional incentives by participating in a specific recycling campaign or event. For example, a campaign can be implemented in which more points than usual are granted to users who perform recycling activities during a specific period. This increases the motivation of users for recycling activities and allows them to actively engage in recycling.

[0067] The tracking unit tracks the recycling results using blockchain technology. The blockchain technology is implemented using, for example, a public blockchain or a private blockchain. For example, the tracking unit records the recycling results in the blockchain and manages them transparently and safely. The tracking unit can also enable users to check the recycling history. Specifically, each time a recycling activity is performed, the results are recorded in the blockchain. The blockchain uses distributed ledger technology to safely manage data and prevent tampering and fraud. This allows the recycling results to be managed reliably. Users can check their own recycling history through a dedicated app. For example, users can check the recycling history of plastic bottles and paper that they have recycled in the past and understand what incentives were given. Furthermore, the tracking unit can provide statistical information based on the recycling results. For example, it is possible to analyze trends in recycling activities in a specific area or period and evaluate the effectiveness of recycling. This makes it possible to find improvements and effective measures in recycling activities and use them to promote recycling.

[0068] The analysis unit can use the generation AI to analyze the type and condition of the item and determine whether it has been recycled appropriately. The generation AI can analyze the type and condition of the item using, for example, deep learning or a neural network. For example, the generation AI can analyze the shape and color of a plastic bottle to determine whether it has been properly separated. The generation AI can also analyze the condition of paper to determine whether it has been properly recycled. The generation AI can, for example, detect scratches and stains on the surface of the item and evaluate the appropriateness of recycling. The generation AI can also analyze the material of the item and propose a method suitable for the recycling process. As a result, by using the generation AI, the type and condition of the item can be accurately analyzed and the accuracy of recycling can be improved. The generation AI, for example, inputs image data of the item and outputs the type and condition of the item. The generation AI analyzes the image data of the item and extracts the characteristics of the item. For example, the generation AI analyzes the characteristics of the item, such as the shape, color, and material, to determine the type and condition of the item. The generation AI evaluates the appropriateness of recycling based on the characteristics of the item. For example, the generation AI determines whether the shape and color of the item comply with the recycling standards. Generative AI can also evaluate whether the material of an item is suitable for the recycling process, which allows for accurate analysis of the type and condition of an item and improves the accuracy of recycling.

[0069] The granting unit can grant an incentive. The incentive is granted in the form of, for example, points or coupons, but is not limited to such examples. For example, the granting unit grants points according to the accuracy of recycling. In addition, the granting unit can also issue coupons and allow the user to use them at a specific store. For example, the granting unit grants 100 points to the user based on the accuracy of recycling. In addition, the granting unit can also issue a 10% discount coupon to the user based on the accuracy of recycling. Furthermore, the granting unit can grant cash to the user based on the accuracy of recycling. For example, the granting unit grants 500 yen to the user based on the accuracy of recycling. As a result, by granting an incentive in the form of points or coupons, it is possible to promote the recycling activity of the user. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can grant an incentive using an AI model that automatically calculates an incentive based on the accuracy of recycling and grants it to the user.

[0070] The reception unit can estimate the user's emotion and adjust the timing of taking a photo based on the estimated user's emotion. For example, when the user is relaxed, the reception unit leaves the timing of taking a photo up to the user. In addition, when the user is in a hurry, the reception unit can also allow the AI ​​to automatically take a photo at the optimal timing. Furthermore, when the user is stressed, the reception unit can enable the user to take a photo with a simple operation. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on a change in facial expression. In addition, the reception unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. In addition, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate. As a result, a more appropriate photo can be taken by adjusting the timing of taking a photo according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI or may be performed without using an AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0071] The reception unit can analyze the user's past recycling history and select an appropriate shooting method. The reception unit, for example, suggests an optimal shooting method based on a recycling method that the user has been successful in the past. The reception unit can also adjust the shooting method from the user's past recycling history so as to avoid a failed method. Furthermore, the reception unit can analyze the user's recycling history and select the most efficient shooting method. For example, the reception unit suggests an optimal shooting method based on a recycling method that the user has been successful in the past. The reception unit can also adjust the shooting method from the user's past recycling history so as to avoid a failed method. Furthermore, the reception unit can analyze the user's recycling history and select the most efficient shooting method. As a result, by analyzing the past recycling history, the optimal shooting method can be selected and the success rate of recycling can be improved. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past recycling history data to the generation AI and cause the generation AI to select the optimal shooting method.

[0072] The reception unit can perform filtering based on the user's environment and situation when taking a photo. For example, when the user is outdoors, the reception unit takes an optimal photo using natural light. In addition, when the user is indoors, the reception unit can also perform filtering taking into account lighting conditions. Furthermore, when the user takes a photo at night, the reception unit can perform filtering so that the photo can be taken clearly even in a dark place. For example, the reception unit detects the brightness of the user's surroundings with a sensor and automatically adjusts the optimal exposure setting. In addition, the reception unit can perform filtering according to the weather and time of day based on the user's location information. For example, when the user takes a photo outdoors on a sunny day, the reception unit performs filtering to make the most of natural light. In addition, when the user takes a photo outdoors on a cloudy day, the reception unit can perform filtering to compensate for the lack of light. As a result, the optimal photo can be taken by performing filtering according to the user's environment and situation. Part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's environmental data to the generation AI and cause the generation AI to execute the optimal filtering setting.

[0073] The reception unit can estimate the user's emotion and determine the priority order of the objects to be photographed based on the estimated emotion of the user. For example, when the user is excited, the reception unit prioritizes photographing important recyclable objects. Furthermore, when the user is relaxed, the reception unit can also photograph the recyclable objects in order. Furthermore, when the user is in a hurry, the reception unit can prioritize photographing the most valuable recyclable object. For example, the reception unit photographs the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. Furthermore, the reception unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate. In this way, important recyclable objects can be photographed preferentially by determining the priority order of the objects to be photographed according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI or may be performed without using an AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] The reception unit can take the user's geographical location information into consideration when taking a photograph and preferentially photograph objects that are highly relevant. For example, when the user is in a specific area, the reception unit preferentially photographs objects that are recommended to be recycled in that area. In addition, when the user is traveling, the reception unit can also preferentially photograph objects that need to be recycled at the travel destination. In addition, when the user is at home, the reception unit can also preferentially photograph objects that need to be recycled at home. For example, the reception unit acquires the user's geographical location information using GPS data and identifies highly relevant recyclable objects based on the information. In addition, the reception unit can also use the user's location information service to identify the user's current location and suggest the optimal recyclable object. In this way, by considering the user's geographical location information, it is possible to preferentially photograph highly relevant recyclable objects. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to identify highly relevant recyclable objects.

[0075] The reception unit can analyze the user's social media activity at the time of taking a photograph and photograph related objects. The reception unit photographs related objects based on, for example, recycling activities shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest objects that need to be recycled. Furthermore, the reception unit can photograph objects with reference to recycling-related accounts that the user follows on social media. For example, the reception unit can analyze the content of the user's social media posts using AI and identify objects that need to be recycled. The reception unit can also determine recycling priorities based on the number of followers and likes of the user's social media. In this way, related recycling objects can be photographed by analyzing the user's social media activities. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to identify related recycling objects.

[0076] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit displays a detailed analysis result. In addition, when the user is in a hurry, the analysis unit can display a concise analysis result. In addition, when the user is excited, the analysis unit can display a visually attractive analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression. In addition, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. In addition, the analysis unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. As a result, by adjusting the expression method of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the recycling object during the analysis. For example, the analysis unit performs a detailed analysis in the case of an important recycling object. In addition, the analysis unit can also perform a standard analysis in the case of a general recycling object. Furthermore, the analysis unit can perform a simplified analysis in the case of a recycling object with low importance. For example, the analysis unit evaluates the importance of the recycling object and adjusts the level of detail of the analysis according to the importance. In addition, the analysis unit can adjust the level of detail of the analysis based on the impact of the recycling object on the environment and the difficulty of recycling. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the recycling object. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the importance data of the recycling object to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the recyclable object during analysis. For example, in the case of a plastic product, the analysis unit applies an analysis algorithm dedicated to plastic. In addition, in the case of a paper product, the analysis unit can also apply an analysis algorithm dedicated to paper. In addition, in the case of a metal product, the analysis unit can also apply an analysis algorithm dedicated to metal. For example, the analysis unit evaluates the category of the recyclable object and selects an optimal analysis algorithm depending on the category. In addition, the analysis unit can adjust the analysis algorithm based on the material and shape of the recyclable object. Thereby, by applying different analysis algorithms depending on the category of the recyclable object, the accuracy of the analysis is improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the recyclable object to the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0079] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit displays a short and to the point analysis result. Furthermore, when the user is relaxed, the analysis unit can display a detailed analysis result. Furthermore, when the user is excited, the analysis unit can display a visually appealing analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression. Furthermore, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the analysis unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. In this way, the length of the analysis can be adjusted according to the user's emotion, thereby providing a more appropriate analysis result. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] The analysis unit can determine the priority of the analysis based on the submission time of the recyclable object during the analysis. For example, the analysis unit gives priority to the analysis of the recyclable object that has just been submitted. The analysis unit can also postpone the analysis of the recyclable object that has been submitted a long time ago. Furthermore, the analysis unit can dynamically adjust the priority of the analysis based on the submission time. For example, the analysis unit determines the priority based on the submission date and time of the recyclable object. The analysis unit can also adjust the priority of the analysis taking into account the frequency of submission of the recyclable object. In this way, efficient analysis is possible by determining the priority of the analysis based on the submission time of the recyclable object. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time data of the recyclable object to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the recyclable objects during analysis. For example, the analysis unit prioritizes analysis of recyclable objects with high relevance. The analysis unit can also postpone analysis of recyclable objects with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance. For example, the analysis unit evaluates the relevance based on the type of recyclable object and information of the submitter. The analysis unit can also evaluate the relevance taking into account the frequency of submission of the recyclable object and past analysis results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the recyclable object. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input relevance data of the recyclable object to the generation AI and cause the generation AI to adjust the order of analysis.

[0082] The granting unit can estimate the user's emotion and adjust the method of granting the incentive based on the estimated user's emotion. For example, when the user is relaxed, the granting unit grants points in stages. In addition, when the user is in a hurry, the granting unit can grant points all at once. Furthermore, when the user is excited, the granting unit can grant a special coupon. For example, the granting unit takes a picture of the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the granting unit calculates an emotion score based on the change in the facial expression. In addition, the granting unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the granting unit analyzes the tone and speed of the voice and calculates the emotion score. In addition, the granting unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the granting unit calculates an emotion score based on the fluctuation of the heart rate. As a result, by adjusting the method of granting the incentive according to the user's emotion, it is possible to provide a more effective incentive. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 attachment unit may be performed, for example, using AI, or may be performed without using AI. For example, the attachment unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0083] The granting unit can select an optimal granting method by analyzing the user's past recycling behavior when granting an incentive. For example, the granting unit grants an optimal incentive based on the user's successful recycling behavior in the past. The granting unit can also adjust the incentive from the user's past recycling behavior so as to avoid unsuccessful behavior. Furthermore, the granting unit can analyze the user's recycling behavior and select the most effective incentive. For example, the granting unit grants an optimal incentive based on the user's successful recycling behavior in the past. The granting unit can also adjust the incentive from the user's past recycling behavior so as to avoid unsuccessful behavior. Furthermore, the granting unit can analyze the user's recycling behavior and select the most effective incentive. In this way, the optimal incentive can be provided by analyzing the user's past recycling behavior. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's past recycling behavior data to the generation AI and cause the generation AI to select the optimal incentive.

[0084] The granting unit can customize the granting means based on the user's current living situation when granting the incentive. For example, if the user is a student, the granting unit grants a student discount coupon. In addition, if the user is a working person, the granting unit can grant points that are useful for work. Furthermore, if the user is an elderly person, the granting unit can grant a health-related coupon. For example, the granting unit acquires the user's living situation using a questionnaire or a database, and selects an optimal incentive based on the information. In addition, the granting unit can customize the incentive taking into account the user's income and family structure. As a result, a more effective incentive can be provided by customizing the incentive according to the user's living situation. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's living situation data into the generation AI and cause the generation AI to select the optimal incentive.

[0085] The granting unit can estimate the user's emotion and determine the priority of incentives based on the estimated user's emotion. For example, when the user is relaxed, the granting unit grants points in stages. In addition, when the user is in a hurry, the granting unit can grant points all at once. Furthermore, when the user is excited, the granting unit can grant a special coupon. For example, the granting unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the granting unit calculates an emotion score based on the change in facial expression. In addition, the granting unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the granting unit analyzes the tone and speed of the voice and calculates the emotion score. In addition, the granting unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the granting unit calculates an emotion score based on the fluctuation of the heart rate. As a result, by determining the priority of incentives according to the user's emotion, more effective incentives can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 attachment unit may be performed, for example, using AI, or may be performed without using AI. For example, the attachment unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0086] The granting unit can select the optimal granting method in consideration of the geographical location information of the user when granting the incentive. For example, when the user is in an urban area, the granting unit grants a coupon that can be used in the urban area. Also, when the user is in a rural area, the granting unit can grant points that can be used in the rural area. Furthermore, the granting unit can select the optimal incentive based on the geographical location information of the user. For example, the granting unit acquires the geographical location information of the user using GPS data, and selects the optimal incentive based on the information. Also, the granting unit can identify the current location of the user using a user's location information service, and propose the optimal incentive. In this way, the optimal incentive can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the geographical location information data of the user to the generation AI, and cause the generation AI to select the optimal incentive.

[0087] The granting unit can analyze the user's social media activity and suggest a means of granting the incentive when granting the incentive. The granting unit grants a related coupon based on, for example, the recycling activity shared by the user on social media. The granting unit can also analyze the content of the user's social media posts and suggest an optimal incentive. Furthermore, the granting unit can grant the incentive with reference to recycling-related accounts that the user follows on social media. For example, the granting unit analyzes the content of the user's social media posts using AI and suggests an optimal incentive. The granting unit can also determine the priority of the incentive based on the number of followers and likes of the user's social media. In this way, the optimal incentive can be provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the granting unit may be performed using, for example, AI, or may be performed without using AI. For example, the granting unit can input the user's social media data to the generation AI and cause the generation AI to execute the proposal of the optimal incentive.

[0088] The tracking unit can estimate the user's emotion and select tracking data based on the estimated user's emotion. For example, when the user is relaxed, the tracking unit selects detailed tracking data. Also, when the user is in a hurry, the tracking unit can select concise tracking data. Furthermore, when the user is excited, the tracking unit can select visually attractive tracking data. For example, the tracking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the tracking unit calculates an emotion score based on the change in facial expression. Also, the tracking unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the tracking unit analyzes the tone and speed of the voice and calculates the emotion score. Also, the tracking unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the tracking unit calculates an emotion score based on the fluctuation of the heart rate. As a result, more appropriate data can be provided by selecting tracking data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0089] The tracking unit can optimize the tracking algorithm by referring to past tracking data during tracking. The tracking unit, for example, selects an optimal tracking algorithm based on past tracking data. The tracking unit can also apply an algorithm that improves tracking accuracy from past tracking data. The tracking unit can also analyze past tracking data and dynamically optimize the tracking algorithm. For example, the tracking unit selects an optimal tracking algorithm based on past recycling history and tracking results. The tracking unit can also apply an algorithm that improves tracking accuracy based on past tracking data. As a result, the tracking algorithm can be optimized and accuracy can be improved by referring to past tracking data. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input past tracking data to the generation AI and cause the generation AI to optimize the tracking algorithm.

[0090] The tracking unit can apply different tracking methods for each category of the recyclable object during tracking. For example, in the case of a plastic product, the tracking unit applies a tracking method dedicated to plastic. In addition, in the case of a paper product, the tracking unit can also apply a tracking method dedicated to paper. In addition, in the case of a metal product, the tracking unit can also apply a tracking method dedicated to metal. For example, the tracking unit evaluates the category of the recyclable object and selects an optimal tracking method according to the category. In addition, the tracking unit can adjust the tracking method based on the material and shape of the recyclable object. In this way, the accuracy of tracking is improved by applying different tracking methods according to the category of the recyclable object. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input category data of the recyclable object to the generation AI and cause the generation AI to select the optimal tracking method.

[0091] The tracking unit can estimate the user's emotion and adjust the tracking frequency based on the estimated user's emotion. For example, the tracking unit can lower the tracking frequency when the user is relaxed. Also, the tracking unit can increase the tracking frequency when the user is in a hurry. Furthermore, the tracking unit can dynamically adjust the tracking frequency when the user is excited. For example, the tracking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the tracking unit calculates an emotion score based on the change in facial expression. Also, the tracking unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the tracking unit analyzes the tone and speed of the voice and calculates the emotion score. Also, the tracking unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the tracking unit calculates an emotion score based on the fluctuation of the heart rate. As a result, more appropriate tracking is possible by adjusting the tracking frequency according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0092] The tracking unit can weight the tracking data based on the submission time of the recyclable object during tracking. For example, the tracking unit can weight the tracking data of a recyclable object that has just been submitted highly. The tracking unit can also weight the tracking data of a recyclable object that has been submitted a long time ago less. Furthermore, the tracking unit can dynamically adjust the weighting of the tracking data based on the submission time. For example, the tracking unit determines the weighting based on the submission date and time of the recyclable object. The tracking unit can also adjust the weighting in consideration of the frequency of submission of the recyclable object. This enables efficient tracking by weighting the tracking data based on the submission time of the recyclable object. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the submission time data of the recyclable object to the generation AI and cause the generation AI to execute the weighting of the tracking data.

[0093] The tracking unit can perform tracking by referring to market data related to the recyclable object during tracking. The tracking unit determines a tracking priority based on, for example, the market value of the recyclable object. The tracking unit can also optimize tracking of the recyclable object by referring to the market data. Furthermore, the tracking unit can adjust a tracking method for the recyclable object based on the market data. For example, the tracking unit determines a tracking priority based on the market price and demand forecast data of the recyclable object. The tracking unit can also adjust a tracking method for the recyclable object based on the market data. Thereby, the accuracy of tracking is improved by referring to the market data related to the recyclable object. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input market data of the recyclable object to the generation AI and cause the generation AI to adjust the tracking method.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The reception unit can analyze the frequency of the user's recycling activities and suggest the optimal timing for taking a photo. For example, if the user frequently engages in recycling activities, the reception unit can send regular reminders to encourage the user to take a photo. In addition, if the user does not engage in recycling activities very often, the reception unit can also suggest taking a photo at a specific time period. Furthermore, the reception unit can select the most effective timing for taking a photo based on the user's recycling activity history. This can improve the success rate of recycling by suggesting the optimal timing for taking a photo depending on the frequency of the user's recycling activities.

[0096] The analysis unit can evaluate the environmental impact of the items to be recycled and determine the priority of the analysis based on the evaluation results. For example, the analysis unit can evaluate the environmental impact of plastic products and prioritize the analysis. The analysis unit can also evaluate the environmental impact of recycling paper products and perform appropriate analysis. Furthermore, the analysis unit can evaluate the environmental impact of recycling metal products and adjust the priority of the analysis. This allows for efficient analysis by taking into account the environmental impact of items to be recycled.

[0097] The granting unit can evaluate the social impact of the user's recycling activity and grant an incentive based on the evaluation result. For example, the granting unit grants a special incentive if the user contributes to the local community through the recycling activity. The granting unit can also grant additional points if the user contributes to environmental protection through the recycling activity. Furthermore, the granting unit can evaluate the impact of the user's recycling activity on other users and adjust the incentive based on the impact. In this way, more effective incentives can be provided by evaluating the social impact of the user's recycling activity.

[0098] The reception unit can estimate the user's emotions and customize the method of photographing the recyclable object based on the estimated user's emotions. For example, if the user is relaxed, detailed guides can be displayed to support the user in taking pictures. If the user is in a hurry, concise guides can be displayed to allow the user to take pictures quickly. Furthermore, if the user is excited, visually appealing guides can be displayed to encourage the user to take pictures. This allows the user to customize the method of photographing according to the user's emotions, enabling the user to take pictures of the recyclable object more appropriately.

[0099] The reception unit can analyze the user's past recycling history and suggest an optimal shooting environment. For example, the optimal shooting environment can be suggested based on the recycling method that the user has been successful in the past. Also, the shooting environment can be adjusted based on the user's past recycling history so as to avoid unsuccessful methods. Furthermore, the user's recycling history can be analyzed to select the most efficient shooting environment. In this way, the optimal shooting environment can be selected by analyzing the past recycling history, and the success rate of recycling can be improved.

[0100] The reception unit can automatically switch the shooting mode based on the user's environment and situation when taking a photo. For example, when the user is outdoors, the shooting mode can be switched to one that uses natural light. Also, when the user is indoors, the shooting mode can be switched to one that takes into account the lighting conditions. Furthermore, when the user takes a photo at night, the mode can be switched to one that allows clear shooting even in dark places. This allows more appropriate photos to be taken by automatically selecting the optimal shooting mode according to the user's environment and situation.

[0101] The reception unit can estimate the user's emotion and adjust the order of photographing the recyclable objects based on the estimated emotion of the user. For example, when the user is relaxed, the recyclable objects are photographed in order. When the user is in a hurry, important recyclable objects can be photographed with priority. Furthermore, when the user is excited, visually attractive recyclable objects can be photographed with priority. Thus, important recyclable objects can be photographed with priority by adjusting the photographing order according to the user's emotion.

[0102] When taking a photograph, the reception unit can suggest the optimal shooting location by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, locations where recycling is recommended in that area can be suggested. Also, when the user is traveling, locations where recycling is necessary at the travel destination can be suggested. Furthermore, when the user is at home, locations where recycling is necessary within the home can be suggested. In this way, by taking into consideration the geographical location information of the user, the optimal shooting location can be suggested and recycling activities can be promoted.

[0103] When a photograph is taken, the reception unit can analyze the user's social media activity and suggest related recyclable items. For example, related recyclable items can be suggested based on recycling activities shared by the user on social media. The reception unit can also analyze the content of the user's social media posts to suggest items that need to be recycled. Furthermore, recyclable items can be suggested based on recycling-related accounts that the user follows on social media. In this way, by analyzing the user's social media activity, related recyclable items can be suggested and recycling activities can be promoted.

[0104] The analysis unit can estimate the user's emotions and customize the display method of the analysis results based on the estimated user's emotions. For example, when the user is relaxed, detailed analysis results can be displayed. When the user is in a hurry, brief analysis results can be displayed. Furthermore, when the user is excited, visually appealing analysis results can be displayed. In this way, by customizing the display method of the analysis results according to the user's emotions, more appropriate analysis results can be provided.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The reception unit takes a photo of the item to be recycled. Examples of the item to be recycled include, but are not limited to, plastic bottles, paper, and metal. The reception unit takes a photo of the item to be recycled using, for example, a smartphone camera. The reception unit can also take high-resolution photos using a digital camera. Furthermore, the reception unit can use AI to evaluate the quality of the photo and select the most suitable photo. For example, the reception unit uses AI to adjust the brightness and contrast of the photo and select a photo in which the item to be recycled is clearly visible. Step 2: The analysis unit uses the generation AI to analyze the photos taken by the reception unit and determine whether the items to be recycled have been recycled appropriately. The generation AI analyzes the type and condition of the items, for example, using deep learning or neural networks. For example, the generation AI analyzes the shape and color of plastic bottles to determine whether they have been properly sorted. The generation AI can also analyze the condition of paper to determine whether it has been properly recycled. Step 3: The granting unit grants an incentive based on the result determined by the analysis unit. The incentive is granted in the form of, for example, points or coupons, but is not limited to such examples. The granting unit grants points according to the accuracy of recycling, for example. The granting unit can also issue coupons so that the user can use them at specific stores. Step 4: The tracking unit tracks the recycling results using blockchain technology. The blockchain technology is implemented using, for example, a public blockchain or a private blockchain. For example, the tracking unit records the recycling results in the blockchain and manages them transparently and securely. The tracking unit can also enable users to check the recycling history.

[0107] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0108] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] For example, the reception unit can take a photo of the recycling object using the camera 42 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the photo using a generation AI to determine the type and condition of the recycling object. For example, the granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants points or coupons based on the analysis results. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12, and tracks the recycling results using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0114] 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.

[0115] 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 the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] 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 performed in a secure state.

[0118] Fig. 4 shows an example of 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0124] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] For example, the reception unit can take a photo of the recycling object using the camera 42 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the photo using a generation AI to determine the type and condition of the recycling object. For example, the granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants points or coupons based on the analysis results. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12, and tracks the recycling performance using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0138] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 voice indicating a user input for 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.

[0140] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] For example, the reception unit can take a photo of the recycling object using the camera 42 of the headset terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the photo using a generation AI to determine the type and condition of the recycling object. For example, the granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants points or coupons based on the analysis results. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12, and tracks the recycling results using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0149] 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 performed in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0155] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] 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 a voice indicating a user input for 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.

[0157] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0159] For example, the reception unit can take a photo of the recycling object using the camera 42 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the photo using a generation AI to determine the type and condition of the recycling object. For example, the granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants points or coupons based on the analysis results. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12, and tracks the recycling results using blockchain technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0167] 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.

[0168] 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 Universal Serial Bus (USB) 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.

[0169] In addition, 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 upon request from the data processing device 12.

[0170] 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.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0173] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) a reception section for taking photos of items to be recycled; an analysis unit that analyzes the photograph taken by the reception unit and determines whether the recycling object has been appropriately recycled; a granting unit that grants an incentive based on the result determined by the analysis unit; A tracking unit that tracks the recycling results using block chain technology. A system characterized by: (Appendix 2) The analysis unit is Using generative AI to analyze the type and condition of items and determine whether they have been recycled appropriately 2. The system of claim 1, (Appendix 3) The applying unit is Offer incentives 2. The system of claim 1, (Appendix 4) The reception unit is To estimate a user's emotion and adjust the timing of taking a photograph based on the estimated user's emotion. 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's past recycling history and select the appropriate photography method 2. The system of claim 1, (Appendix 6) The reception unit is When taking a photo, filtering is performed based on the user's environment and situation. 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and prioritize the objects to be photographed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The reception unit is When taking photos, the system takes into account the user's geographic location information to prioritize capturing images of relevant objects. 2. The system of claim 1, (Appendix 9) The reception unit is When taking a photo, analyze the user's social media activity and capture related objects 2. The system of claim 1, (Appendix 10) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of the recyclable items 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of recyclable material 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, prioritize analysis based on when recyclables were submitted 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the analysis order based on recyclable item relevance 2. The system of claim 1, (Appendix 16) The applying unit is To estimate a user's emotion and adjust an incentive provision method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The applying unit is When awarding incentives, the system analyzes the user's past recycling behavior to select the optimal awarding method. 2. The system of claim 1, (Appendix 18) The applying unit is Customize incentive delivery based on the user's current life situation 2. The system of claim 1, (Appendix 19) The applying unit is Estimating user emotions and determining incentive priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The applying unit is When awarding incentives, the optimal awarding method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 21) The applying unit is When awarding incentives, the system analyzes users' social media activity to suggest methods of awarding them. 2. The system of claim 1, (Appendix 22) The tracking unit includes: Estimate the user's emotions and select tracking data based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The tracking unit includes: When tracking, reference past tracking data to optimize tracking algorithms 2. The system of claim 1, (Appendix 24) The tracking unit includes: When tracking, apply different tracking methods to different categories of recyclables 2. The system of claim 1, (Appendix 25) The tracking unit includes: Inferring user emotion and adjusting tracking frequency based on the estimated user emotion 2. The system of claim 1, (Appendix 26) The tracking unit includes: During tracking, the tracking data is weighted based on when the recyclables were submitted 2. The system of claim 1, (Appendix 27) The tracking unit includes: When tracking, refer to relevant market data for the recyclable items. 2. The system of claim 1, [Explanation of symbols]

[0179] 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 section for taking photos of items to be recycled; an analysis unit that analyzes the photograph taken by the reception unit and determines whether the recycling object has been appropriately recycled; a granting unit that grants an incentive based on the result determined by the analysis unit; A tracking unit that tracks the recycling results using block chain technology. A system characterized by:

2. The analysis unit is Using AI to analyze the type and condition of items to determine whether they have been recycled appropriately 2. The system of claim 1 .

3. The reception unit is To estimate a user's emotion and adjust the timing of taking a photograph based on the estimated user's emotion.

2. The system of claim 1 .

4. The reception unit is Analyze the user's past recycling history and select the appropriate photography method 2. The system of claim 1 .

5. The reception unit is When taking a photo, filtering is performed based on the user's environment and situation.

2. The system of claim 1 .

6. The reception unit is Estimate the user's emotions and prioritize the objects to be photographed based on the estimated user's emotions.

2. The system of claim 1 .

7. The reception unit is When taking photos, the system takes into account the user's geographic location information to prioritize capturing images of relevant objects.

2. The system of claim 1 .

8. The reception unit is When taking a photo, analyze the user's social media activity and capture related objects 2. The system of claim 1 .

9. The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1 .

Images