Description:Field of the Invention

The present subject relates to the field of waste management systems, particularly to an online application platform designed for the environmentally responsible disposal of electronic waste (e-waste).
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
After China and the US, India is now the country that produces the most e-waste. The fact that the informal sector handles more than 95% of the waste simply makes matters worse. A report from the Central Pollution Control Board states that India produced 1,014,961.2 tons of e-waste for 21 different types of EEE during the 2019–2020 fiscal year.
In the segment of waste management, particularly electronic waste (e-waste), systems have been developed to address the growing concern of environmentally sound disposal methods. Historically, such disposal has been managed through various means, including municipal collection, private recycling facilities, and electronics retailer take-back programs. However, the systems of prior art have been fraught with several inefficiencies and limitations.
A common drawback of conventional e-waste management systems has been the lack of convenience for the user. Said systems often require individuals to physically transport their e-waste to collection centers, which may not be readily accessible or may only accept waste during limited hours. The inconvenience can result in improper disposal of e-waste, such as inclusion with general household waste, leading to environmental pollution and loss of recyclable materials.
Moreover, the prior art has frequently lacked mechanisms for providing immediate and accurate value assessment of e-waste, resulting in the undervaluation of such waste and a disincentive for consumers to participate in environmentally responsible recycling practices. The absence of real-time pricing, especially for e-waste in varying conditions, has further contributed to the inefficiency of existing systems.
The methods employed by traditional e-waste management systems for collecting and processing waste have also been suboptimal. Logistics in prior art systems have not been optimized for routing efficiency, often leading to increased carbon emissions and higher operational costs. Such systems have also not taken full advantage of technological advancements that could streamline the collection and recycling process.
Additionally, prior art in the field of e-waste management has not effectively capitalized consumer incentives. The distribution of profits derived from the recycling of e-waste has been either non-existent or opaque, providing little to no financial return or incentive to the consumer. The lack of incentive has contributed to lower consumer engagement and participation rates in e-waste recycling programs.
In light of said drawbacks, there is a significant need for an improved e-waste management system that addresses the aforementioned inefficiencies. None of the prior art system provides a convenient, user-friendly platform for consumers to dispose of their e-waste. Further, said system lacked advanced pricing algorithms to ensure that consumers receive fair compensation for their e-waste, thereby encouraging more sustainable disposal practices.
None of the prior art system incorporated optimized logistics for the collection and transportation of e-waste, leveraging technology to reduce environmental impact and operational costs. Moreover, said prior art system lacked transparency in calculation and distribution of a portion of the profits gained from the recycling of e-waste back to the consumer.
Thus, there exists a need in the art for an e-waste management and incentive system that would represent a substantial improvement over prior art, providing benefits to consumers, recyclers, and the environment alike. Fulfilment of said need would facilitate the responsible disposal of e-waste and also foster a circular economy where all stakeholders are incentivized to engage in and support sustainable waste management practices.

Summary
The present subject relates to the field of waste management systems, particularly to an online application platform designed for the environmentally responsible disposal of electronic waste (e-waste).
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
In the contemporary landscape of waste management, the disposal of electronic waste (e-waste) has emerged as environmental challenge. Conventional methods for e-waste disposal have been inefficient, often resulting in the underutilization of valuable materials and adverse environmental impacts. To address said challenges, an electronic waste management and incentive system has been developed, comprising a series of interconnected modules designed to facilitate the responsible disposal of e-waste and incentivize user participation through financial rewards.
The core of the system is founded on an interface module, accessible via a web application. Said interface module is configured to receive information from customers about their e-waste. Said interface module serves as the initial point of interaction where customers can input data regarding the type, quantity, and condition of their e-waste.
Operatively connected to the interface module is a data management module, which performs the function of storing and categorizing the received e-waste information. The categorization is based on several criteria, essential for the accurate assessment and subsequent processing of the e-waste.
Critical to the valuation of e-waste is the pricing module, which incorporates a state-of-the-art Deep Learning Convolutional Neural Network (CNN) algorithm. Said pricing module is tasked with determining the value of the e-waste in real-time by analyzing data pertaining to the type, quantity, and condition. The immediacy and accuracy of the process are pivotal in providing instant price quotes, enhancing user engagement by offering transparent and fair compensation for their e-waste.
A logistics module, in communication with the interface module, is responsible for the coordination of e-waste pickup and transportation. The module ensures the efficient collection of e-waste by working in conjunction with third-party recyclers, selected based on various criteria including location, recycling capabilities, and adherence to environmental regulations.
Integral to the system is the profit distribution module, which links to both the pricing and logistics modules. The module calculates the profits generated from the recycling process and allocates a share to the customers, directly proportionate to the volume and value of e-waste they have contributed. Such a mechanism not only promotes sustainable disposal practices but also provides a tangible financial return to the users.
Further enhancements to the system include the ability for customers to schedule pickups, track the status of their e-waste disposal, and gain insights into the incentive earnings. The interface module is augmented with a photo upload feature, which, in conjunction with the CNN algorithm, refines the accuracy of price estimations. The system is also equipped with a route optimization feature within the logistics module, ensuring the most efficient collection routes are employed.
Thus, the system presents a solution to e-waste management by integrating technological advancements with a customer-centric incentive model. The system is tailored to encourage responsible recycling practices, reduce environmental impact, and economically benefit the participating customers, thus representing a significant advancement over prior art in the field of e-waste management.
In contemporary environmental management, the disposal of electronic waste (e-waste) presents significant challenges due to the rapid obsolescence of electronic devices and the toxic nature of their components. A method has been developed to not only streamline the disposal of e-waste but also to offer incentives to customers who engage in such environmentally responsible actions. The method comprises a series of structured steps executed by an integrated system.
Initially, e-waste information is received from customers through a web application. The first step involves the collection of details about the e-waste, such as type, quantity, and condition, directly from the customer. Subsequently, the received e-waste information is stored and categorized within a data management module. The categorization process is essential for organizing the e-waste data in a manner that facilitates the subsequent valuation and logistical steps.
Once the e-waste information has been categorized, the price of the e-waste is determined. The valuation is executed by employing a Convolutional Neural Network (CNN) algorithm within a pricing module. The sophisticated CNN algorithm analyses the categorized e-waste data, assessing factors including the material composition, market demand, and condition of the e-waste, to ascertain the monetary value. Said analysis step provides the basis for the financial incentives offered to customers.
The method further includes the coordination of the pickup and transportation of the e-waste. Such coordination is managed by a logistics module, which interfaces with selected third-party recyclers. The recyclers are chosen based on criteria such as proximity, recycling capabilities, and compliance with environmental standards. The step ensures that the e-waste is transported from the customer to the recycling facilities in an efficient and environmentally sound manner.
Finally, profits generated from the recycling of the e-waste are calculated. The profit distribution module is responsible for the calculation, taking into account the value of the recycled materials in the market. A portion of said profits is then distributed to the contributing customers. Such distribution is based on the amount and type of e-waste provided, thereby directly incentivizing customers to participate in the e-waste recycling program.
The method addresses the dual objectives of enhancing the recycling of e-waste and providing financial returns to those who contribute to the recycling effort. By employing advanced algorithms for pricing and sophisticated logistics management, the method significantly improves upon traditional e-waste disposal methods. The inclusion of a profit-sharing model further distinguishes the method, encouraging broader participation and fostering a more sustainable approach to e-waste management.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 depicts electronic waste (e-waste) management and incentive system, in accordance with the embodiments of the present disclosure.
FIG. 2 depicts method for managing e-waste disposal and customer incentives, in accordance with the embodiments of the present disclosure.
FIG. 3 represents sequence diagram web-application enabling the seller to upload the e-waste details and the e-waste recycler collects e-waste from a warehouse and perform recycle activities, in accordance with the embodiments of the present disclosure.
FIG. 4 represents flowchart of process for electronic waste (e-waste) management through a web portal, involving user interaction, image processing using artificial intelligence, and the eventual distribution of generated revenue, in accordance with the embodiments of the present disclosure.

Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
The present subject relates to the field of waste management systems, particularly to an online application platform designed for the environmentally responsible disposal of electronic waste (e-waste).
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The current disclosure pertains to a system 100 that leverages a web-based interface to facilitate the collection, categorization, and recycling of e-waste, while also providing an incentive model that rewards users monetarily for their responsible disposal actions. Said disclosure incorporates the approach to e-waste management by integrating advanced deep learning algorithms to accurately appraise e-waste value based on various attributes, including type, quantity, and condition. The system provides a seamless process for users to schedule e-waste pickup and track the recycling process, further contributing to the circular economy by ensuring the e-waste is appropriately handled and processed.
Furthermore, the disclosure encompasses a profit-sharing mechanism that calculates and distributes a portion of the revenue generated from recycled materials back to the users, thereby incentivizing the continued participation in the e-waste management program. The dual approach of combining technological aspects with an incentive structure is designed to encourage broader user engagement and promote sustainable environmental practices in the disposal of electronic waste. The system thus addresses a need for enhanced e-waste recycling efforts, offers a platform for efficient and transparent management of the e-waste disposal process, and presents the incentive framework that aligns the interests of consumers, recyclers, and environmental stewardship.
FIG. 1 depicts electronic waste (e-waste) management and incentive system, in accordance with the embodiments of the present disclosure.
In an embodiment, the electronic waste (e-waste) management and incentive system 100 encompasses several modules designed to facilitate the responsible disposal of e-waste and to incentivize users through financial rewards. According to a figurative elucidation of FIG. 1, showcasing an architectural setup of the system 100 that can comprise functional elements, yet not limited to an interface module 102, a data management module 104, a pricing module 106, a logistics module 108, and a profit distribution module 110, each of which is configured to interoperate within the system to streamline the process of e-waste recycling. A person ordinarily skilled in art would prefer those elements or components of the system 100, to be functionally or operationally coupled with each other, in accordance with the embodiments of present disclosure.
In an embodiment, the interface module 102 serves as the primary point of interaction with customers. The interface module 102 is accessible via a web application, which allows customers to input information regarding their e-waste, including but not limited to, type, quantity, and condition. The module 102 may include a user-friendly dashboard that guides customers through the data entry process and may also include multimedia upload capabilities, allowing customers to provide photographic evidence of their e-waste, thereby enhancing the accuracy of the data provided.
In an embodiment, the data management module 104 is operatively connected to said interface module 102. The module 104 is responsible for storing the information received from customers. Said management module 104 categorizes the information based on predetermined criteria, facilitating the subsequent valuation and logistical processing. The data management module 104 may utilize various database management systems and data structuring techniques to ensure the integrity and accessibility of the data.
In an embodiment, the pricing module 106, which is key to the system's operation, incorporates a Deep Learning Convolutional Neural Network (CNN) algorithm. The algorithm analyzes the data provided by customers in real-time to determine the pricing of e-waste. The CNN algorithm is trained on a dataset that includes images and known prices of e-waste items in various conditions, which allows the system to appraise the e-waste accurately. The pricing module offers instant price quotes to customers, which can be immediately communicated via the interface module 102.
In an embodiment, the logistics module's 108 function is to coordinate the pickup and transportation of e-waste. The module 108 interfaces with third-party recyclers, which are selected based on a set of criteria including geographic proximity, recycling capabilities, and environmental compliance. The logistics module may also feature route optimization algorithms that calculate the most efficient pickup routes, taking into account the locations and availability of both customers and recyclers.
Connected to both the pricing and logistics modules is the profit distribution module 110. After the e-waste has been recycled and profits have been realized, the module 110 calculates the share of profits to be distributed to customers. The distribution is based on the amount and type of e-waste provided by each customer, ensuring a fair incentive system. The profit distribution module may also provide a transparent accounting interface where customers can view the breakdown of profits earned and the distribution of incentives.
In an embodiment, the system may include additional features that enhance the functionality. For example, the interface module 102 may allow customers to schedule e-waste pickups and track the status of their e-waste disposal and incentive earnings. Moreover, the data management module 104 may maintain a historical record of each customer's e-waste contributions, prices received, and profits shared, thus fostering a long-term relationship between the system and the users. For example, the system's operation could include a customer logging into the web application and entering details of an old smartphone, including the model, condition, and any damage said smartphone has sustained.
In an embodiment, the customer uploads photos of the device, which the pricing module analyzes using the CNN algorithm. The customer is then provided with an instant price quote and opts to schedule a pickup. The logistics module arranges for a local recycler to collect the device, optimizing the pickup route based on the recycler's current schedule. Once the device is recycled, the profit distribution module calculates the revenue generated from the materials recovered and credits the customer's account with their share of the profits.
Referring to one or more preceding embodiments, the e-waste management and incentive system 100 described herein represents a significant advancement in the field of environmental management and recycling. By leveraging advanced technologies and incentive mechanisms, the system 100 encourages the proper disposal of e-waste and promotes sustainability.
FIG. 2 depicts method for managing e-waste disposal and customer incentives, in accordance with the embodiments of the present disclosure.
Disclosed method 200 provides an approach to managing electronic waste (e-waste) disposal, while also incentivizing customers for their participation in environmentally responsible recycling practices. The method 200 integrates a series of interconnected modules and steps, employing advanced technologies to streamline the e-waste disposal process.
Referring to a pictorial depiction put forth in FIG. 2, representing a flow chart of the method 200 that can comprise steps of, yet not restricted to, (at step 202) receiving e-waste information from a customer, (at step 204) storing and categorizing the received e-waste information, (at step 206) determining the price of the e-waste, (at step 208) coordinating pickup and transportation of the e-waste, and (at step 210) calculating and distributing profits to the contributing customer. Said steps of the method 200 can be performed or executed, collectively or selectively, randomly or sequentially or in a combination thereof, in accordance with the embodiments of current disclosure.
In an embodiment, the method 200 begins with customers accessing a web application designed to be user-friendly and intuitive. Through said web application, customers input detailed information regarding their e-waste, including the type of electronic device, the condition, and other relevant attributes. The information is vital for the subsequent steps in the method.
Once e-waste information is received, said information is stored and categorized in a data management module. The module organizes the information based on predefined categories such as type, size, material composition, and condition of the e-waste. Efficient data categorization facilitates accurate pricing and logistical planning.
In an embodiment, the pricing module within the method utilizes a Deep Learning Convolutional Neural Network (CNN) algorithm to determine the price of the e-waste. The CNN algorithm analyzes the categorized e-waste information, considering factors like market value, recycling potential, and condition. The module provides a dynamic pricing mechanism, offering real-time, fair market value for the e-waste.
In an embodiment, the logistics module is responsible for coordinating the pickup and transportation of the e-waste. The module interfaces with third-party recyclers, selecting the most appropriate based on geographic proximity, recycling capabilities, and environmental compliance standards. The logistics module ensures efficient and responsible handling of the e-waste from pickup to recycling.
In an embodiment, the profit distribution module calculates the profits generated from the recycling of the e-waste. A portion of said profits is then distributed to the contributing customers, proportionate to their contribution to the e-waste collected. The incentive mechanism encourages ongoing customer participation and promotes sustainable e-waste recycling practices. For instance, a customer may use the web application to input information about an old laptop, including the model, age, and any functional issues. The data management module categorizes the information, and the pricing module, utilizing the CNN algorithm, assesses the laptop’s value. The logistics module arranges for a local recycler to collect the laptop, optimizing the collection route. Once recycled, the profit distribution module calculates the revenue generated from the laptop's materials and credits a share of the profits to the customer's account.
Referring to one or more preceding embodiments, the method 200 outlined herein represents an approach to e-waste management, leveraging technology to enhance efficiency and sustainability while providing financial incentives to customers. The method significantly improves the recycling of electronic waste and contribute positively to environmental conservation efforts.
Referring to one or more preceding embodiments, some of the advantages of said system can be elucidated herein such as eco-conscious and accessible, rendering monetary rewards, collaborative efforts with recycling enterprises, employment opportunities, devoting 30% of earnings to sellers motivates participation through economic rewards. Further, the system invests 30% of the generated profits into the upkeep of the web application guarantees the ongoing enhancement and operational efficiency, offering a dependable and current interface for users.
Referring to one or more preceding embodiments, the web-based platform can be designed to incentivize customers to responsibly dispose of their electronic waste through the application, providing an alternative to mixing said waste with general refuse or selling to local shops. The system gathers pertinent details about the waste and partners with an external recycling service to manage the waste's transportation and processing. Additionally, the platform incorporates a mechanism for profit calculation, with proceeds being shared with the contributing customers.
The valuation of the e-waste on the platform is contingent upon the specific type and volume provided by each vendor. The Deep Learning Convolutional Neural Network (CNN) algorithm is utilized to ascertain the e-waste's value, taking into account the extent of damage to the item. The profits generated from the recycling process are also allocated to the sellers, rewarding them for their contributions to the recycling effort.
FIG. 3 represents sequence diagram web-application enabling the seller to upload the e-waste details and the e-waste recycler collects e-waste from a warehouse and perform recycle activities, in accordance with the embodiments of the present disclosure.
The sequence diagram presented in Figure 3 delineates the flow of web-application system, designated as 300, facilitates electronic waste (e-waste) management. This platform acts as an intermediary between sellers of e-waste and recycling entities. The process initiates with the seller, labeled as 101, who possesses e-waste and undertakes the action 103 to upload the details of the said e-waste onto the web application, denoted as 102.
Following the upload, the system employs a Deep Learning algorithm, represented by 104, which performs an analytical assessment of the uploaded images. This algorithm evaluates extent of damage to the e-waste products and subsequently approximating their value, as indicated by action 105.
Recyclers are then notified, via process 107, about the availability of e-waste within their vicinity. They proceed to collect the selected e-waste, as shown by action 109, and store it in their premises, depicted by 108. The collected e-waste is then transported to their facilities, as denoted by 110, which is followed by process 111.
The final stage of the flow involves the recycling of the e-waste, represented by 112, culminating in the generation of profits. This financial gain is then distributed among the involved parties: the recyclers receive a 40% share of the profit, as indicated by 116; the seller, 101, is allocated 30% of the profit, marked by 117; and the remaining 30% is directed towards the service provider and the maintenance of the web application, suggesting a sustainable and mutually beneficial business model.
FIG. 4 represents flowchart of process for electronic waste (e-waste) management through a web portal, involving user interaction, image processing using artificial intelligence, and the eventual distribution of generated revenue, in accordance with the embodiments of the present disclosure.
According to figurative illustration made in FIG. 4, showcases a flowchart that outlines a process for electronic waste (e-waste) management through a web portal, involving user interaction, image processing using artificial intelligence, and the eventual distribution of generated revenue. The process starts with users registering on the web portal, which could be designed to manage e-waste disposal. Said step creates a user account and profile that will be used to track the e-waste disposal and revenue-sharing process. After registration, users are expected to upload images of their e-waste along with a descriptive text. Said upload images step provides the necessary information for the subsequent damage assessment and pricing of the e-waste.
Referring to FIG. 4, by utilizing the uploaded images and descriptions, a Deep Learning Convolutional Neural Network (CNN) algorithm evaluates the condition of the e-waste. The CNN algorithm likely analyzes the extent of damage, which may influence the valuation and recycling process. In a next step, a third-party agent then verifies the validity of the submitted e-waste details, collects the e-waste from the user, and forwards the collected e-waste to a recycling facility. The step bridges the gap between the e-waste generator and the recycling process, ensuring that the e-waste is handled responsibly.
Referring to FIG. 4, after the e-waste has been recycled, the revenue generated from the process is shared among the stakeholders. Said stakeholders may include the original user who provided the e-waste, the third-party agents, and other parties involved in the platform or the recycling process. The flowchart outlines said system that not only encourages proper disposal of e-waste but also provides financial incentives to users who participate in the program. The use of a CNN algorithm for damage assessment indicates a technologically advanced approach to pricing e-waste, which could improve the accuracy of pricing models used by the platform. The final step ensures that all parties benefit from the successful recycling of e-waste, which could incentivize the adoption of such a platform.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claims:

An electronic waste (e-waste) management and incentive system, comprising:

an interface module is configured to provide a customer interface accessible via a web application for inputting e-waste information by customers;
a data management module is operatively connected to the interface module, wherein the data management module is configured to store and categorize e-waste information based on type, quantity, and condition;
a pricing module incorporates a Deep Learning Convolutional Neural Network (CNN) algorithm to determine pricing of e-waste in real-time by analyzing the type, quantity, and condition data, wherein the pricing module being operatively connected to the data management module;
a logistics module is operatively connected to said interface module, wherein the logistics module is configured to coordinate e-waste pickup and transportation arrangements with a third-party recycler; and
a profit distribution module is operatively connected to the pricing module and said logistics module, wherein the profit distribution module is configured to calculate profits from the recycling process and disburse a portion of said profits to customers based on their contribution to the e-waste collected.
2. The system of claim 1, wherein the interface module is further configured to allow customers to schedule e-waste pickups and track the status of their e-waste disposal and incentive earnings.
3. The system of claim 1, wherein the pricing module utilizes the CNN algorithm to generate instant price quotes for the e-waste based on the condition of the product, including the extent of breakage and functionality.
4. The system of claim 1, wherein the logistics module selects the third-party recycler based on a set of criteria including geographic proximity, recycling capability, and environmental compliance.
5. The system of claim 1, wherein the profit distribution module is further configured to provide a transparent accounting interface for customers to view the breakdown of profits earned and the corresponding distribution of incentives.
6. The system of claim 1, wherein the data management module is further configured to maintain a historical record of each customer's e-waste contributions, prices received, and profits shared.
7. The system of claim 1, wherein the interface module further comprises a photo upload feature enabling customers to upload images of their e-waste, which the pricing module uses to enhance the accuracy of the price estimation.
8. The system of claim 7, wherein said Deep Learning Convolutional Neural Network (CNN) algorithm is trained on a dataset comprising images and known prices of various conditions of e-waste items to improve pricing estimations.
9. The system of claim 1, wherein the logistics module includes a route optimization feature that determines the most efficient pickup routes for the third-party recycler based on pickup locations and times requested by customers.
10. A method for managing e-waste disposal and customer incentives, the method comprising the steps of: receiving e-waste information from a customer via a web application; storing and categorizing the received e-waste information in a data management module; determining the price of the e-waste using a CNN algorithm within the pricing module based on the categorized information; coordinating pickup and transportation of the e-waste via a logistics module with a selected third-party recycler; and calculating and distributing profits to the contributing customer through a profit distribution module based on the recycled e-waste.ONLINE APPLICATION FOR CUSTOMERS TO INCENTIVIZE E-WASTE DISPOSAL

An electronic waste (e-waste) management and incentive system streamlines the disposal and recycling of e-waste via a web application. The system features an interface module that allows customers to submit detailed information about their e-waste, including type, quantity, and condition. An integrated data management module categorizes said information by employing a deep learning Convolutional Neural Network (CNN) algorithm and assess the information about e-waste. A logistics module coordinates the collection of e-waste, partners with third-party recyclers to ensure efficient and environmentally friendly disposal. A profit distribution module calculates the proceeds from the recycling operations and allocates a share of the profits to the contributing customers.

Fig. 1

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FIG. 3
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FIG. 4
, Claims:I/We claims:

An electronic waste (e-waste) management and incentive system, comprising:

an interface module is configured to provide a customer interface accessible via a web application for inputting e-waste information by customers;
a data management module is operatively connected to the interface module, wherein the data management module is configured to store and categorize e-waste information based on type, quantity, and condition;
a pricing module incorporates a Deep Learning Convolutional Neural Network (CNN) algorithm to determine pricing of e-waste in real-time by analyzing the type, quantity, and condition data, wherein the pricing module being operatively connected to the data management module;
a logistics module is operatively connected to said interface module, wherein the logistics module is configured to coordinate e-waste pickup and transportation arrangements with a third-party recycler; and
a profit distribution module is operatively connected to the pricing module and said logistics module, wherein the profit distribution module is configured to calculate profits from the recycling process and disburse a portion of said profits to customers based on their contribution to the e-waste collected.
2. The system of claim 1, wherein the interface module is further configured to allow customers to schedule e-waste pickups and track the status of their e-waste disposal and incentive earnings.
3. The system of claim 1, wherein the pricing module utilizes the CNN algorithm to generate instant price quotes for the e-waste based on the condition of the product, including the extent of breakage and functionality.
4. The system of claim 1, wherein the logistics module selects the third-party recycler based on a set of criteria including geographic proximity, recycling capability, and environmental compliance.
5. The system of claim 1, wherein the profit distribution module is further configured to provide a transparent accounting interface for customers to view the breakdown of profits earned and the corresponding distribution of incentives.
6. The system of claim 1, wherein the data management module is further configured to maintain a historical record of each customer's e-waste contributions, prices received, and profits shared.
7. The system of claim 1, wherein the interface module further comprises a photo upload feature enabling customers to upload images of their e-waste, which the pricing module uses to enhance the accuracy of the price estimation.
8. The system of claim 7, wherein said Deep Learning Convolutional Neural Network (CNN) algorithm is trained on a dataset comprising images and known prices of various conditions of e-waste items to improve pricing estimations.
9. The system of claim 1, wherein the logistics module includes a route optimization feature that determines the most efficient pickup routes for the third-party recycler based on pickup locations and times requested by customers.
10. A method for managing e-waste disposal and customer incentives, the method comprising the steps of: receiving e-waste information from a customer via a web application; storing and categorizing the received e-waste information in a data management module; determining the price of the e-waste using a CNN algorithm within the pricing module based on the categorized information; coordinating pickup and transportation of the e-waste via a logistics module with a selected third-party recycler; and calculating and distributing profits to the contributing customer through a profit distribution module based on the recycled e-waste.ONLINE APPLICATION FOR CUSTOMERS TO INCENTIVIZE E-WASTE DISPOSAL