Methods and electronic devices for automated waste management

Embodiment herein discloses methods and devices for waste management by using an artificial intelligence based waste object categorizing engine. The method includes acquiring at least one image and detecting at least one waste object from the at least one acquired image. Additionally, the method determines that the at least one detected waste object matches with a pre-stored waste object and identifies a type of the detected waste object using the pre-stored waste object. Furthermore, the method includes displaying the type of the detected waste object based on the identification.

FIELD OF THE INVENTION

The present invention relates to waste management systems, and more particularly, to devices and methods that automate waste management.

BACKGROUND

Interpretation Considerations

This section describes technical field in detail and discusses problems encountered in the technical field. Therefore, statements in the section are not to be construed as prior art.

Discussion of History of the Problem

Common existing waste disposal systems include unclassified garbage collected from various places which are then manually separated at a waste disposal facility. The manual separation of solid waste brings health hazards for waste sorters as well as is less efficient, time consuming and not completely feasible due to the large quantity of waste disposed by modern households, business, and industry. To make a waste disposal system efficient, an automatic waste disposal system is needed for sorting, processing, crushing, compacting, and rinsing the waste using an identifier (e.g., barcode identifier, or the like).

In order to make this process efficient, various methods and systems have been introduced in the prior arts. U.S. patent Ser. No. 10/943,897 (Kline et al) discloses a waste material recovery and conversion center/power plant, to replace traditional trash transfer stations and landfills.

U.S. Pat. No. 7,269,516 (Brunner et al) discloses mining experiment information to identify pattern(s) from data measurement databases collected from observation.

U.S. patent Ser. No. 15/963,755 (Kumar et al) discloses a material sorting system that sorts materials utilizing a vision and/or x-ray system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification.

U.S. patent Ser. No. 16/177,137 (Horowitz et al) discloses systems for optical material characterization of waste materials using machine learning. Further, the U.S. patent Ser. No. 16/247,449 (Parr et al) discloses a system control for a material recovery (or recycling) facility.

However, in the prior arts, dating back over many decades, there is no automated method and system for the waste management that is accurate. Therefore, there is a long-felt need for an inventive approach that can overcome the limitations associated with conventional waste management techniques. In order to solve these problems, the present invention provides an automated device, system and method for waste management that is fast and accurately reliable.

SUMMARY

The present invention discloses an artificial intelligence based method for an automatic waste management.

In a first aspect of the invention, a method for a waste management is disclosed. The method includes acquiring at least one image. Additionally, the method includes detecting at least one waste object from the at least one acquired image. Further, the method includes determining that the at least one detected waste object matches with a pre-stored waste object, identifying a type of the detected waste object using the pre-stored waste object, and displaying the type of the detected waste object based on the identification.

In one embodiment, the method further includes notifying the type of the detected waste object to a user.

In an alternative preferred embodiment, the pre-stored waste object is generated by acquiring a waste object dataset comprising a waste object with various categories, acquiring a portion of an image corresponding to the waste object from the acquired waste object dataset, training the portion of the image corresponding to the waste object using a machine learning model, and generating the pre-stored waste object based on the trained portion of the image corresponding to the waste object.

In an embodiment, detecting the at least one waste object from the at least one acquired image includes identifying the at least one waste object from the at least one acquired image, extracting the at least one identified waste object from the at least one acquired image by processing a foreground portion of the at least one acquired image and a background portion of the at least one acquired image, determining at least one feature parameter based on the extraction, analyzing a pixel or pixels corresponding to the at least one identified waste object based on the determined feature parameter, and detecting the at least one waste object from the at least one acquired image based on the analyzed pixel(s).

In yet another embodiment, identifying the type of the detected waste object using the pre-stored waste object includes determining whether multiple types of the detected waste object are detected, and performing one of: in response to determining that multiple types of the waste object is not detected, identifying the type of the detected waste object using at least one feature parameter, and in response to determining that multiple types of the waste object is detected, determining at least one feature parameter based on the at least one identified waste object, analyzing a pixel or pixels corresponding to the at least one identified waste object based on the determined feature parameter, and detecting the at least one waste object from the at least one acquired image based on the analyzed pixel(s).

In alternative embodiments, the feature parameter comprises a shape of the waste object, a color of the waste object, an intensity of the waste object.

In a second aspect of the present invention, an electronic device for an automatic waste management is disclosed. The electronic device includes a processor coupled to a memory, and an artificial intelligence based waste object categorizing engine coupled to the processor. The artificial intelligence based waste object categorizing engine is configured to acquire at least one image, detect at least one waste object from the at least one acquired image, and determine that the at least one detected waste object matches with a pre-stored waste object. The artificial intelligence based waste object categorizing engine is also configured to identify a type of the detected waste object using the pre-stored waste object, and may display the type of the detected waste object based on the identification.

DESCRIPTION OF AN EXEMPLARY PREFERRED EMBODIMENT

Interpretation Considerations

While reading this section (Description of An Exemplary Preferred Embodiment, which describes the exemplary embodiment of the best mode of the invention, hereinafter referred to as “exemplary embodiment”), one should consider the exemplary embodiment as the best mode for practicing the invention during filing of the patent in accordance with the inventor's belief. As a person with ordinary skills in the art may recognize substantially equivalent structures or substantially equivalent acts to achieve the same results in the same manner, or in a dissimilar manner, the exemplary embodiment should not be interpreted as limiting the invention to one embodiment.

The discussion of a species (or a specific item) invokes the genus (the class of items) to which the species belongs as well as related species in this genus. Similarly, the recitation of a genus invokes the species known in the art. Furthermore, as technology develops, numerous additional alternatives to achieve an aspect of the invention may arise. Such advances are incorporated within their respective genus and should be recognized as being functionally equivalent or structurally equivalent to the aspect shown or described.

A function or an act should be interpreted as incorporating all modes of performing the function or act, unless otherwise explicitly stated. For instance, sheet drying may be performed through dry or wet heat application, or by using microwaves. Therefore, the use of the word “paper drying” invokes “dry heating” or “wet heating” and all other modes of this word and similar words such as “pressure heating”.

Unless explicitly stated otherwise, conjunctive words (such as “or”, “and”, “including”, or “comprising”) should be interpreted in the inclusive and not the exclusive sense.

As will be understood by those of the ordinary skill in the art, various structures and devices are depicted in the block diagram to not obscure the invention. In the following discussion, acts with similar names are performed in similar manners, unless otherwise stated.

The foregoing discussions and definitions are provided for clarification purposes and are not limiting. Words and phrases are to be accorded their ordinary, plain meaning, unless indicated otherwise.

In the following detailed description of embodiments of the invention, numerous specific details are set forth in order to provide a thorough understanding of the embodiment of invention. However, it will be obvious to a person skilled in the art that the embodiments of invention may be practiced with or without these specific details. In other instances, well known methods, procedures and components have not been described in detail so as not to unnecessarily obscure aspects of the embodiments of the invention.

Furthermore, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions and equivalents will be apparent to those skilled in the art, without parting from the spirit and scope of the invention.

In a preferred embodiment, the present invention provides an artificial intelligence based waste object categorizing engine that is in selected embodiments custom designed (and may thus employ a using a custom designed and captured training model), and that is created from a machine learning method called deep learning method. The machine learning enables the artificial intelligence based waste object categorizing engine to automatically learn and improve from experience without being explicitly programmed.

The deep learning method uses networks capable of learning in an unsupervised fashion from data that is unstructured or unlabeled. The deep learning method employs multiple layers of neural networks that enable the artificial intelligence based waste object categorizing engine of the present invention to teach itself through inference and pattern recognition, rather than development of procedural code or explicitly coded software algorithms. The neural networks are modeled according to the neuronal structure of a mammal's cerebral cortex, wherein neurons represented as nodes and synapses represented as uniquely weighted paths between the nodes. The nodes are then organized into layers to comprise a network. The neural networks are organized in a layered fashion that includes an input layer, intermediate or hidden layers, and an output layer.

The neural networks enhance their learning capability by varying the uniquely weighted paths based on their received input. The successive layers within the neural network incorporates a learning capability by modifying their weighted coefficients based on their received input patterns. The training of the neural networks is very similar to how we teach children to recognize an object. The neural network is repetitively trained from a base data set, where results from the output layer are successively compared to the correct classification of the image.

In an alternate representation, any machine learning paradigm instead of neural networks can be used in the training and learning process.

FIG. 1is a block diagram of an electronic device100for waste management. The electronic device (100) can be, for example, but not limited to a smart sort artificial intelligence (AI) bin system, a smart bin wastage sort device, a smart waste separator, a smart phone, a smart internet of things (JOT) device, a smart server, or the like.

In one embodiment, the electronic device includes a processor102, a communicator104, a display106, a memory108, an artificial intelligence based waste object categorizing engine110, an imaging unit112, and a sensor114. Although physical connections are not illustrated, the processor102is communicatively-coupled with the communicator104, the display106, the memory108, the artificial intelligence based waste object categorizing engine110, the imaging unit112, and the sensor114in any manner known in the electronic arts.

The imaging unit112can be, for example but not limited to a standalone camera, a digital camera, a video, camera, infra-red (IR) or ultra-violet (UV) camera or the like. The sensor114can be, for example but not limited to a distance sensor, a fill level sensor, an electronic scale, strain gauges or the like.

In one embodiment, the imaging unit112acquires at least one image and shares the at least one acquired image to the artificial intelligence based waste object categorizing engine110. In one example, the camera captures real-time digital images (e.g., RGB images or the like) or near real-time 2-dimensional digital images or continuous stream of digital images and adds a geo-tag to the acquired images, where the images may include multiple subjects. The multiple subjects include a user's hand on a waste object, the waste object on a tray, a background portion along with the acquired images. In another example, the digital camera captures a waste image and the sensor detects useful feature information from the waste image, then the digital camera and the sensor transfers the information to the artificial intelligence based waste object categorizing engine110.

After receiving the at least one acquired image, the artificial intelligence based waste object categorizing engine110detects at least one waste object from the at least one acquired image. In an example, the artificial intelligence based waste object categorizing engine110processes continuous streams of the digital images or the acquired images to produce properly cropped images containing only the waste objects and minimal background for contextual understanding and increasing probability certainty related to the waste object.

In an alternative embodiment, the artificial intelligence based waste object categorizing engine110is configured to identify the at least one waste object from the at least one acquired image. Additionally, the artificial intelligence based waste object categorizing engine110is configured to extract the at least one identified waste object from the at least one acquired image by processing a foreground portion of the at least one acquired image and a background portion of the at least one acquired image. Based on the extraction, the artificial intelligence based waste object categorizing engine110is configured to determine at least one feature parameter. The feature parameter can be, for example but not limited to a shape of the waste object, a color of the waste object, an intensity of the waste object, an IR-detectable or UV-detectable image, a texture information of the of the waste object or the like.

In an example, the artificial intelligence based waste object categorizing engine110utilizes connected-component information corresponding to the acquired images to divide the image into pixels and detect foreground that are not part of a primary item of interest in the foreground image. This results in a bounding box around a main waste object to remove portions of other objects in the raw acquired image and processes the raw acquired images using AI algorithms or vision computer algorithms. Further, the artificial intelligence based waste object categorizing engine110creates the feature values representing how each pixel responded to the AI algorithms or the vision computer algorithms.

Based on the determined feature parameter, the artificial intelligence based waste object categorizing engine110is configured to analyze a pixel or pixels corresponding to the at least one identified waste object. Based on the analyzed pixel(s), the artificial intelligence based waste object categorizing engine110is configured to detect the at least one waste object from the at least one acquired image.

After detecting the at least one waste object from the at least one acquired image, the artificial intelligence based waste object categorizing engine110is configured to determine that the at least one detected waste object matches with a pre-stored waste object.

In an embodiment, the pre-stored waste object is generated by acquiring a waste object dataset comprising a set of waste object along with various categories, acquiring a portion of the image corresponding to the each set of waste object from the acquired waste object dataset, training the portion of the image corresponding to the each set of the waste object using a machine learning model306, and generating the pre-stored waste object based on the trained portion of the image corresponding to the waste object. The machine learning model306is explained in conjunction with theFIG. 3.

By using the pre-stored waste object, the artificial intelligence based waste object categorizing engine110is configured to identify a type of the detected waste object. In an example, the artificial intelligence based waste object categorizing engine110is configured to identify the type of the detected waste object using a machine learning classifier or a filter. The type can be, for example, but not limited to a recyclable type, a trash type, a compost type, or the like. In an example, the images correspond to a glass, a cardboard, a metal, a paper, a Styrofoam, a food then, recycling type waste will be a glass, straws, aluminum and the trash type waste will be Styrofoam, coffee cups.

In an alternative embodiment, the artificial intelligence based waste object categorizing engine110is configured to determine whether multiple types of the detected waste object are detected. Alternatively, if multiple types of the waste object are not detected, the artificial intelligence based waste object categorizing engine110is configured to identify the type of the detected waste object using the at least one feature parameter.

In another embodiment, if multiple types of the waste object are detected, the artificial intelligence based waste object categorizing engine110determines the at least one feature parameter based on the at least one identified waste object, analyzes the pixel or pixels corresponding to the at least one identified waste object based on the determined feature parameter, and detects the at least one waste object from the at least one acquired image based on the analyzed pixel(s).

Based on identifying the type of the detected waste object, the artificial intelligence based waste object categorizing engine110is configured to display the type of the detected waste object on the display106. The display106can be, for example, but not limited to, an information display, a LED display, an LCD display or the like.

Further, the artificial intelligence based waste object categorizing engine110is configured to notify the type of the detected waste object to a user using the communicator104. The communicator104can be, for example, but not limited to, a Bluetooth communicator, a Wireless fidelity (Wi-Fi) communicator, a light fidelity (Li-Fi) communicator or the like. In an example, the notification is provided in the form of a visual alert through an audio using a speaker, LED's and on-screen messaging. In another example, the notification is provided in the form push messages to the user.

Further, the memory108comprises stored instructions, the instructions causing the artificial intelligence based waste object categorizing engine110to perform functions on the at least one image when executed by the at least one processor102. The imaging unit112is connected with the processor102via the communicator104including a wired communication means or a wireless communication means such as, but not limited to, Bluetooth, near field communication, Wi-Fi, universal serial bus, or the like.

In an embodiment, if the images are colored images, then the artificial intelligence based waste object categorizing engine110utilizes to add extra information in order to assist in higher accuracy pixel classification. The accuracy of the artificial intelligence based waste object categorizing engine110is directly proportional to the quality of the images. The image resolution provides most effective classification of individual pixels and overall objects yet to be tested in various lighting conditions, backgrounds and variable scenarios. The camera image capture must be continuous (i.e., from point of detection to point of disposal). The images must be well lit, not distorted and as unobtrusive as possible.

Further, the artificial intelligence based waste object categorizing engine110uses multiple techniques including clustering, and a KNN classifier, but other classifiers can be used within the scope of the invention.

The communicator104is configured to communicate with internal units and with external devices via one or more networks or a second electronic device (illustrated in theFIG. 2). The memory108may include one or more computer-readable storage media. Accordingly, the memory108may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard disc, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory108may, in some examples, be considered a non-transitory storage medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted that the memory108is non-movable.

AlthoughFIG. 1shows various units of the electronic device100, it is understood by those of skill in the art upon reading this disclosure that other embodiments are not limited thereon. In other embodiments, the electronic device100may include less or more number of various units. Further, the labels or names of the various units are used only for illustration purpose and does not limit the scope of the invention. One or more units can be combined together to perform same or substantially similar function to manage the waste.

FIG. 2is a block diagram of a system200for waste management. In one embodiment, the system200includes a first electronic device100aand a second electronic device100b. The first electronic device100atransfers the at least one image to the second electronic device100bin real-time, in near real-time, or in a recorded format. After receiving the at least one image from the first electronic device100a, the second electronic device100bperforms the various operations to manage the waste. The operations and functions of the second electronic device100bare previously explained in conjunction with theFIG. 1.

FIG. 2shows the limited overview of the system200but, it is readily understood to those of skill in the art upon reading this disclosure that other embodiments are not so limited. Further, the system200can include any number of hardware or software components communicating with each other.

FIG. 3is a block diagram of the artificial intelligence based waste object categorizing engine110included in the electronic device100for waste management. In one embodiment, the artificial intelligence based waste object categorizing engine110includes an artificial intelligence model302, a classifier304, and a machine learning model306. Additionally, the artificial intelligence model302includes a box generator302aand a shape identifier302b. The classifier304can be, for example, but not limited to a k-nearest neighbors (KNN) classifier. The machine learning model306can be, for example but not limited to, a supervised learning and deep learning based learning model and multilayer hybrid deep-learning based learning model.

In an embodiment, the machine learning model306is configured to classify the waste objects in the raw images into high level groups such as metals, glass, cardboard, paper, Styrofoam, food, plastic, etc. to direct, reward, educate and align context with content. The artificial intelligence model302requires training examples prior to classification, allows the machine learning model306to associate specific combinations of object vectors with specific classes types. The result of this stage of the artificial intelligence model302during runtime operation is an overall classification for the object based on the configured categories. The waste object will be deposited based on the classification. Additionally, objects not falling under current classifications will be fed into the machine learning routine, described inFIG. 7andFIG. 8, to further train the system and expand on possible classification brackets.

The box generator302aoutputs a set of bounding boxes related to the waste information, where each bounding boxes defines the location, size and category label of the waste object. The box generator302agenerates a clear boundary for a physical characteristics corresponding to the waste object. In an example, an icon size and an icon share are visually varied based on an intensity of the physical characteristics corresponding to the waste object. The shape generator302boutputs predicted shapes and intensity of the physical characteristics corresponding to the waste object. The box generator302aand the shape generator302bcan operate individually either in series or parallel or as a single entity. The classifier304classifies the pixel value features into classes using an unsupervised learning model.

In an embodiment, a framework performs a machine learning procedure to train the classifier306using a training image pixel dataset. The classifier304is applied to image pixel(s) to identify one or more different pixels, which may then be corrected. The artificial intelligence model302and the machine learning model306receive one or more training image datasets from a reference imaging system. Alternatively, the artificial intelligence model302and the machine learning model306may use or incorporate corrob architecture, training and implementation to “teach”, modify and implement identification of waste materials.

In another embodiment, the artificial intelligence model302uses a convolutional neural network (CNN)-based technique to extract the features corresponding to the image and a multilayer perceptron technique to consolidate image features to classify wastes as recyclable, trash, compost or the others. The multilayer perceptron technique is trained and validated against the manually labelled waste objects. Further, the artificial intelligence model302acts as a response center to classify the waste object by consolidating information collected from the imaging unit112.

In another embodiment, the machine learning model306can be a layer based neural network (e.g., 4 layer deep learning network, 5 layer neural network or the like) and train it for a predictive analysis. For example, the 4 layer based neural network has 32, 16, 10 and 4 nodes at each level for achieving deep learning for the waste object prediction. The predict function will pass feature vector set to the neural network and produce the output as seen inFIG. 4. As shown in theFIG. 4, the layers between a first layer (i.e., input layer) and a last layer (i.e., output layer) are called as hidden layers. All layers are used to process and predict the waste object. In another example, 4 layer neural network is used for waste object prediction in which last layer (i.e., output layer will have 6 nodes for predicting the waste object). In general, the neural network has 1stlayer including 32 nodes, 2ndlayer corresponding to 16 nodes, last layer including 5 nodes or 6 nodes for predicting the waste object.

In another embodiment, the machine learning model306is created by a tenser flow library. Initially, the machine learning model306builds a data set of m-set of waste objects which is created by getting and tagging information across Internet for the waste classification. Further, the machine learning model306extracts the features of the waste objects in the dataset. From the tenser flow library model, the machine learning model306will co-relate the waste object belongs to which category. In an example, the waste predicted through the machine learning model306are recyclable, trash, compost.

In an embodiment, the accuracy and the speed of the machine learning model306varies based on amount of raw dataset the machine learning model306is trained on. In another embodiment, the accuracy and the speed of the machine learning model306varies based on a frame rate, overall CPU power, a GPU power or the like.

FIG. 5is a flow chart500illustrating a method for waste management, in accordance with an embodiment of the present invention. The operations (502-512) are performed by the artificial intelligence based waste object categorizing engine110.

In act502the method comprises acquiring the at least one image. At act504, the method includes detecting the at least one waste object from the at least one acquired image. Then, in act506, the method includes determining that the at least one detected waste object matches with the pre-stored waste object. Next, in act508the method includes identifying the type of the detected waste object using the pre-stored waste object. At act510, the method includes displaying the type of the detected waste object based on the identification. And, in act512the method includes notifying the type of the detected waste object to the user.

The proposed method can be used to direct the user behavior for waste sorting using the AI based computer vision techniques. The proposed method can be used to evaluate and sort waste into desired categories, i.e., recyclables, trash and compost. The proposed method can be implemented in a trash disposal at many location (e.g., office spaces, apartments, recreational area, stadiums, home, public places, park, street cleaning, or the like). The proposed method can be used by a user (e.g., technicians, agriculture user, food court servant, pedestrian, or the like).

The proposed method can be used to capture the visual information of the user carrying the waste object to analyze and sort waste into the right stream and provide a visual alert (through LED's and on-screen messaging) or audio message to the user, so as to automatically sort waste disposed of by the user.

FIG. 6is an example flow chart600illustrating various operations for waste management.

Starting in act602the method includes capturing the image and adding the geo-tagging on the image. As an example, the camera captures the image and adds the geo-tagging on the image.

In an act604the method includes detecting and extracting the foreground object from the acquired image. As an example, the artificial intelligence based waste object categorizing engine110detects and extracts the main objects and sub-images from the acquired raw image and separates the background portion from the acquired raw image.

At act606, the method includes computing the feature value corresponding to the feature parameter for the pixel clarification associated with the acquired raw image. In an example, the artificial intelligence based waste object categorizing engine110computes the feature value corresponding to the feature parameter for the pixel clarification associated with the acquired raw image using the shape of the waste object and color of the waste object.

Then, in act608the method includes identifying the waste sub-parts using the pixel clarification. As an example, the artificial intelligence based waste object categorizing engine110identifies the waste sub-parts using the pixel clarification.

Next, in act610, the method again computes the feature value corresponding to the feature parameter for the pixel clarification. As an example, the artificial intelligence based waste object categorizing engine110again computes the feature value corresponding to the feature parameter for the pixel clarification.

At query612, the method can determine whether multiple waste objects are detected. If multiple waste objects are not detected then, in an act614, the method includes classifying the waste object. As an example, the artificial intelligence based waste object categorizing engine110may classify the waste object.

Then, at act616the method includes triggering the sensor114from the waste classification. As an example, the processor102triggers the sensor114for the waste classification.

Alternatively from the query612, if multiple waste objects are detected then, at an act618the method includes performing the feature clarification corresponding to the features values for multiple object detection. In an example, the artificial intelligence based waste object categorizing engine110performs the feature clarification corresponding to the features values for multiple object detection.

After act618, the method proceeds to act620which includes detecting and classifying the multiple objects based on the feature clarification. As an example, the artificial intelligence based waste object categorizing engine110detects and classifies the multiple objects based on the feature clarification.

FIG. 7is a flow chart illustrating various operations for creating the machine learning model306in conjunction withFIG. 5. The operations (702-706) are performed by the artificial intelligence model302.

The method700starts in act702which includes acquiring a raw dataset including the set of waste object along with various categories. Next, in an act704, the method includes acquiring the portion of the image corresponding to the waste object from the raw dataset. Then, in act706the method includes creating the machine learning model by using the acquired waste object information. The machine learning model is trained based on a frame rate, overall CPU power, a GPU power, or the like.

FIG. 8is a flow chart illustrating various operations for training and maintaining the machine learning model306in connection with theFIG. 5, in accordance with an embodiment of the present invention. The operations (802-806) are performed by the artificial intelligence model302.

First, in an act802the method includes acquiring the portion of the image corresponding to the waste object from the raw dataset. Next in an act804the method includes labelling the main object within the waste objects. Then, in act806the method includes training and maintaining the machine learning model based on the labelled main object. The labelled main object includes multiple class of the images corresponding to the waste object.

Simultaneous reference is made toFIG. 9andFIG. 10, which are perspective views of a smart bin wastage sort device100c, that incorporate the above teachings of the invention. The smart bin wastage sort device100cis an example of an electronic device100. Specifically, substantial operations and functions of the electronic device100are previously explained in conjunction with theFIG. 1toFIG. 8.

As shown in theFIG. 9andFIG. 10the smart bin wastage sort device (100c) includes a bin housing (116), a smart bin back panel (118), a collection can (120), a bin housing door (122), a bin housing lid with an opening (124), a digital camera (112a), an information display (106a), a distance sensor (114a), a speaker (126), an optical indicator (128), a fill level sensor (114b), an electronic scale (130), a strain gauges (114c) (Shown inFIG. 11), the processor (102) (Shown inFIG. 12), a power supply (132) (Shown inFIG. 12), a power distribution board (134) (Shown inFIG. 12), a mounting plate (136) (Shown inFIG. 12), a visual indicator (138) for direction (Shown inFIG. 13), a digital camera array (112b) for wider field of vision (Shown inFIG. 13), and a wide screen information display (106b) (Shown inFIG. 13). The device shown is preferably sized for home or public use, such as in an airport, sports facility (such as a stadium, for example), school or office location such as a hallway, break room, or restroom, for example.

The bin housing (116) includes the collection can (120) for collecting all types of waste material. The smart bin back panel (118) is attached with a top portion of the bin housing (116), and covers the top portion of the bin housing (116). The bin housing door (122) is provided with the bin housing (116), and the bin housing (116) includes the bin housing lid with the opening (124) for accessing and keeping the waste in the collection can (120).

The digital camera (112a) captures the image of the waste and the information display (106a) displays the type of the waste. The distance sensor (114a) measures the distance between the user and the smart bin wastage sort device (100a). The speaker (126) informs the type of the waste to the user.

As shown in more detail inFIG. 10, the optical indicator (128) indicates the type of the waste to the user and the fill level sensor (114b) measures the level of the waste stored in the collection can (120). The electronic scale (130) is provided in bottom of the collection can (120).

FIG. 11is a partial sectional view of the collection can120included in the smart bin wastage sort device100c. As shown in theFIG. 11, the strain gauges (114c) measures the weight of the waste stored in the collection can (120). The processor (102) is coupled with various elements e.g., the collection can (120), the bin housing door (122), the bin housing lid with the opening (124), the digital camera (112a), the information display (106a), the distance sensor (114a), the speaker (126), the optical indicator (128), the fill level sensor (114b), the electronic scale (130), and the strain gauges (114c)) in the smart bin wastage sort device (100a).

FIG. 12is a perspective view of the smart bin back panel118included in the smart bin wastage sort device100c, in accordance with an embodiment of the present invention. As shown in theFIG. 12, the power supply (132) supplies the power in the smart bin wastage sort device (100a) through the power distribution board (134). The mounting plate (136) is provided in the smart bin back panel (118).

FIG. 13is a perspective view of the smart bin wastage sort device100cincluding the visual indicator138, in accordance with an embodiment of the present invention. As shown in theFIG. 13, the visual indicator (138) indicates the direction to the user for waste disposal and the digital camera array (112b) is used for wider field of vision. The wide screen information display (106b) displays information related to the waste.

FIG. 14is schematic view of an example system in which the smart bin wastage sort device100ccommunicates with a smart phone100dfor waste management, in accordance with an embodiment of the present invention.

In one embodiment, the system includes the smart bin wastage sort device100cand the smart phone100d. The smart bin wastage sort device100ctransfers the at least one image to the smart phone100din real-time or in near real-time or in a recorded format. After receiving the at least one image from the smart bin wastage sort device100c, the smart phone100dperforms the various operations to manage the waste. The operations and functions of the smart phone100dare substantially explained in conjunction with theFIG. 1,FIG. 2andFIG. 9toFIG. 13.

Although the invention has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the invention. Upon reading this disclosure, changes, modifications, and substitutions may be made by those skilled in the art to achieve the same purpose the invention. The exemplary embodiments are merely examples and are not intended to limit the scope of the invention. It is intended that the present invention cover all other embodiments that are within the scope of the descriptions and their equivalents.

The methods and processes described herein may have fewer or additional steps or states and the steps or states may be performed in a different order. Not all steps or states need to be reached. The methods and processes described herein may be embodied in, and fully or partially automated via, software code modules executed by one or more general purpose computers. The code modules may be stored in any type of computer-readable medium or other computer storage device. Some or all of the methods may alternatively be embodied in whole or in part in specialized computer hardware. The systems described herein may optionally include displays, user input devices (e.g., touchscreen, keyboard, mouse, voice recognition, etc.), network interfaces, etc.

The results of the disclosed methods may be stored in any type of computer data repository, such as relational databases and flat file systems that use volatile and/or non-volatile memory (e.g., magnetic disk storage, optical storage, EEPROM and/or solid state RAM).

The networked electronic devices described herein may be in the form of a mobile communication device (e.g., a cell phone), laptop, tablet computer, interactive television, game console, media streaming device, head-wearable display, virtual or augmented reality device, networked watch, etc. The networked devices may optionally include displays, user input devices (e.g., touchscreen, keyboard, mouse, voice recognition, etc.), network interfaces, etc.