system

Abstract

The system according to this embodiment aims to enable users to easily understand the appropriate disposal method for each type of waste. [Solution] A system according to an embodiment includes a receiving unit, an identifying unit, a suggestion unit, and a providing unit. The receiving unit receives photos or text information about garbage from a user. The identifying unit identifies the type of garbage based on the information received by the receiving unit. The suggestion unit suggests a disposal method based on the type of garbage identified by the identifying unit. The providing unit provides the user with the disposal method or reuse suggestion suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies had the challenge of making it difficult for users to easily understand the appropriate disposal method for each type of waste.

[0005] The system according to this embodiment aims to enable users to easily understand the appropriate disposal method for each type of waste. [Means for solving the problem]

[0006] The system according to this embodiment comprises a reception unit, an identification unit, a proposal unit, and a provision unit. The reception unit receives photographic or textual information of waste from the user. The identification unit identifies the type of waste based on the information received by the reception unit. The proposal unit proposes a disposal method based on the type of waste identified by the identification unit. The provision unit provides the user with the disposal method or recycling proposal proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable the user to easily understand the appropriate disposal method depending on the type of waste. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A garbage disposal support system according to an embodiment of the present invention allows a user to input photos and text information about the garbage they wish to dispose of. The system then outputs garbage disposal methods in accordance with the rules of the user's local municipality. This garbage disposal support system not only recommends how to dispose of the garbage, but also recommends selling the garbage at a flea market or auction, or taking it to a recycle shop. For example, a user inputs photos and text information about the garbage they wish to dispose of. To do this, the user simply takes a photo of the garbage or inputs the information in text. For example, the user inputs a photo of an old television or text information such as "old television." This information is then input into a generation AI. The generation AI then analyzes the input information and identifies the type of garbage. The generation AI determines the type of garbage based on the photos and text information and suggests disposal methods in accordance with the rules of the user's local municipality. For example, it provides information such as the need to dispose of an old television on a specific day of the week at a designated location. Furthermore, the generation AI not only recommends how to dispose of the garbage, but also recommends selling the item at a flea market, auction, or recycle shop. For example, if the old television is still usable, the system suggests selling it on a flea market app or auction site. Taking it to a recycle shop also indicates the possibility of it being reused. This system allows users to easily learn how to dispose of garbage and also increases the possibility of reuse. This is expected to reduce the burden on the environment and make effective use of resources. This garbage disposal support system allows users to easily learn how to dispose of garbage and also increases the possibility of reuse.

[0029] The waste disposal support system according to this embodiment comprises a reception unit, an identification unit, a proposal unit, and a provision unit. The reception unit receives photographs or text information of waste from the user. Examples of waste photographs or text information from the user include, but are not limited to, JPEG images, text files, and handwritten notes. For example, the reception unit can receive photographs of waste taken by the user with a smartphone. The reception unit can also receive text information entered by the user. For example, if the user enters "old television," the reception unit receives that information. The identification unit identifies the type of waste based on the information received by the reception unit. The identification unit uses a generation AI to determine the type of waste based on the photographs and text information. For example, the identification unit analyzes a photograph of the waste and identifies the type of waste based on its shape, color, and characteristics. The identification unit can also analyze text information and identify the type of waste. For example, the identification unit analyzes the text information "old television" and identifies it as electronic waste. The proposal unit proposes a disposal method based on the type of waste identified by the identification unit. The suggestion unit uses generation AI to propose waste disposal methods in accordance with the rules of the user's local municipality. For example, the suggestion unit provides information such as the need to put out old televisions on specific days of the week at designated locations. In addition to waste disposal methods, the suggestion unit also recommends selling on flea markets or auctions, or taking items to recycling shops. For example, if an old television is still usable, the suggestion unit suggests listing it on a flea market app or auction site. It also indicates that taking it to a recycling shop could lead to its reuse. The provision unit provides the user with the disposal methods and reuse suggestions proposed by the suggestion unit. For example, the provision unit notifies the user of the proposed disposal method on their smartphone. The provision unit can also display the proposed reuse suggestions to the user. For example, the provision unit provides the user with links to flea market apps or auction sites. As a result, the waste disposal support system according to this embodiment allows users to easily learn about waste disposal methods and expands the possibilities for reuse.Some or all of the above-described processes in the identification unit, suggestion unit, and provision unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input a photo of garbage into the generation AI and cause the generation AI to identify the type of garbage. The suggestion unit can input the identified type of garbage into the generation AI and cause the generation AI to suggest a disposal method. The provision unit can input the proposed disposal method or reuse proposal into the generation AI and cause the generation AI to provide it to the user.

[0030] The identification unit can determine the type of garbage based on a photograph or text information. For example, the identification unit analyzes a photograph of the garbage and identifies the type of garbage based on its shape, color, and characteristics. For example, the identification unit inputs a photograph of the garbage into a generation AI, which analyzes the content of the photograph to identify the type of garbage. The identification unit can also analyze text information to identify the type of garbage. For example, the identification unit inputs text information such as "old TV" into the generation AI, which analyzes the information and identifies it as electronic garbage. This allows the identification unit to accurately determine the type of garbage based on the photograph or text information. Some or all of the above-described processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input a photograph of the garbage into the generation AI, which analyzes the content of the photograph to identify the type of garbage.

[0031] The suggestion function can propose waste disposal methods that comply with the rules of the local municipality. For example, the suggestion function can use a generating AI to propose waste disposal methods that comply with the rules of the local municipality. For example, the suggestion function can provide information such as the need to put out old televisions at a designated location on specific days of the week. In addition to waste disposal methods, the suggestion function can also recommend selling on flea markets or auctions, or taking them to recycling shops. For example, if an old television is still in usable condition, the suggestion function can suggest selling it on a flea market app or auction site. The suggestion function can also indicate that taking it to a recycling shop could lead to its reuse. In this way, the suggestion function can propose appropriate waste disposal methods that comply with the rules of the local municipality. Some or all of the above processing in the suggestion function may be performed using a generating AI, for example, or without using a generating AI. For example, the suggestion function can input the identified type of waste into a generating AI, and the generating AI can propose a disposal method that complies with the rules of the local municipality.

[0032] The suggestion unit can recommend listing items on a flea market or auction site. The suggestion unit can, for example, use a generative AI to recommend listing items on a flea market or auction site. For example, if an old television is still in usable condition, the suggestion unit might suggest listing it on a flea market app or auction site. This allows the suggestion unit to promote waste recycling and reduce environmental impact. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input the identified type of waste into a generative AI, which can then recommend listing it on a flea market or auction site.

[0033] The suggestion unit can recommend taking it to a recycle shop. The suggestion unit recommends taking it to a recycle shop, for example, using a generation AI. For example, the suggestion unit indicates that if it is an old television that is still usable, there is a possibility that it can be reused by taking it to a recycle shop. By doing so, the suggestion unit can increase the possibility of reuse by recommending taking it to a recycle shop. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the identified type of garbage into the generation AI, which can then recommend taking it to a recycle shop.

[0034] The providing unit can provide the proposed processing method or reuse proposal to the user. For example, the providing unit notifies the user of the proposed processing method via their smartphone. The providing unit can also display the proposed reuse proposal to the user. For example, the providing unit can provide the user with a link to a flea market app or an auction site. This allows the user to easily receive the proposed processing method or reuse proposal. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the proposed processing method or reuse proposal to the generation AI, which can then provide it to the user.

[0035] The reception desk can analyze the user's past garbage information input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (photos, text, etc.) that the user has frequently used in the past. The reception desk can also suggest relevant input methods based on the types of garbage the user has entered in the past. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI, which can then select the optimal input method.

[0036] The reception desk can filter the waste information input based on the user's current living situation and areas of interest. For example, if the user is planning to move, the reception desk will filter the information based on the waste disposal rules of the new residence. The reception desk can also prioritize displaying information about recycling and reuse if the user is interested in environmental protection. Furthermore, if the user is busy, the reception desk can suggest a simple and quick method for inputting waste information. This allows the reception desk to provide information tailored to the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input data on the user's living situation and areas of interest into a generative AI, which can then perform the filtering.

[0037] The reception desk can prioritize inputting highly relevant information when users enter garbage information, taking into account their geographical location. For example, if a user lives in a specific city or town, the reception desk can prioritize displaying information based on the garbage disposal rules of that area. Furthermore, if a user is traveling, the reception desk can provide information based on the garbage disposal rules of their current location. Additionally, if a user is planning to move, the reception desk can prioritize displaying information based on the garbage disposal rules of their new residence. This allows the reception desk to provide highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's geographical location information into a generative AI, which can then prioritize inputting highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input relevant information when inputting waste information. For example, the reception unit can input relevant waste information based on posts about waste that the user has shared on social media. The reception unit can also provide information on recycling and reuse based on information about environmental protection organizations that the user follows on social media. Furthermore, the reception unit can input relevant waste information based on events that the user has participated in on social media. In this way, the reception unit can provide relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, and the generative AI can input relevant information.

[0039] When identifying a type of garbage, the identification unit can appropriately adjust the identification algorithm by referring to past identification data. The identification unit, for example, optimizes the identification algorithm based on data on garbage identified in the past. The identification unit can also improve the identification accuracy for a specific type of garbage from the past identification data. Furthermore, the identification unit can analyze the past identification data and adjust the parameters of the identification algorithm. This allows the identification unit to optimize the identification algorithm based on the past identification data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input past identification data into the generation AI, which then optimizes the identification algorithm.

[0040] When identifying the type of waste, the identification unit can apply different identification methods to each category of waste. For example, in the case of plastic waste, the identification unit can use image recognition technology to identify it. In addition, in the case of electronic waste, the identification unit can also identify it based on text information. Furthermore, in the case of hazardous waste, the identification unit can also identify it using a method for detecting specific chemical components. This allows the identification unit to apply an appropriate identification method for each category of waste. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the identification unit can input different identification methods for each category of waste into the generation AI, and the generation AI can perform the identification.

[0041] The identification unit can determine a priority order for identifying waste types based on the waste submission deadline. For example, the identification unit prioritizes identification of waste with an approaching submission deadline. The identification unit can also adjust the priority order based on the waste submission deadline. Furthermore, if the waste submission deadline is far off, the identification unit can prioritize identifying other types of waste. In this way, the identification unit can adjust the priority order based on the waste submission deadline. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input waste submission deadline data into a generating AI, which can then determine the priority order.

[0042] The identification unit can improve the accuracy of its identification by referring to relevant literature on waste when identifying the type of waste. For example, the identification unit can improve the accuracy of its identification by referring to academic papers on waste types. It can also improve the accuracy of its identification by referring to patent documents on waste types. Furthermore, the identification unit can improve the accuracy of its identification by referring to industry guidelines on waste types. In this way, the identification unit can improve the accuracy of its identification by referring to relevant literature on waste. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the identification unit can input waste-related literature data into a generating AI, which can then improve the accuracy of its identification.

[0043] The proposal unit can adjust the level of detail of its proposals based on the importance of the waste. For example, if the waste is important, the proposal unit will propose a detailed disposal method. If the waste is of low importance, the proposal unit may also propose a simplified disposal method. Furthermore, the proposal unit can adjust the level of detail of its proposals based on the importance of the waste. In this way, the proposal unit can adjust the level of detail of its proposals based on the importance of the waste. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the proposal unit can input waste importance data into a generating AI, and the generating AI can adjust the level of detail of its proposals.

[0044] The suggestion unit can apply different suggestion algorithms depending on the waste category when making suggestions. For example, in the case of plastic waste, the suggestion unit will prioritize suggesting recycling methods. In the case of electronic waste, the suggestion unit may also suggest taking it to a recycling shop. Furthermore, in the case of hazardous waste, the suggestion unit may suggest appropriate disposal methods. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the waste category. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input waste category data into a generative AI, and the generative AI can apply a suggestion algorithm.

[0045] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the garbage. For example, the suggestion unit prioritizes proposals when the deadline for submitting the garbage is approaching. The suggestion unit can also adjust the priority of the proposal based on the time of submission of the garbage. Furthermore, the suggestion unit can also prioritize other proposals when the deadline for submitting the garbage is far away. This allows the suggestion unit to adjust the priority of the proposal based on the time of submission of the garbage. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input garbage submission time data into the generation AI, which can then determine the priority of the proposal.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the garbage when making suggestions. For example, if the relevance of the garbage is high, the suggestion unit prioritizes the suggestion. The suggestion unit can also adjust the order of suggestions based on the relevance of the garbage. Furthermore, if the relevance of the garbage is low, the suggestion unit can prioritize other suggestions. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the garbage. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can input garbage relevance data into the generation AI, and the generation AI can adjust the order of suggestions.

[0047] At the time of providing, the providing unit can select an appropriate display method by referring to the user's past operation history. The providing unit, for example, suggests an optimal display method based on display methods used by the user in the past. The providing unit can also suggest a display method with high visibility based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and select the most efficient display method. This allows the providing unit to provide an optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's past operation history data into the generation AI, which can select the optimal display method.

[0048] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the providing unit to provide the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information into the generation AI, which can select the optimal display method.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception desk can analyze a user's past waste disposal history and automatically suggest similar waste information. For example, it can record the types and methods of waste previously disposed of by the user, and when similar waste is entered again, it can suggest methods based on past disposal methods. The reception desk can also suggest disposal methods for related waste based on information about waste previously disposed of by the user. Furthermore, the reception desk can suggest the optimal disposal method based on information about waste previously disposed of by the user. In this way, the reception desk can suggest efficient waste disposal methods based on the user's past waste disposal history.

[0051] The identification unit can improve its identification accuracy by referring to the user's past waste disposal history when identifying the type of waste. For example, it can optimize the identification algorithm based on data of previously identified waste. Furthermore, the identification unit can improve the accuracy of identifying specific types of waste based on past identification data. In addition, the identification unit can analyze past identification data and adjust the parameters of the identification algorithm. This allows the identification unit to optimize the identification algorithm based on past identification data.

[0052] The proposal department can adjust the level of detail in its proposals based on the importance of the waste. For example, for important waste, it will propose a detailed disposal method. Conversely, for less important waste, the proposal department can propose a simplified disposal method. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the waste. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the waste.

[0053] The providing unit can select an appropriate display method by referring to the user's past operation history. For example, the providing unit can suggest an optimal display method based on the display methods used by the user in the past. The providing unit can also suggest a display method with high visibility based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and select the most efficient display method. This allows the providing unit to provide an optimal display method based on the user's past operation history.

[0054] When identifying a type of garbage, the identification unit can refer to past identification data and appropriately adjust the identification algorithm. For example, the identification algorithm is optimized based on data on garbage that has been identified in the past. The identification unit can also improve the accuracy of identifying a specific type of garbage from the past identification data. Furthermore, the identification unit can analyze the past identification data and adjust the parameters of the identification algorithm. This allows the identification unit to optimize the identification algorithm based on the past identification data.

[0055] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the garbage. For example, if the deadline for submitting the garbage is approaching, the suggestion unit can give priority to the proposal. The suggestion unit can also adjust the priority of the proposal based on the time of submission of the garbage. Furthermore, if the deadline for submitting the garbage is far away, the suggestion unit can also give priority to other proposals. This allows the suggestion unit to adjust the priority of the proposal based on the time of submission of the garbage.

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

[0057] Step 1: The reception unit receives a photo of trash or text information from a user. The photo of trash or text information from a user includes, for example, a JPEG image, a text file, or a handwritten note. The reception unit can receive a photo of trash taken by a user with a smartphone or text information entered by a user. Step 2: The identification unit identifies the type of garbage based on the information received by the reception unit. The identification unit uses generative AI to determine the type of garbage based on photographs and text information. For example, it analyzes a photograph of the garbage and identifies the type of garbage based on its shape, color, and characteristics. It also analyzes text information and identifies it as electronic garbage based on the information "old television." Step 3: The suggestion unit proposes a disposal method based on the type of garbage identified by the identification unit. Using generative AI, the suggestion unit suggests a disposal method that complies with the rules of the local city, town, or village. For example, in the case of an old television, it provides information that it needs to be put out in a designated place on a specific day of the week. It also suggests putting it up for sale at a flea market or auction, or taking it to a recycle shop. Step 4: The providing unit provides the processing method and reuse proposals proposed by the proposing unit to the user. The providing unit notifies the user's smartphone of the proposed processing method and displays the reuse proposals to the user. For example, the providing unit provides the user with a link to a flea market app or an auction site.

[0058] (Example 2) A garbage disposal support system according to an embodiment of the present invention allows a user to input photos and text information about the garbage they wish to dispose of. The system then outputs garbage disposal methods in accordance with the rules of the user's local municipality. This garbage disposal support system not only recommends how to dispose of the garbage, but also recommends selling the garbage at a flea market or auction, or taking it to a recycle shop. For example, a user inputs photos and text information about the garbage they wish to dispose of. To do this, the user simply takes a photo of the garbage or inputs the information in text. For example, the user inputs a photo of an old television or text information such as "old television." This information is then input into a generation AI. The generation AI then analyzes the input information and identifies the type of garbage. The generation AI determines the type of garbage based on the photos and text information and suggests disposal methods in accordance with the rules of the user's local municipality. For example, it provides information such as the need to dispose of an old television on a specific day of the week at a designated location. Furthermore, the generation AI not only recommends how to dispose of the garbage, but also recommends selling the item at a flea market, auction, or recycle shop. For example, if the old television is still usable, the system suggests selling it on a flea market app or auction site. Taking it to a recycle shop also indicates the possibility of it being reused. This system allows users to easily learn how to dispose of garbage and also increases the possibility of reuse. This is expected to reduce the burden on the environment and make effective use of resources. This garbage disposal support system allows users to easily learn how to dispose of garbage and also increases the possibility of reuse.

[0059] The waste disposal support system according to this embodiment comprises a reception unit, an identification unit, a proposal unit, and a provision unit. The reception unit receives photographs or text information of waste from the user. Examples of waste photographs or text information from the user include, but are not limited to, JPEG images, text files, and handwritten notes. For example, the reception unit can receive photographs of waste taken by the user with a smartphone. The reception unit can also receive text information entered by the user. For example, if the user enters "old television," the reception unit receives that information. The identification unit identifies the type of waste based on the information received by the reception unit. The identification unit uses a generation AI to determine the type of waste based on the photographs and text information. For example, the identification unit analyzes a photograph of the waste and identifies the type of waste based on its shape, color, and characteristics. The identification unit can also analyze text information and identify the type of waste. For example, the identification unit analyzes the text information "old television" and identifies it as electronic waste. The proposal unit proposes a disposal method based on the type of waste identified by the identification unit. The suggestion unit uses generation AI to propose waste disposal methods in accordance with the rules of the user's local municipality. For example, the suggestion unit provides information such as the need to put out old televisions on specific days of the week at designated locations. In addition to waste disposal methods, the suggestion unit also recommends selling on flea markets or auctions, or taking items to recycling shops. For example, if an old television is still usable, the suggestion unit suggests listing it on a flea market app or auction site. It also indicates that taking it to a recycling shop could lead to its reuse. The provision unit provides the user with the disposal methods and reuse suggestions proposed by the suggestion unit. For example, the provision unit notifies the user of the proposed disposal method on their smartphone. The provision unit can also display the proposed reuse suggestions to the user. For example, the provision unit provides the user with links to flea market apps or auction sites. As a result, the waste disposal support system according to this embodiment allows users to easily learn about waste disposal methods and expands the possibilities for reuse.Some or all of the above-described processes in the identification unit, suggestion unit, and provision unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input a photo of garbage into the generation AI and cause the generation AI to identify the type of garbage. The suggestion unit can input the identified type of garbage into the generation AI and cause the generation AI to suggest a disposal method. The provision unit can input the proposed disposal method or reuse proposal into the generation AI and cause the generation AI to provide it to the user.

[0060] The identification unit can determine the type of garbage based on a photograph or text information. For example, the identification unit analyzes a photograph of the garbage and identifies the type of garbage based on its shape, color, and characteristics. For example, the identification unit inputs a photograph of the garbage into a generation AI, which analyzes the content of the photograph to identify the type of garbage. The identification unit can also analyze text information to identify the type of garbage. For example, the identification unit inputs text information such as "old TV" into the generation AI, which analyzes the information and identifies it as electronic garbage. This allows the identification unit to accurately determine the type of garbage based on the photograph or text information. Some or all of the above-described processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input a photograph of the garbage into the generation AI, which analyzes the content of the photograph to identify the type of garbage.

[0061] The suggestion function can propose waste disposal methods that comply with the rules of the local municipality. For example, the suggestion function can use a generating AI to propose waste disposal methods that comply with the rules of the local municipality. For example, the suggestion function can provide information such as the need to put out old televisions at a designated location on specific days of the week. In addition to waste disposal methods, the suggestion function can also recommend selling on flea markets or auctions, or taking them to recycling shops. For example, if an old television is still in usable condition, the suggestion function can suggest selling it on a flea market app or auction site. The suggestion function can also indicate that taking it to a recycling shop could lead to its reuse. In this way, the suggestion function can propose appropriate waste disposal methods that comply with the rules of the local municipality. Some or all of the above processing in the suggestion function may be performed using a generating AI, for example, or without using a generating AI. For example, the suggestion function can input the identified type of waste into a generating AI, and the generating AI can propose a disposal method that complies with the rules of the local municipality.

[0062] The suggestion unit can recommend listing items on a flea market or auction site. The suggestion unit can, for example, use a generative AI to recommend listing items on a flea market or auction site. For example, if an old television is still in usable condition, the suggestion unit might suggest listing it on a flea market app or auction site. This allows the suggestion unit to promote waste recycling and reduce environmental impact. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input the identified type of waste into a generative AI, which can then recommend listing it on a flea market or auction site.

[0063] The suggestion unit can recommend taking it to a recycle shop. The suggestion unit recommends taking it to a recycle shop, for example, using a generation AI. For example, the suggestion unit indicates that if it is an old television that is still usable, there is a possibility that it can be reused by taking it to a recycle shop. By doing so, the suggestion unit can increase the possibility of reuse by recommending taking it to a recycle shop. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the identified type of garbage into the generation AI, which can then recommend taking it to a recycle shop.

[0064] The providing unit can provide the proposed processing method or reuse proposal to the user. For example, the providing unit notifies the user of the proposed processing method via their smartphone. The providing unit can also display the proposed reuse proposal to the user. For example, the providing unit can provide the user with a link to a flea market app or an auction site. This allows the user to easily receive the proposed processing method or reuse proposal. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the proposed processing method or reuse proposal to the generation AI, which can then provide it to the user.

[0065] The reception unit can estimate the user's emotions and adjust the timing of the garbage information input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the reminder and prompt the user to input garbage information when the user is relaxed. Furthermore, if the user is relaxed, the reception unit can immediately send a notification prompting the user to input garbage information. Furthermore, if the user is busy, the reception unit can set a reminder to input garbage information later and send a notification at an appropriate time. This allows the reception unit to prompt the user to input garbage information at an appropriate time according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which then performs emotion estimation and adjusts the timing of the garbage information input based on the result.

[0066] The reception desk can analyze the user's past garbage information input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (photos, text, etc.) that the user has frequently used in the past. The reception desk can also suggest relevant input methods based on the types of garbage the user has entered in the past. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI, which can then select the optimal input method.

[0067] The reception desk can filter the waste information input based on the user's current living situation and areas of interest. For example, if the user is planning to move, the reception desk will filter the information based on the waste disposal rules of the new residence. The reception desk can also prioritize displaying information about recycling and reuse if the user is interested in environmental protection. Furthermore, if the user is busy, the reception desk can suggest a simple and quick method for inputting waste information. This allows the reception desk to provide information tailored to the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input data on the user's living situation and areas of interest into a generative AI, which can then perform the filtering.

[0068] The reception unit can estimate the user's emotions and determine the priority of garbage information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can postpone input of garbage information of low importance. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input all garbage information at once. Furthermore, if the user is in a hurry, the reception unit can also suggest that only the most important garbage information be input first. This allows the reception unit to adjust the input priority of garbage information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which then performs emotion estimation and determines the priority of garbage information based on the result.

[0069] The reception desk can prioritize inputting highly relevant information when users enter garbage information, taking into account their geographical location. For example, if a user lives in a specific city or town, the reception desk can prioritize displaying information based on the garbage disposal rules of that area. Furthermore, if a user is traveling, the reception desk can provide information based on the garbage disposal rules of their current location. Additionally, if a user is planning to move, the reception desk can prioritize displaying information based on the garbage disposal rules of their new residence. This allows the reception desk to provide highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's geographical location information into a generative AI, which can then prioritize inputting highly relevant information.

[0070] The reception unit can analyze the user's social media activity and input relevant information when inputting waste information. For example, the reception unit can input relevant waste information based on posts about waste that the user has shared on social media. The reception unit can also provide information on recycling and reuse based on information about environmental protection organizations that the user follows on social media. Furthermore, the reception unit can input relevant waste information based on events that the user has participated in on social media. In this way, the reception unit can provide relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, and the generative AI can input relevant information.

[0071] The identification unit can estimate the user's emotions and adjust the accuracy of waste type identification based on the estimated user emotions. For example, if the user is stressed, the identification unit can use a simple classification method to identify the type of waste. Alternatively, if the user is relaxed, the identification unit can use a more detailed classification method to identify the type of waste. Furthermore, if the user is in a hurry, the identification unit can use a simplified algorithm to quickly identify the type of waste. This allows the identification unit to adjust the accuracy of waste type identification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using a generative AI, or not using a generative AI. For example, the identification unit can input user emotion data into a generative AI, which can perform emotion estimation and adjust the accuracy of waste type identification based on the results.

[0072] When identifying a type of garbage, the identification unit can appropriately adjust the identification algorithm by referring to past identification data. The identification unit, for example, optimizes the identification algorithm based on data on garbage identified in the past. The identification unit can also improve the identification accuracy for a specific type of garbage from the past identification data. Furthermore, the identification unit can analyze the past identification data and adjust the parameters of the identification algorithm. This allows the identification unit to optimize the identification algorithm based on the past identification data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input past identification data into the generation AI, which then optimizes the identification algorithm.

[0073] When identifying the type of waste, the identification unit can apply different identification methods to each category of waste. For example, in the case of plastic waste, the identification unit can use image recognition technology to identify it. In addition, in the case of electronic waste, the identification unit can also identify it based on text information. Furthermore, in the case of hazardous waste, the identification unit can also identify it using a method for detecting specific chemical components. This allows the identification unit to apply an appropriate identification method for each category of waste. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the identification unit can input different identification methods for each category of waste into the generation AI, and the generation AI can perform the identification.

[0074] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the identification unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the identification unit can provide a display method that focuses on the main points. This allows the identification unit to adjust the display method of the identification results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's emotion data into the generation AI, which then performs emotion estimation and adjusts the display method of the identification results based on the result.

[0075] The identification unit can determine a priority order for identifying waste types based on the waste submission deadline. For example, the identification unit prioritizes identification of waste with an approaching submission deadline. The identification unit can also adjust the priority order based on the waste submission deadline. Furthermore, if the waste submission deadline is far off, the identification unit can prioritize identifying other types of waste. In this way, the identification unit can adjust the priority order based on the waste submission deadline. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input waste submission deadline data into a generating AI, which can then determine the priority order.

[0076] The identification unit can improve the accuracy of its identification by referring to relevant literature on waste when identifying the type of waste. For example, the identification unit can improve the accuracy of its identification by referring to academic papers on waste types. It can also improve the accuracy of its identification by referring to patent documents on waste types. Furthermore, the identification unit can improve the accuracy of its identification by referring to industry guidelines on waste types. In this way, the identification unit can improve the accuracy of its identification by referring to relevant literature on waste. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the identification unit can input waste-related literature data into a generating AI, which can then improve the accuracy of its identification.

[0077] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This allows the suggestion unit to adjust the way suggestions are expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which then performs emotion estimation and adjusts the way suggestions are expressed based on the results.

[0078] The proposal unit can adjust the level of detail of its proposals based on the importance of the waste. For example, if the waste is important, the proposal unit will propose a detailed disposal method. If the waste is of low importance, the proposal unit may also propose a simplified disposal method. Furthermore, the proposal unit can adjust the level of detail of its proposals based on the importance of the waste. In this way, the proposal unit can adjust the level of detail of its proposals based on the importance of the waste. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the proposal unit can input waste importance data into a generating AI, and the generating AI can adjust the level of detail of its proposals.

[0079] The suggestion unit can apply different suggestion algorithms depending on the waste category when making suggestions. For example, in the case of plastic waste, the suggestion unit will prioritize suggesting recycling methods. In the case of electronic waste, the suggestion unit may also suggest taking it to a recycling shop. Furthermore, in the case of hazardous waste, the suggestion unit may suggest appropriate disposal methods. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the waste category. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input waste category data into a generative AI, and the generative AI can apply a suggestion algorithm.

[0080] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the suggestion unit to adjust the length of the suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the length of the suggestions based on the results.

[0081] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the garbage. For example, the suggestion unit prioritizes proposals when the deadline for submitting the garbage is approaching. The suggestion unit can also adjust the priority of the proposal based on the time of submission of the garbage. Furthermore, the suggestion unit can also prioritize other proposals when the deadline for submitting the garbage is far away. This allows the suggestion unit to adjust the priority of the proposal based on the time of submission of the garbage. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input garbage submission time data into the generation AI, which can then determine the priority of the proposal.

[0082] The suggestion unit can adjust the order of suggestions based on the relevance of the garbage when making suggestions. For example, if the relevance of the garbage is high, the suggestion unit prioritizes the suggestion. The suggestion unit can also adjust the order of suggestions based on the relevance of the garbage. Furthermore, if the relevance of the garbage is low, the suggestion unit can prioritize other suggestions. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the garbage. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can input garbage relevance data into the generation AI, and the generation AI can adjust the order of suggestions.

[0083] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. In this way, the service provider can adjust how the information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, the generative AI can perform emotion estimation, and the service provider can adjust how the information is displayed based on the result.

[0084] At the time of providing, the providing unit can select an appropriate display method by referring to the user's past operation history. The providing unit, for example, suggests an optimal display method based on display methods used by the user in the past. The providing unit can also suggest a display method with high visibility based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and select the most efficient display method. This allows the providing unit to provide an optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's past operation history data into the generation AI, which can select the optimal display method.

[0085] The service provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider may postpone providing less important information. Conversely, if the user is relaxed, the service provider may provide all information at once. Furthermore, if the user is in a hurry, the service provider may prioritize providing only the most important information. In this way, the service provider can adjust the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can perform emotion estimation and determine the priority of information based on the results.

[0086] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the providing unit to provide the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information into the generation AI, which can select the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts photos of garbage and text information from the user using the camera 42 or touch panel 38A of the smart device 14. The identification unit identifies the type of garbage using a generation AI by the identification processing unit 290 of the data processing device 12. The suggestion unit generates proposals for garbage disposal methods and reuse in accordance with the rules of the city, ward, town, or village where the user lives by using the identification processing unit 290 of the data processing device 12. The provision unit provides the proposed disposal methods and reuse proposals to the user using the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts photos of garbage and text information from the user using the camera 42 and microphone 238 of the smart glasses 214. The identification unit identifies the type of garbage using a generation AI by the identification processing unit 290 of the data processing device 12. The suggestion unit generates proposals for garbage disposal methods and reuse in accordance with the rules of the city, ward, town, or village where the user lives by using the identification processing unit 290 of the data processing device 12. The provision unit provides the proposed disposal methods and reuse proposals to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit accepts photos of garbage and text information from the user using the camera 42 and microphone 238 of the headset-type terminal 314. The identification unit identifies the type of garbage using a generation AI by the identification processing unit 290 of the data processing device 12. The suggestion unit generates proposals for garbage disposal methods and reuse in accordance with the rules of the city, ward, town, or village where the user lives by using the identification processing unit 290 of the data processing device 12. The provision unit provides the proposed disposal methods and reuse proposals to the user using the display 343 and speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives photos of garbage and text information from the user using the camera 42 and microphone 238 of the robot 414. The identification unit identifies the type of garbage using a generation AI by the identification processing unit 290 of the data processing device 12. The suggestion unit generates proposals for garbage disposal methods and reuse in accordance with the rules of the city, ward, town, or village where the user lives by using the identification processing unit 290 of the data processing device 12. The provision unit provides the proposed disposal methods and reuse proposals to the user using the speaker 240 and display device of the robot 414.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception desk can analyze a user's past waste disposal history and automatically suggest similar waste information. For example, it can record the types and methods of waste previously disposed of by the user, and when similar waste is entered again, it can suggest methods based on past disposal methods. The reception desk can also suggest disposal methods for related waste based on information about waste previously disposed of by the user. Furthermore, the reception desk can suggest the optimal disposal method based on information about waste previously disposed of by the user. In this way, the reception desk can suggest efficient waste disposal methods based on the user's past waste disposal history.

[0089] The identification unit can improve its identification accuracy by referring to the user's past waste disposal history when identifying the type of waste. For example, it can optimize the identification algorithm based on data of previously identified waste. Furthermore, the identification unit can improve the accuracy of identifying specific types of waste based on past identification data. In addition, the identification unit can analyze past identification data and adjust the parameters of the identification algorithm. This allows the identification unit to optimize the identification algorithm based on past identification data.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. This allows the suggestion unit to adjust the way suggestions are expressed depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The proposal department can adjust the level of detail in its proposals based on the importance of the waste. For example, for important waste, it will propose a detailed disposal method. Conversely, for less important waste, the proposal department can propose a simplified disposal method. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the waste. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the waste.

[0092] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method is provided. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This allows the providing unit to adjust the display method of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The providing unit can select an appropriate display method by referring to the user's past operation history. For example, the providing unit can suggest an optimal display method based on the display methods used by the user in the past. The providing unit can also suggest a display method with high visibility based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and select the most efficient display method. This allows the providing unit to provide an optimal display method based on the user's past operation history.

[0094] The reception system can estimate the user's emotions and adjust the timing of trash information input based on the estimated emotions. For example, if the user is stressed, the system can delay the reminder and prompt them to input trash information when they are relaxed. Alternatively, if the user is relaxed, the system can immediately send a notification prompting them to input trash information. Furthermore, if the user is busy, the system can set a reminder to input later and send a notification at an appropriate time. This allows the system to prompt for trash information input at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] When identifying a type of garbage, the identification unit can refer to past identification data and appropriately adjust the identification algorithm. For example, the identification algorithm is optimized based on data on garbage that has been identified in the past. The identification unit can also improve the accuracy of identifying a specific type of garbage from the past identification data. Furthermore, the identification unit can analyze the past identification data and adjust the parameters of the identification algorithm. This allows the identification unit to optimize the identification algorithm based on the past identification data.

[0096] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the garbage. For example, if the deadline for submitting the garbage is approaching, the suggestion unit can give priority to the proposal. The suggestion unit can also adjust the priority of the proposal based on the time of submission of the garbage. Furthermore, if the deadline for submitting the garbage is far away, the suggestion unit can also give priority to other proposals. This allows the suggestion unit to adjust the priority of the proposal based on the time of submission of the garbage.

[0097] The information provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is stressed, less important information may be postponed. Conversely, if the user is relaxed, the provider can provide all information at once. Furthermore, if the user is in a hurry, the provider can prioritize providing only the most important information. In this way, the provider can adjust the priority of information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit receives a photo of trash or text information from a user. The photo of trash or text information from a user includes, for example, a JPEG image, a text file, or a handwritten note. The reception unit can receive a photo of trash taken by a user with a smartphone or text information entered by a user. Step 2: The identification unit identifies the type of garbage based on the information received by the reception unit. The identification unit uses generative AI to determine the type of garbage based on photographs and text information. For example, it analyzes a photograph of the garbage and identifies the type of garbage based on its shape, color, and characteristics. It also analyzes text information and identifies it as electronic garbage based on the information "old television." Step 3: The suggestion unit proposes a disposal method based on the type of garbage identified by the identification unit. Using generative AI, the suggestion unit suggests a disposal method that complies with the rules of the local city, town, or village. For example, in the case of an old television, it provides information that it needs to be put out in a designated place on a specific day of the week. It also suggests putting it up for sale at a flea market or auction, or taking it to a recycle shop. Step 4: The providing unit provides the processing method and reuse proposals proposed by the proposing unit to the user. The providing unit notifies the user's smartphone of the proposed processing method and displays the reuse proposals to the user. For example, the providing unit provides the user with a link to a flea market app or an auction site.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] 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.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a photo or text information of garbage from a user; an identification unit that identifies the type of garbage based on the information received by the reception unit; a suggestion unit that proposes a disposal method based on the type of waste identified by the identification unit; a providing unit that provides a user with the processing method or reuse proposal proposed by the proposing unit. A system characterized by:

2. The identification unit Identify the type of garbage based on photos or text information The system of claim 1 .

3. The proposal unit Suggest ways to dispose of garbage in accordance with local municipal rules The system of claim 1 .

4. The proposal unit Recommend selling at a flea market or auction The system of claim 1 .

5. The proposal unit We recommend taking it to a recycling shop. The system of claim 1 .

6. The providing unit Providing users with suggested disposal methods or reuse suggestions The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the timing of garbage information input based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past garbage information input history and select the appropriate input method The system of claim 1 .

9. The reception unit When entering garbage information, filtering is performed based on the user's current living situation and areas of interest. The system of claim 1 .

10. The reception unit Estimate the user's emotions and prioritize the garbage information to be input based on the estimated user emotions. The system of claim 1 .

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