Support system, support method, support device, and program

Abstract

Provide a support system capable of ensuring the accuracy of calculating the decarbonization amount. 【Solution means】The support system includes a terminal equipped with a camera and a server capable of communicating with the terminal. The terminal captures an object for which the decarbonization amount is to be calculated and transmits an image of the object to the server. The server includes an acquisition unit that acquires the image of the object transmitted by the terminal, and a recognition unit that recognizes features used for calculating the decarbonization amount from the image of the object. The terminal displays the calculation result of the decarbonization amount of the object based on the features.

Description

Technical Field

[0001] The present invention relates to a support system, a support method, a support device, and a program.

Background Art

[0002] Patent Document 1 discloses a support system that mediates an exchange contract between a decarbonization point based on the amount of decarbonization, which is the amount of carbon dioxide emissions suppressed by a user refraining from using automobiles and motorcycles and moving on foot or by bicycle, and benefits provided by a client, thereby supporting the maintenance of the health of individual users and the realization of a decarbonized society.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, in efforts towards decarbonization, there are various activities such as the use of portable water cylinders, the use of compost, and waste oil recovery. However, if the recording of these activities depends only on manual input or taking photos, there is a problem that it is difficult to ensure accuracy.

[0005] The present invention has been made in view of the above problems, and its main object is to provide a support system, a support method, a support device, and a program capable of ensuring the accuracy of calculating the amount of decarbonization.

Means for Solving the Problems

[0006] To solve the above problems, a support system according to one aspect of the present invention includes a terminal equipped with a camera and a server capable of communicating with the terminal. The terminal captures an object for which the decarbonization amount is to be calculated and transmits an image of the object to the server. The server includes an acquisition unit that acquires the image of the object transmitted by the terminal, and a recognition unit that recognizes features used for calculating the decarbonization amount from the image of the object. The terminal displays the calculation result of the decarbonization amount of the object based on the features.

[0007] Also, a support method according to another aspect of the present invention is to capture an object for which the decarbonization amount is to be calculated by a terminal equipped with a camera, recognize features used for calculating the decarbonization amount from the image of the object, calculate the decarbonization amount of the object based on the features, and display the calculation result of the decarbonization amount on the terminal.

[0008] Also, a support device according to another aspect of the present invention includes an acquisition unit that acquires an image of an object for which the decarbonization amount is to be calculated, captured by a terminal equipped with a camera, and a relay unit that causes a recognition unit to recognize features used for calculating the decarbonization amount from the image of the object.

[0009] Also, a program according to another aspect of the present invention causes a computer to acquire an image of an object for which the decarbonization amount is to be calculated, captured by a terminal equipped with a camera, and cause a recognition unit to recognize features used for calculating the decarbonization amount from the image of the object.

Advantages of the Invention

[0010] According to the present invention, it is possible to ensure the accuracy of calculating the decarbonization amount.

Brief Description of the Drawings

[0011]

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Embodiments for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In this specification and each figure, elements that are the same as those described above with respect to the previously presented figures may be assigned the same reference numerals, and detailed descriptions may be omitted as appropriate.

[0013] FIG. 1 is a diagram showing an example of a support system 100. The support system 100 includes a server 1 and a terminal 2 possessed by a user UZ. The server 1 and the terminal 2 are connected to a communication network NT such as the Internet and can communicate with each other via the network.

[0014] The server 1 is composed of a plurality of server computers including a support device 3 and an AI (Artificial Intelligence) server 4. However, the server 1 is not limited to this and may be composed of one server computer.

[0015] The terminal 2 is a portable wireless information terminal such as a smartphone or a tablet computer.

[0016] The support system 100 is a system for supporting the behavioral transformation of the user UZ towards decarbonization by visualizing the decarbonization amount of the object BJ related to the decarbonization actions of the user UZ.

[0017] The object BJ is, for example, a bottle, food waste, a lunch box container, a container containing waste oil, food, an invoice, and fresh food, etc. The decarbonization amount represents the reduction amount of greenhouse gas emissions such as carbon dioxide.

[0018] Figure 2 is a block diagram showing a configuration example of the support device 3. The support device 3 is a computer including a processing unit 11, a storage unit 12, and a communication unit 13. The AI server 4 also has a similar configuration.

[0019] The processing unit 11 includes a CPU and executes information processing according to the programs stored in the storage unit 12. The storage unit 12 includes a RAM, a ROM, and a non-volatile memory, etc. The communication unit 13 is an interface for connecting the support device 3 to the communication network NT.

[0020] The programs may be provided to the support device 3 via a non-temporary storage medium or may be provided to the support device 3 via a communication line.

[0021] Figure 3 is a block diagram showing a configuration example of the terminal 2. The terminal 2 is also a computer including a processing unit 21, a storage unit 22, and a communication unit 23, similar to the support device 3. The terminal 2 further includes a touch panel 24, a camera 25, and a GNSS receiver 26.

[0022] In addition to the communication unit 23 that realizes a mobile communication or wireless LAN communication method for connecting the terminal 2 to the communication network NT, the communication unit 23 further includes a communication unit that realizes short-range wireless communication such as Bluetooth (registered trademark).

[0023] The touch panel 24 integrally includes a display unit and an operation unit. The GNSS receiver 26 detects the position of the terminal 2 based on radio waves received from the GNSS (Global Navigation Satellite System).

[0024] The processing unit 21 captures an image of the object BJ with the camera 25 according to the application program stored in the storage unit 22, and transmits the image of the object BJ to the server 1. As shown in FIG. 4, an imaging screen PS for capturing the object BJ is displayed on the terminal 2.

[0025] On the imaging screen PS, a display area SA where the object BJ being imaged by the camera 25 is displayed, an imaging button BT, and a character string CL prompting imaging are arranged. By the user UZ operating the imaging button BT, an image of the object BJ is captured.

[0026] Also, the processing unit 21 receives a response from the server 1 and displays the calculation result of the decarbonization amount based on the characteristics of the object BJ. As shown in FIG. 5, a notification screen NS for displaying the calculation result DC of the decarbonization amount is displayed on the terminal 2.

[0027] FIG. 6 is a block diagram showing a functional configuration example of the support device 3 and the AI server 4.

[0028] The support device 3 includes an acquisition unit 31, a relay unit 32, a calculation unit 33, and a notification unit 34. These functional units 31 to 34 are realized by the processing unit 11 executing information processing according to a program. Also, the support device 3 includes an image storage unit 39. The image storage unit 39 may be constructed in the storage unit 12 or in an external storage device.

[0029] The AI server 4 includes a recognition unit 41. This functional unit 41 is realized by the processing unit of the AI server 4 executing information processing according to a program. Also, the AI server 4 includes a model storage unit 49. The model storage unit 49 may be constructed in the storage unit of the AI server 4 or in an external storage device.

[0030] The acquisition unit 31 of the support device 3 acquires the image of the object BJ transmitted by the terminal 2 and stores it in the image storage unit 39.

[0031] The relay unit 32 causes the recognition unit 41 of the AI server 4 to recognize the features used for calculating the decarburization amount from the image of the object BJ.

[0032] The calculation unit 33 calculates the decarburization amount of the object BJ based on the features recognized by the recognition unit 41 of the AI server 4.

[0033] The notification unit 34 notifies the terminal 2 of the calculation result of the decarburization amount and causes the terminal 2 to display the calculation result of the decarburization amount.

[0034] Note that the implementation entity of each functional unit is not limited to the above example. For example, the support device 3 may include the recognition unit 41 to recognize features from the image of the object BJ. Alternatively, the terminal 2 may include the calculation unit 33 to calculate the decarburization amount based on the features acquired from the support device 3.

[0035] The recognition unit 41 of the AI server 4 recognizes the features used for calculating the decarburization amount from the image of the object BJ.

[0036] The recognition unit 41 uses the learned model stored in the model storage unit 49 to recognize features from the image of the object BJ. Not limited to this, the recognition unit 41 may also recognize features from the image of the object BJ by rule-based image recognition.

[0037] The model storage unit 49 stores a plurality of learned models prepared for each type of the object BJ, and the recognition unit 41 uses the learned model corresponding to the type of the object BJ to recognize features from the image of the object BJ.

[0038] The features used for calculating the decarburization amount include, for example, features related to the amount (capacity, weight, or volume, etc.) of the object BJ. The decarburization amount of the object BJ is calculated according to the recognized amount.

[0039] In addition, the features used to calculate the decarbonization amount may include the eligibility of the object BJ. The decarbonization amount of the object BJ is calculated when the eligibility is equal to or greater than a predetermined value, and is not calculated when the eligibility is less than the predetermined value.

[0040] Hereinafter, an overview of the learning phase and the inference phase of the learned model will be described.

[0041] As shown in FIG. 7, in the learning phase, machine learning of the model MD (pre-learning model) is executed using the learning image MG and the teacher data of the amount and eligibility. The model MD is an image recognition model such as a convolutional neural network (CNN).

[0042] One element of the output layer of the model MD is composed of, for example, an identity function and outputs a numerical value representing the "amount". Another element of the output layer of the model MD is composed of, for example, a sigmoid function and outputs a numerical value between 0 and 1 representing the "eligibility".

[0043] The "amount" of the teacher data is a numerical value representing capacity, weight, volume, etc. The "eligibility" of the teacher data is a binary label representing the presence or absence of eligibility. For example, an OK label representing the presence of eligibility is represented by 1, and an NG label representing the absence of eligibility is represented by 0.

[0044] In machine learning, the learning image MG is input into the model MD, the difference between the amount and eligibility output from the model MD and the amount and eligibility of the teacher data is calculated, and error backpropagation calculation is performed to reduce the calculated difference, thereby adjusting the parameters of the model MD.

[0045] The learned model LM generated in this way is stored in the model storage unit 49.

[0046] As shown in FIG. 8, in the inference phase, the recognition unit 41 inputs the image SG of the object BJ transmitted by the terminal 2 into the learned model LM and outputs a numerical value representing the "amount" and a numerical value representing the "eligibility".

[0047] Specific examples such as the generation of a learned model, feature recognition, and calculation of decarbonization amount for each type of object BJ will be described in detail later.

[0048] FIG. 9 is a flowchart showing an example of the procedure of the support method realized in the support system 100. The processing unit 21 of the terminal 2 executes the processes S21 to S24 shown in the left part of the figure according to the program. The processing unit 11 of the support device 3 executes the processes S31 to S34 shown in the center of the figure according to the program. The processing unit of the AI server 4 executes the processes S41 to S42 shown in the right part of the figure according to the program.

[0049] First, the terminal 2 captures an image of the object BJ with the camera 25 according to the operation of the user UZ (S21). For the imaging, an imaging screen PS (see FIG. 4) is used. The imaging screen PS is prepared for each type of object BJ and is selected by the user UZ before imaging.

[0050] Next, the terminal 2 attaches a tag representing the type of the object BJ to the image of the captured object BJ (S22). Here, the tag corresponding to the imaging screen PS used for imaging the object BJ is selected and attached to the image.

[0051] Next, the terminal 2 transmits the image of the object BJ with the tag attached to the support device 3 (S23).

[0052] For example, when the object BJ is a bottle, the user UZ captures an image of the bottle using the imaging screen PS for the bottle. As a result, a tag representing the bottle is attached to the captured image of the bottle.

[0053] The support device 3 acquires the image of the object BJ transmitted by the terminal 2 (S31, processing as the acquisition unit 31).

[0054] Next, the support device 3 relays the image of the object BJ (S32, processing as the relay unit 32). That is, the support device 3 causes the AI server 4 to recognize the features used for calculating the decarbonization amount from the image of the object BJ.

[0055] The AI server 4 recognizes features used for calculating the decarbonization amount from the image of the object BJ transmitted by the support device 3 (S41, processing as the recognition unit 41), and transmits the recognized features to the support device 3 (S42).

[0056] At this time, the support device 3 designates to use a learned model corresponding to the tag attached to the image of the object BJ, and the AI server 4 recognizes features using the designated learned model. The selection of the learned model corresponding to the tag may be performed by the AI server 4.

[0057] For example, when the object BJ is a bottle, the features of the bottle are recognized using a learned model for bottles corresponding to the tag representing the bottle attached to the image.

[0058] Based on the features transmitted by the AI server 4, the support device 3 calculates the decarbonization amount of the object BJ (S33, processing as the calculation unit 33), and notifies the terminal 2 of the calculation result of the decarbonization amount (S34, processing as the notification unit 34).

[0059] The terminal 2 displays the calculation result of the decarbonization amount notified by the support device 3 (S24). For the display of the decarbonization amount, a notification screen NS (see FIG. 5) is used. Note that the calculation of the decarbonization amount may be performed on the terminal 2.

[0060] As described above, by visualizing the decarbonization amount of the object BJ, the user UZ can visually understand the contribution of the object BJ to decarbonization, so it is possible to promote behavioral changes towards effective decarbonization actions. Also, by calculating the decarbonization amount after recognizing the features of the object BJ, it is possible to ensure accuracy. That is, it is possible to avoid misunderstandings, incorrect inputs, false inputs, etc. that occur when relying only on manual input or photo shooting, and ensure the accuracy of the calculation of the decarbonization amount.

[0061] In addition, by awarding decarbonization points to user UZ according to the amount of decarbonization and making the decarbonization points exchangeable for benefits provided within a community such as a local government, it becomes possible to enhance the motivation for user UZ to save decarbonization points and further promote the behavior change of user UZ.

[0062] Hereinafter, specific examples such as the generation of a learned model for each type of object BJ, the recognition of features, and the calculation of the amount of decarbonization will be described.

[0063] (1) My Bottle A large amount of carbon dioxide is emitted during the manufacturing, disposal, and recycling processes of plastic bottles. Therefore, not buying bottled beverages and carrying a portable water bottle (so-called my bottle) is a decarbonization action. Therefore, in the example where the object BJ is a bottle, the capacity of the bottle and its suitability as a portable water bottle are recognized by a learned model, and the amount of decarbonization by using the bottle as a portable water bottle is calculated. For example, by attaching an OK label to the learning image of a bottle with a lid made of stainless steel, aluminum, plastic, or silicone and being portable, and attaching an NG label to the learning image of a container without a lid made of paper, plastic, stainless steel, pottery, or glass, a learned model can be generated to determine the suitability as a portable water bottle. The amount of decarbonization is calculated according to the capacity of the bottle when the bottle is suitable as a portable water bottle, and is not calculated when the bottle is not suitable as a portable water bottle.

[0064] (2) Compost Since food waste contains a lot of moisture, if it is discarded as combustible waste, a large amount of carbon dioxide is emitted during the incineration process. Therefore, treating food waste with compost is a decarbonization action. Therefore, in the example where the object BJ is food waste, the weight or volume of the food waste and its suitability for compost are recognized by a learned model, and the amount of decarbonization by treating the food waste with compost is calculated. For example, by attaching an OK label to learning images of compostable waste (especially those with a background of soil, outdoors, or a sink in the image), and an NG label to learning images of waste mixed with inappropriate waste, waste in a trash can, or non-combustible waste, etc., and generating a learned model, it becomes possible to determine the eligibility for composting. The decarbonization amount is calculated according to the weight or volume when the food waste is suitable for composting, and is not calculated when the food waste is not suitable for composting.

[0065] (3) Lunch box containers A large amount of carbon dioxide is emitted in the process of manufacturing, discarding, and recycling disposable plastic or paper lunch box containers. Therefore, bringing a lunch box in a non-disposable lunch box container is a decarbonization action. Therefore, in the example where the object BJ is a lunch box container, the learned model recognizes the eligibility of the lunch box container as waste, and calculates the decarbonization amount by using a lunch box container that does not fall under waste. For example, by attaching an OK label to learning images of non-disposable plastic or metal lunch box containers, and an NG label to learning images of disposable plastic or paper containers (so-called lunch box plastics), and generating a learned model, it becomes possible to determine whether the lunch box container falls under waste. The decarbonization amount is calculated when the lunch box container does not fall under waste (that is, when it is not disposable), and is not calculated when the lunch box container falls under waste. The decarbonization amount may be calculated according to the capacity of the lunch box container or may be uniform.

[0066] (4) Used oil Used oil (waste cooking oil) at home, if drained in the kitchen, will lead to environmental pollution, and if discarded as waste, a large amount of carbon dioxide will be emitted during the incineration process. Therefore, bringing used oil to a collection spot is a decarbonization action. Therefore, in the example where the object BJ is a container containing used oil, the learned model recognizes the capacity of the container and the eligibility for the collection of used oil, and calculates the decarbonization amount by collecting the used oil. For example, an OK label is attached to a learning image of brown or yellow oil (especially those with a collection spot reflected in the background) contained in a transparent container such as a dedicated container or a PET bottle, and an NG label is attached to a learning image of oil mixed with food waste or another liquid, oil with a lot of dirt or solids, machine oil, another liquid such as a beverage, or oil that has solidified or been absorbed by paper. By generating a learned model, it becomes possible to determine the eligibility for the collection of waste oil. The decarbonization amount is calculated according to the capacity of the container when the container containing the waste oil is suitable for collection, and is not calculated when the container containing the waste oil is not suitable for collection.

[0067] (5) Food drive Not throwing away the extra food at home and donating it to welfare organizations etc. is known as a food drive. Donating food without throwing it away also becomes a decarbonization action. Therefore, for an example where the object BJ is food, the eligibility of the food as a donated food is recognized by a learned model, and the decarbonization amount by donating the food to a food drive is calculated. For example, an OK label is attached to a learning image of food suitable for donation such as rice, dried noodles, canned food, retort food, or instant food, and an NG label is attached to a learning image of food not suitable for donation such as opened food, fresh food, or food with an expired or unknown expiration date. By generating a learned model, it becomes possible to determine the eligibility of the food as a donated food. The decarbonization amount is calculated when the food is suitable as a donated food, and is not calculated when the food is not suitable as a donated food. The decarbonization amount may be calculated according to the weight or volume of the food, or may be uniform. The application of the terminal 2 displays the collection spots of the food drive on a map and detects the implementation of the food drive when an operation related to donation is performed at the collection spot. The detection of the implementation of the food drive may be performed after the support device 3 acquires the position of the terminal 2.

[0068] (6) Inspection certificate Reducing the usage of electricity, gas, etc. is a decarbonization action. Therefore, in the example where the object BJ is a meter reading slip, the usage amount during the target period and the usage amount during the past period described in the meter reading slip are recognized by the learned model, and the decarbonization amount corresponding to the difference between the usage amount during the target period and the usage amount during the past period is calculated. For example, as shown in the imaging screen PS for the meter reading slip shown in FIG. 10, the meter reading slip includes a character string CU indicating the usage amount this month and a character string PU indicating the usage amount last month. Therefore, the usage amount this month and the usage amount last month are recognized from these, the decrease in the usage amount this month compared to the usage amount last month is calculated, and the decarbonization amount corresponding to the decrease is calculated. The target period and the past period are not limited to monthly units and may be annual units.

[0069] (7) Fresh food Consuming fresh food produced in a region in that region or a neighboring region (so-called local production and local consumption) is a decarbonization action because it is expected to reduce the carbon dioxide emissions associated with transportation. Therefore, in the example where the object BJ is fresh food, the production area of the fresh food is recognized from the label attached to the fresh food by the learned model, and the decarbonization amount based on the comparison between the position of the terminal 2 and the production area of the fresh food is calculated. For example, as shown in the imaging screen PS for fresh food shown in FIG. 11, the fresh food is attached with a label LB, and information such as the production area and weight is recognized from the label LB by the learned model. Also, for example, an OK label is attached to the learning image of fresh food with an individual label, and an NG label is attached to the learning image of fresh food sold in bulk including the price tag at the sales floor, and a learned model is generated to enable the determination of the eligibility of the label. Also, for example, an OK label is attached to the learning image in which the interior of a house is shown in the background, and an NG label is attached to the learning image in which the interior of a store is shown in the background, and a learned model is generated to enable the determination of the eligibility of the imaging location. In other words, it is possible to determine whether the fresh food is unpurchased or purchased.

[0070] FIG. 12 is a flowchart showing an example of the procedure of the support method when the object BJ is fresh food. For the steps corresponding to the flowchart of FIG. 9 above, detailed descriptions are omitted by attaching the same numbers.

[0071] After the user UZ purchases fresh food, the user launches the application program of the terminal 2 at home or the like, and uses the imaging screen PS (see FIG. 11) to image the fresh food with the camera 25 (S21).

[0072] Next, the terminal 2 acquires the position of the terminal 2 by the GNSS receiver 26 (S26), attaches a tag representing the fresh food and the position of the terminal 2 to the image (S22), and transmits the image to the support device 3 (S23).

[0073] The AI server 4 recognizes information such as the place of origin and weight described in the label LB from the image transmitted by the support device 3 (S41), and transmits it to the support device 3 (S42).

[0074] The support device 3 calculates the decarbonization amount based on the comparison between the position of the terminal 2 and the place of origin of the fresh food (S33). For example, when the place of origin of the fresh food is the same or an adjacent prefecture as the position of the terminal 2, the support device 3 calculates the decarbonization amount according to the weight of the fresh food. For the weight of the fresh food, for example, the general weight per unit quantity of the target food described in the "Food Composition Table" or the like included in the label LB is used.

[0075] The decarbonization amount of the fresh food is calculated as an amount representing how much the carbon dioxide emissions discharged at the distribution stage of the fresh food are reduced compared to the general emissions of the same type of fresh food. For the calculation of the decarbonization amount, the transport emission amount per unit weight (predetermined value) is used.

[0076] For example, when purchasing tomatoes produced in Fukuoka in Fukuoka Prefecture, the decarbonization amount is calculated using, as a comparison value, the amount of carbon dioxide emissions based on the transportation from the place of origin (e.g., Hiroshima Prefecture) of tomatoes generally sold at supermarkets and the like in that area. There are various ways to determine the comparison value, and it may also be possible to use, as the comparison value, the amount of carbon dioxide emissions based on the transportation from the main place of origin of the target fresh food (e.g., Ehime or Wakayama in the case of mandarins).

[0077] The data once read from the label LB of the fresh food is matched with data such as the place of origin, producer, and expiration date, and is treated as used, so that the decarbonization amount is not calculated repeatedly for the same fresh food.

[0078] In this example, the fresh food was imaged by the camera 25, the place of origin and weight were recognized from the label LB included in the image, and the decarbonization amount was calculated. However, this is not the only way, and the following methods may also be used.

[0079] For example, the code data may be read from a code image representing a two-dimensional code such as a QR code printed on the label LB or a one-dimensional code such as a barcode by the terminal 2, the place of origin and weight may be extracted from the code data, and the decarbonization amount may be calculated.

[0080] Also, when the label LB is an electronic tag, the code data may be read from the label LB using a short-range wireless communication function such as NFC (Near Field Communication) of the terminal 2, the place of origin and weight may be extracted from the code data, and the decarbonization amount may be calculated.

[0081] In addition, the position of the terminal 2 may be compared with the position of the store registered in advance, and the decarbonization amount of the fresh food may be calculated when the terminal 2 is outside the store, and the decarbonization amount may not be calculated when the terminal 2 is inside the store.

[0082] In addition, the decarbonization points assigned based on the decarbonization amount may have different conditions for assignment and use at the community level. For example, the decarbonization points related to local production and consumption assigned based on the decarbonization amount of fresh food as in this example may be made available only in the community of that region, or the decarbonization points may be assigned preferentially to that region over other regions. This can further strengthen the behavioral change of users towards the decarbonization actions of local production and consumption.

[0083] Also, as described above, unpackaged fresh food was regarded as NG because purchase confirmation was not possible. However, for example, after photographing the label of unpackaged fresh food at the sales floor and then obtaining and photographing the corresponding receipt, the two images obtained thereby may be matched to treat it as a purchased item.

[0084] As described above, the embodiments of the present invention have been explained, but the present invention is not limited to the embodiments described above, and it goes without saying that various changes are possible for those skilled in the art.

[0085] Hereinafter, typical embodiments will be listed.

[0086] As a first aspect, the support system includes a terminal equipped with a camera and a server capable of communicating with the terminal. The terminal captures an object for which the decarbonization amount is to be calculated and transmits an image of the object to the server. The server includes an acquisition unit that acquires the image of the object transmitted by the terminal, and a recognition unit that recognizes features used for calculating the decarbonization amount from the image of the object. The terminal displays the calculation result of the decarbonization amount of the object based on the features. According to this, it becomes possible to ensure the accuracy of the calculation of the decarbonization amount.

[0087] As a second aspect, in the first aspect, the recognition unit recognizes the features related to the amount of the object, and the decarbonization amount may be calculated according to the amount. According to this, it becomes possible to ensure the accuracy of the calculation of the decarbonization amount by using the amount of the object.

[0088] As a third aspect, in the first aspect, the recognition unit recognizes the eligibility of the object as the feature, and the decarbonization amount may not be calculated when the eligibility is below a predetermined level. According to this, it is possible to ensure the accuracy of the calculation of the decarbonization amount by using the eligibility of the object.

[0089] As a fourth aspect, in the first aspect, the object is a bottle, the recognition unit recognizes the capacity of the bottle and the eligibility as a portable water container of the bottle, and the decarbonization amount may be the decarbonization amount by using the bottle as the portable water container. According to this, it is possible to ensure the accuracy of the calculation of the decarbonization amount by using the portable water container.

[0090] As a fifth aspect, in the first aspect, the object is food waste, the recognition unit recognizes the weight or volume of the food waste and the eligibility for compost of the food waste, and the decarbonization amount may be the decarbonization amount by processing the food waste with the compost. According to this, it is possible to ensure the accuracy of the calculation of the decarbonization amount by processing food waste with compost.

[0091] As a sixth aspect, in the first aspect, in the above aspect, the object is a lunch box container, the recognition unit recognizes the applicability as waste of the lunch box container, and the decarbonization amount may be the decarbonization amount by using the lunch box container that does not fall under the waste. According to this, it is possible to ensure the accuracy of the calculation of the decarbonization amount by using a lunch box container that does not fall under the waste.

[0092] As a seventh aspect, in the first aspect, the object is a container containing waste oil, the recognition unit recognizes the capacity of the container and the eligibility for the recovery of the waste oil, and the decarbonization amount may be the decarbonization amount by the recovery of the waste oil. According to this, it is possible to ensure the accuracy of the calculation of the decarbonization amount by the recovery of waste oil.

[0093] As an eighth aspect, in the first aspect, the object is food, the recognition unit recognizes the eligibility of the food as donated food, and the decarbonization amount may be the decarbonization amount by donating the food to a food drive. According to this, it becomes possible to ensure the accuracy of calculating the decarbonization amount by donating food to a food drive.

[0094] As a ninth aspect, in the first aspect, the object is a check ticket, the recognition unit recognizes the usage amount during the target period and the usage amount during the past period described in the check ticket, and the decarbonization amount may be the decarbonization amount corresponding to the difference between the usage amount during the target period and the usage amount during the past period. According to this, it becomes possible to ensure the accuracy of calculating the decarbonization amount according to the change in the usage fee.

[0095] As a tenth aspect, in the first aspect, the terminal includes an acquisition unit that acquires position data of the terminal, the object is fresh food, the recognition unit recognizes the origin of the fresh food from a label attached to the fresh food, and the decarbonization amount may be the decarbonization amount based on the comparison between the position of the terminal and the origin of the fresh food. According to this, it becomes possible to ensure the accuracy of calculating the decarbonization amount based on the origin of the fresh food.

[0096] As an eleventh aspect, in the first aspect, the terminal may attach a tag indicating the type of the object to the image of the object, and the recognition unit may recognize the feature using the model corresponding to the tag from among a plurality of models prepared for each type of the object. According to this, it becomes possible to ensure the accuracy of calculating the decarbonization amount for each type of the object.

[0097] As a twelfth aspect, a support method includes imaging an object for calculating a decarbonization amount with a terminal equipped with a camera, recognizing a feature used for calculating the decarbonization amount from an image of the object, calculating the decarbonization amount of the object based on the feature, and displaying the calculation result of the decarbonization amount on the terminal. According to this, it becomes possible to ensure the accuracy of calculating the decarbonization amount.

[0098] In a 13th aspect, a support device includes an acquisition unit that acquires an image of an object for which a decarbonization amount is to be calculated, which is captured by a terminal equipped with a camera, and a relay unit that causes a recognition unit to recognize features used for calculating the decarbonization amount from the image of the object. According to this, it becomes possible to ensure the accuracy of the calculation of the decarbonization amount.

[0099] In a 14th aspect, a program causes a computer to acquire an image of an object for which a decarbonization amount is to be calculated, which is captured by a terminal equipped with a camera, and to cause a recognition unit to recognize features used for calculating the decarbonization amount from the image of the object. According to this, it becomes possible to ensure the accuracy of the calculation of the decarbonization amount.

Explanation of Signs

[0100] 1 Server, 2 Terminal, 3 Support device, 4 AI server, 11 Processing unit, 12 Storage unit, 13 Communication unit, 21 Processing unit, 22 Storage unit, 23 Communication unit, 24 Touch panel, 25 Camera, 26 GNSS receiver, 31 Acquisition unit, 32 Relay unit, 33 Calculation unit, 34 Notification unit, 39 Image storage unit, 41 Recognition unit, 49 Model storage unit, 100 Support system

Claims

1. A terminal equipped with a camera; A server capable of communicating with the terminal; Equipped with The terminal captures an image of an object for which a decarbonization amount is to be calculated, and transmits the image of the object to the server; The server, an acquisition unit that acquires an image of the object transmitted by the terminal; A recognition unit that recognizes features used to calculate the decarbonization amount from the image of the object; Equipped with The terminal displays a calculation result of the decarbonization amount of the object based on the characteristics, The recognition unit recognizes the suitability of the object as the feature, The decarbonization amount is not calculated when the eligibility is equal to or lower than a predetermined value. Support system.

2. The recognition unit recognizes the feature related to the amount of the object, The decarbonization amount is calculated according to the amount, The support system according to claim 1 .

3. the object is a bottle, The recognition unit recognizes the capacity of the bottle and the suitability of the bottle as a portable water bottle, The carbon dioxide removal amount is the carbon dioxide removal amount by using the bottle as the portable water bottle. The support system according to claim 1 .

4. The object is food waste, The recognition unit recognizes the weight or volume of the food waste and the suitability of the food waste for composting, The amount of carbon removal is the amount of carbon removal by treating the food waste with the compost. The support system according to claim 1 .

5. The object is a lunch box container, The recognition unit recognizes whether the lunch container is waste, The amount of carbon dioxide removed is the amount of carbon dioxide removed by using the lunch container that is not classified as waste. The support system according to claim 1 .

6. The object is a container containing waste oil, The recognition unit recognizes the capacity of the container and its suitability for collection of the waste oil, The amount of decarbonization is the amount of decarbonization by recovery of the waste oil. The support system according to claim 1 .

7. The object is a food product, The recognition unit recognizes the eligibility of the food as a donation food, The carbon dioxide reduction amount is the carbon dioxide reduction amount by donating the food to a food drive. The support system according to claim 1 .

8. The object is a meter reading slip, The recognition unit recognizes the usage amount for the target period and the usage amount for the past period described on the meter reading slip, The decarbonization amount is a decarbonization amount corresponding to the difference between the usage amount in the target period and the usage amount in the past period. The support system according to claim 1 .

9. The terminal includes an acquisition unit that acquires location data of the terminal, The object is fresh food, The recognition unit recognizes a place of origin of the fresh food from a label attached to the fresh food, The carbon dioxide removal amount is a carbon dioxide removal amount based on a comparison between the location of the terminal and the place of origin of the fresh food. The support system according to claim 1 .

10. The terminal adds a tag representing a type of the object to the image of the object; the recognition unit recognizes the feature by using a model corresponding to the tag from among a plurality of models prepared for each type of the object; The support system according to claim 1 .

11. An image of the object for which the amount of decarbonization is to be calculated is captured by a terminal equipped with a camera, Recognizing features used to calculate the decarbonization amount from the image of the object; Calculating the decarbonization amount of the object based on the characteristics; Displaying the calculation result of the decarbonization amount on the terminal. A method for assisting a user by a computer, comprising: The recognition step includes recognizing the suitability of the object as the characteristic; The decarbonization amount is not calculated when the eligibility is equal to or lower than a predetermined value. How to help.

12. An acquisition unit that acquires an image of an object for calculating a decarbonization amount captured by a terminal equipped with a camera; A relay unit that causes the recognition unit to recognize features to be used for calculating the decarbonization amount from the image of the object; Equipped with The recognition unit recognizes the suitability of the object as the feature, The decarbonization amount is not calculated when the eligibility is equal to or lower than a predetermined value. Support equipment.

13. Acquiring an image of an object for calculating the amount of decarbonization captured by a terminal equipped with a camera; and causing a recognition unit to recognize features to be used for calculating the decarbonization amount from an image of the object; on the computer, The recognition unit recognizes the suitability of the object as the feature, The decarbonization amount is not calculated when the eligibility is equal to or lower than a predetermined value. program.

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