Patent Issued for Sustainable resources exchange method and system (USPTO 11830070): Accenture Global Solutions Limited

2023 DEC 18 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Kumar, Mithilesh (New Delhi, IN), Raj, Sahil (Babhniyawan, IN), Tiwari, Saumya (Etawah, IN), filed on August 25, 2021, was published online on November 28, 2023, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11830070 is assigned to Accenture Global Solutions Limited (Dublin, Ireland).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Industrial waste produced by chemical companies is often a byproduct of a process meant to produce a particular product. This waste is mostly disposed of in landfills or incinerated, and only a small percentage is recycled or composted. When manufacturers try to recycle chemical byproducts, they usually either float tenders or use informal channels to trade off their industrial grade wastes or byproducts to a party in need. However, this process is not regularized. Thus, the large majority of industrial waste-which can be readily or with minimal processing re-used-goes unnoticed and its value is destroyed.

“Chemicals management covers sourcing, transport, storage, use, production and management of occupational health and environmental risks throughout a material’s life cycle. Successful outcomes in chemicals waste management ultimately require industry and regulators to develop workable policies, robust assessment methods and sound risk management measures. Unfortunately, due to the depletion of fossil fuels, the emerging effects of CO₂ emissions, and the rising demands for energy, there is a growing need for alternative waste management processes. Beyond industrial waste, there are also agricultural waste, biological waste, municipal sewage sludge (MSS), municipal solid waste (MSW), and shredder residue being produced. Due to the limited amount of space available for landfill use and the increasing costs of hazardous waste disposal, an alternative solution is needed. Although a number of waste management methods are currently employed, they are either impractical, generate further pollution, mainly cover only solid waste, are not regulated, or are too costly in terms of energy and economics. For example, existing methods fail to provide end-to-end services such as listing, search, purchase, intelligent process matching, etc. Matching a buyer and seller based on a chemical byproduct available by the seller and need of buyer is particularly difficult with chemical byproducts because the byproducts may need to go through further processing to produce the product needed by the seller. This means that it may not be immediately apparent that a buyer has a byproduct available that would be of value to a seller, making the buyer and seller a good match for an exchange. In addition, conventional marketplace portals generally allow only listing of the products, while the sales are executed through agreements which are conducted outside the portal. There is also a discouraging lack of transparency on inclusion of sustainability as a factor for product recommendations in current waste exchange paradigms.

“There is a need in the art for a system and method that addresses the shortcomings discussed above.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “A system and method for identifying suppliers in a marketplace for the exchange of industrial byproducts that promotes compliance with sustainability goals and facilitates the implementation of a circular economy is disclosed. The system and method solve the problems discussed above by providing an intelligent system that is designed to bring together consumers and suppliers of waste products across industries. The system is designed to operate automatically with little human intervention during sale or purchase of the products. The automated architecture includes a recommendation engine for identifying suppliers that match a buyer’s specifications and are aligned with minimum sustainable waste management practices. In particular, the disclosed system and method can match a seller of a byproduct with a buyer when it is not immediately obvious that the byproduct can be further processed to yield the product desired by the buyer.

“In one aspect, the disclosure provides a method of identifying suppliers in a marketplace for the exchange of industrial byproducts. The method includes a first step of receiving, from a first consumer and at a cloud-based trading system, a search request for a first product, and a second step of mining data from a profile of the first consumer in order to obtain process capability data for the first consumer. A third step includes generating a process dictionary specific to the first customer based on the process capability data, the process dictionary including at least a first process, and a fourth step includes automatically identifying, at the trading system and with reference to the process dictionary, a first conversion option for the first product by which the first product can be obtained from a second product via the first process. The method further includes a fifth step of generating, via a recommendation engine of the trading system, a list including either or both of: direct suppliers offering the first product, or indirect suppliers offering the second product. In addition, the method includes a sixth step of displaying the list via a buyer dashboard for the trading system wherein the list indicates whether the supplier is a direct supplier or an indirect supplier, and the required process for converting the second product to the first product is indicated for indirect suppliers.

“In another aspect, the disclosure provides a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to: (1) receive, from a first consumer and at a cloud-based trading system, a request for a first product; (2) mine data from a profile of the first consumer in order to obtain process capability data for the first consumer; (3) generate a process dictionary specific to the first customer based on the process capability data, the process dictionary including at least a first process; (4) automatically identify, at the trading system and with reference to the process dictionary, a first conversion option for the first product by which the first product can be obtained from a second product via the first process; (5) generate, via a recommendation engine of the trading system, a list including either or both of: direct suppliers offering the first product, or indirect suppliers offering the second product; and (6) display the list via a buyer dashboard for the trading system wherein the list indicates whether the supplier is a direct supplier or an indirect supplier, and the required process for converting the second product to the first product is indicated for indirect suppliers.

“In another aspect, the disclosure provides a system for identifying suppliers in a marketplace for the exchange of industrial byproducts, the system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to: (1) receive, from a first consumer and at a cloud-based trading system, a request for a first product; (2) mine data from a profile of the first consumer in order to obtain process capability data for the first consumer; (3) generate a process dictionary specific to the first customer based on the process capability data, the process dictionary including at least a first process; (4) automatically identify, at the trading system and with reference to the process dictionary, a first conversion option for the first product by which the first product can be obtained from a second product via the first process; (5) generate, via a recommendation engine of the trading system, a list including either or both of: direct suppliers offering the first product, or indirect suppliers offering the second product; and (6) display the list via a buyer dashboard for the trading system wherein the list indicates whether the supplier is a direct supplier or an indirect supplier, and the required process for converting the second product to the first product is indicated for indirect suppliers.

“Other systems, methods, features, and advantages of the disclosure will be, or will become, apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description and this summary, be within the scope of the disclosure, and be protected by the following claims.”

The claims supplied by the inventors are:

“1. A computer-implemented method for applying machine learning to identify latent features of suppliers for an exchange of industrial by-products, the method comprising: receiving, at a recommendation system, a dataset including, for multiple suppliers, their energy usage, carbon intensity, green practice certification, greenhouse gas emission, available manufacturing process capabilities, and available chemical compounds; receiving, at the recommendation system, labeled data that includes data patterns representing correspondences between a desired chemical compound and manufacturing process capabilities that can be used to convert other chemical compounds to the desired chemical compound; training a recommendation machine learning (ML) model for the recommendation system on the data patterns using a random-forest algorithm to determine a recommendation to present to a user in response to a set of feature values selected by the user, wherein the recommendation includes a transaction that has minimum energy emissions based on a distance between a supplier and the user; receiving, from a first user and at the trained recommendation ML model, a set of feature values including a first chemical compound and manufacturing process capabilities available to the first user; generating, via the trained recommendation ML model, a matrix identifying one or more potential suppliers, available chemical compounds that can be converted to the first chemical compound, and manufacturing process capabilities available to each of the potential suppliers, or to the first user, that can be used to convert each of the available chemical compounds to the first chemical compound; calculating, for each of the one or more suppliers, a sustainability score based on the supplier’s energy usage, carbon intensity, and greenhouse gas emission; determining, via a decision tree algorithm of the trained recommendation ML model, whether the sustainability score for each supplier is greater than a selected classification threshold; filtering the matrix to exclude suppliers whose sustainability score is below the classification threshold; and presenting on a computing device, via a user interface of the recommendation system and based only on the suppliers included in the filtered matrix, a recommendation to the first user including either or both of: direct suppliers offering the first chemical compound, or indirect suppliers offering another chemical compound that can be converted to the first chemical compound via manufacturing process capabilities available to the indirect supplier and/or the first user.

“2. The method of claim 1, further comprising: receiving, from the first user and at the recommendation system, one or more product fulfillment parameters as part of the set of feature values; and further filtering, via the recommendation system, the matrix to include only suppliers that match the first user’s product fulfillment parameters.

“3. The method of claim 2, wherein the product fulfillment parameters include one or more of industry type, supplier company rating, location of supplier, distance from the first user to supplier, manufacturing processes available to the supplier, cost per tonne, minimum order quantity, and types of waste.

“4. The method of claim 1, wherein the sustainability score for a supplier is calculated using a first calculation based on the supplier’s green practice certification and energy usage, and a second calculation based on the supplier’s carbon intensity and greenhouse gas emission.

“5. The method of claim 1, wherein the one or more suppliers include a first supplier, and the method further comprises: calculating, at the recommendation system, a first sustainability score for the first supplier; determining, at the recommendation system, that the first sustainability score is below the selected classification threshold; and excluding, at the recommendation system, the first supplier from the recommendation.

“6. The method of claim 5, further comprising displaying, at a supplier dashboard of the recommendation system for the first supplier, an alert showing the first sustainability score compared to a target sustainability score.

“7. The method of claim 5, further comprising displaying, at a supplier dashboard of the recommendation system for the first supplier, an alert describing the exclusion, and one or more recommendations for improving their sustainability score.

“8. The method of claim 1, further comprising presenting in the recommendation, one or more industry-specific trends related to re-usability of by-products in order to promote a circular economy.

“9. The method of claim 1, wherein the recommendation includes information about each supplier’s sustainability.

“10. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to apply machine learning to identify latent features of suppliers for an exchange of industrial by-products by performing the following: receive, at a recommendation system, a dataset including, for multiple suppliers, their energy usage, carbon intensity, green practice certification, greenhouse gas emission, available manufacturing process capabilities, and available chemical compounds; receive, at the recommendation system, labeled data that includes data patterns representing correspondences between a desired chemical compound and manufacturing process capabilities that can be used to convert other chemical compounds to the desired chemical compound; train a recommendation machine learning (ML) model for the recommendation system on the data patterns using a random-forest algorithm to determine a recommendation to present to a user in response to a set of feature values selected by the user, wherein the recommendation includes a transaction that has minimum energy emissions based on a distance between a supplier and the user; receive, from a first user and at the trained recommendation ML model, a set of feature values including a first chemical compound and manufacturing process capabilities available to the first user; generate, via the trained recommendation ML model, a matrix identifying one or more potential suppliers, available chemical compounds that can be converted to the first chemical compound, and manufacturing process capabilities available to each of the potential suppliers, or to the first user, that can be used to convert each of the available chemical compounds to the first chemical compound; calculate, for each of the one or more suppliers, a sustainability score based on the supplier’s energy usage, carbon intensity, and greenhouse gas emission; determine, via a decision tree algorithm of the trained recommendation ML model, whether the sustainability score for each supplier is greater than a selected classification threshold; filter the matrix to exclude suppliers whose sustainability score is below the classification threshold; and present on a computing device, via a user interface of the recommendation system and based only on the suppliers included in the filtered matrix, a recommendation to the first user including either or both of: direct suppliers offering the first chemical compound, or indirect suppliers offering another chemical compound that can be converted to the first chemical compound via manufacturing process capabilities available to the indirect supplier and/or the first user.

“11. The non-transitory computer-readable medium storing software of claim 10, wherein the instructions further cause the one or more computers to: receive, from the first user and at the recommendation system, one or more product fulfillment parameters as part of the set of feature values; and further filter, via the recommendation system, the matrix to include only suppliers that match the first user’s product fulfillment parameters.

“12. The non-transitory computer-readable medium storing software of claim 11, wherein the product fulfillment parameters include one or more of industry type, supplier company rating, location of supplier, distance from the first user to supplier, manufacturing processes available to the supplier, cost per tonne, minimum order quantity, and types of waste.

“13. The non-transitory computer-readable medium storing software of claim 10, wherein the sustainability score for a supplier is calculated using a first calculation based on the supplier’s green practice certification and energy usage, and a second calculation based on the supplier’s carbon intensity and greenhouse gas emission.

“14. The non-transitory computer-readable medium storing software of claim 10, wherein the one or more suppliers include a first supplier, and the instructions further cause the one or more computers to: calculate, at the recommendation system, a first sustainability score for the first supplier; determine, at the recommendation system, that the first sustainability score is below the selected classification threshold; and exclude, at the recommendation system, the first supplier from the recommendation.

“15. The non-transitory computer-readable medium storing software of claim 14, wherein the instructions further cause the one or more computers to display, at a supplier dashboard of the recommendation system for the first supplier, an alert showing the first sustainability score compared to a target sustainability score.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent, see: Kumar, Mithilesh. Sustainable resources exchange method and system. U.S. Patent Number 11830070, filed August 25, 2021, and published online on November 28, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11830070)&db=USPAT&type=ids

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