Patent Application
20220076326
Embodiments described herein relate generally to a system and method for automatically creating lots and pricing for auctions.
Historically, in the world of electronics supply chain, reverse logistics has been an afterthought. However, easy consumer returns and rapid product upgrade and turnover have elevated the role of reverse logistics in the electronics domain to a process with the potential to unlock the value of used, damaged and otherwise devalued inventory, saving the retailer millions of dollars a year. What is needed is a smart lotting and pricing system that will optimize the return on the disposition of returned items.
Worldwide secondary markets exist for electronic devices for both B2B and B2C channels. One such secondary market distribution that has the potential of providing a high revenue stream is the auction. Applicant has developed a novel, AI-driven auction platform that automatically determines optimal lot makeup and pricing that provides an organization with the greatest value for the devices that it has to auction or liquidate.
Embodiments of such an artificial intelligence-driven solution enable reverse logistics operations to determine the highest value disposition for a returned product and to configure lots and set pricing on those items suitable for auction or liquidation for maximum return. The system and method described allows the receiving process to be integrated with the reverse logistics supply chain to automatically create, price and publish lots for auction and communicate the contents of a lot to a warehouse management system for packing and shipping when the lot has been sold.
The platform provides inventory visibility across return facilities with flexibility in lot creation. It also applies pricing analytics such as minimum opening bids/reserve pricing and market-based pricing. It provides channel-based liquidation, including presales, traditional auction, spot buy and auction and salvage paths. The system and method described provides an integrated processing platform from sales order to shipping.
The features, functions and advantages that have been discussed may be achieved independently in various embodiments of the present disclosure or may be combined with yet other embodiments, further details of which can be seen with reference to the following description and drawings.
Embodiments of the claimed subject matter will now be described more fully with reference to the accompanying drawings, in which some, but not all embodiments are shown. The subject matter disclosed may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the disclosure may satisfy legal requirements. Like numbers refer to like elements throughout.
Where possible, any terms expressed in the singular form herein are meant to also include the plural form and vice versa, unless explicitly stated otherwise. Also, as used herein, the term “a” and/or “an” shall mean “one or more,” even though the phrase “one or more” is also used herein. Furthermore, when it is said herein that something is “based on” something else, it may be based on one or more other things as well. In other words, unless expressly indicated otherwise, as used herein “based on” means “based at least in part on” or “based at least partially on.” It should also be understood that while some embodiments describe the methods or products as comprising one or more elements, the methods or elements may also consist of or consist essentially of the elements disclosed herein.
A “user” as used herein may refer to any entity or individual associated with use of the system or method as described herein. A user may be a seller of products or an employee of such a user, or it may refer to an independent processor providing services for a seller. An operator may refer to an individual receiving and processing an item. A “module” is known to those of skill in the art as computer instructions stored in a server or computer's non-transitory memory, which when executed by a processor direct the specific actions performed by the machine. An “item” refers to a particular piece processed according to this disclosure, while a “product” refers to a class or type of item. The words “item” and “device” may be used interchangeably when discussing an exemplary embodiment.
As used herein, a “user interface” or “user portal” generally includes a plurality of interface devices and/or software that allow a user to input data, manage lots created by the system and view statistics. For example, a user interface may include a graphical user interface (GUI) or an interface to input computer-executable instructions that direct the processing device to carry out specific function. A “system interface” allows two separate systems to meet, interact with and exchange data. Input and output devices may include a display, mouse, keyboard, button, touchpad, touch screen, microphone, speaker, LED, light, joystick, switch, and/or other user input/output device for communicating with one or more users. A number of modules referred to in this disclosure are given the term “engine.” An engine is a part of the system that performs a core or essential function for other programs. Thus, a routing and value analysis engine and the AI based maximization engine perform essential computational functions for this system. In this disclosure, a “secondary market” may be any sales or dispositional opportunity for a returned item.
Unless specifically stated otherwise in context, it may be appreciated that terms such as “processing”, “detecting”, “determining”, “receiving”, or the like may refer to the action and/or processes of a computer or computing system, or similar device that manipulates or transforms data represented as physical quantities within the computing system's registers, memories or other data storage into other data similarly represented within the computing system. The embodiments are not limited in this context.
An automated lot and pricing system and method is described in terms of its use with electronic devices, such as mobile phones, tablets, etc., however, it may be used with any type of item for which secondary markets, such as auction and liquidation markets exist.
An automated lot configuration and pricing system and method provides an integrated auction platform for optimizing the disposition of returned or overstock electronic devices in the auction and refurbished item markets. It acts as a single point of service for a company to manage its auction and liquidation strategy. It automatically creates optimal lots with available devices and includes an auction value forecasting engine that uses multiple algorithms to forecast the selling price of the product in multiple channels and across multiple geographies. The system integrates multiple auction pipelines and provides the oversight over B2B and B2C liquidation processes, with smart listing capabilities to provide the best possible scenarios and routing for the greatest revenue extraction.
An exemplary embodiment of an automated lot configuration and pricing system is comprised of highly distributed components which provides the middleware orchestration of integration with a company's fulfilment and financial payments systems.
Referring to
Items returned by purchasers may be received at a warehouse, retail store, through a kiosk or similar receiving station 302. The receiving process may be broken down in various steps 302 with the sub-stations manned with operators each of whom process the items with a single service. Alternatively, a receiving station 302 may be available which performs a substantial number of services in place, with items conveyed when required. Items may be conveyed along receiving sub-stations 302 for performance of services. Each item receiving station 302 may be equipped with a terminal or computing device with access to a network. Operators may access a role-based user interface 304 allowing performance of different receiving functions according to function, and view dashboards related to management of the system.
An RMA analysis module 306 allows a user to access records related to a return, such as the RMA against which the item will be received and details regarding the transaction. The user may also conduct research on the item or an RMA. Generally, RMA information may be extracted by API or other data communication method, from an integration with a WMS, ERP 308, POS 310 or kiosk system in which the transaction was initiated. The item's entire history may be extracted and viewed when an item is serialized or is otherwise identifiable in the system.
Rules logic 312 and repository provide rules and data that drive the gateway (i.e. a dispositional path based on business rules or needs) or further disposition of an item. Rules may be based on the business and operational requirements of a reverse logistics organization. In an embodiment, an organization may have a plurality of sales channels running a plurality of programs with rules under which a product may be sold and returned. The product itself may have restrictions, for example, a model that is obsolete or is no longer in demand may take a different dispositional path than a current model. Similarly, an individual item may have rules attached to it based on various attributes. Rules may be complex and have one or more dependencies that determine an item disposition. A rule logic module 312 with memory and processor may apply rules at multiple layers of business and operational levels, for example the channel, program, product, item and transactional data levels. An item profile stored in a system database may be evaluated for applicable rules at any time during the process.
A warranty module 314 provides disposition according to original equipment manufacturers (OEM) or secondary rebuild or redistribution vendor warranties and/or supplier's policies. Rule logic and eligibility requirements may direct an item to the warranty module 314 for processing. Rules regarding warranty disposition may be complex; an item eligible for warranty may not be directed back to the warrantor if other rules take precedence. An exemplary warranty process is described in the exemplary embodiment below.
An inspection and cosmetic grading 316 sub-system may determine the physical/cosmetic state of an item and contribute to the item value determination for a secondary market. For example, cloth rips and tears, broken zippers, stains and missing notions on an item of apparel, or scratches and dents on a television screen greatly reduce the options for high value disposition. In addition to value determination, these attributes determine what kind of secondary processing may be required in order for the item to be resold in a particular secondary market. Cosmetic grading typically requires a machine with computer vision capabilities in order to determine a cosmetic rating for a device, which has an effect on the price at which a particular item may be disposed.
A product notification 318 system may be utilized for communications regarding the receiving process. As items are dispositioned, a label is printed and attached to the product and the product is directed to the appropriate intermediate or endpoint disposition bin or bucket.
As was described above, the system may be integrated with an enterprise resource planning system (ERP) 308 and/or a point of sale (POS) 310 or kiosk system which initiate sales and return transactions or are otherwise interfaced with the intelligent disposition system. In addition, the system may be integrated with a warehouse management system (WMS) 320, which may be both the source of data and the target of updates when the item receiving process is complete.
A specialty services module 322 comprises computer-executable instructions stored in memory, which, when executed by the processor directs the computer to perform any special services required for an item in order to determine its optimal dispositional value. Special services are generally product dependent and may generally be used to test the functional operation of the item or perform a service required for a particular product. As the items move through special services, results of each service are added to the item profile.
Finally, item profiles are processed through a value determination 324 module which determines the optimal disposition for the item. As the optimal disposition is determined, a label may be printed or marked with infrared ink, with item details and a physical location for disposition (for example, a number or location of a shipping tote for salvaged items) and affixed to the item. The item may then be physically conveyed to the appropriate location. The value determination module 324 may be operatively connected to a forecasting server 326 for determining the optimal value disposition of an item. Those items that are determined to have an optimal disposition of resale or auction, or similar disposition, may have their profile transmitted to the AI-based revenue maximization engine 108 where they may be apportioned to consumer resale, B2B resale/auction, and be priced, lotted, and placed in an auction for final disposition.
It will be apparent to those of ordinary skill that
Artificial intelligence methods, deep learning methods in particular, being highly scalable, accurate and adaptive to fluctuating behavior, provide superior results compared to programmatic/statistical methods of forecasting. The maximization engine 108 may use deep learning methods, such as recurrent neural networks, long short-term memory networks, gated recurrent unit networks and attention mechanisms (time series forecasting with machine learning), training the models on historical data to develop models for forecasting consumer and business demand, and for performing a sentiment analysis to determine a reputation score. The deep learning algorithms described here are mentioned by way of example and not limitation, and other appropriate algorithms may be used in their place.
In an exemplary embodiment, a revenue maximization engine 108 may use a number of machine learning or deep learning models, either alone or in combination, for forecasting inventory and price, performing sentiment analysis and lotting. Time series forecasting with deep learning algorithms may be used for determining inventory, which may be determined for sales as a whole or by sales channel. Time series algorithms typically are comprised of factors such as long-term trends, seasonality, stationarity (changes over time), noise such as random fluctuations or variations due to uncontrolled factors, and autocorrelation. An inventory forecast module 408 may train a time-series forecasting block using historically observed data, while taking into account significant events, launch of new items, holiday offers, etc. Referring to
The sentiment analyzer, or market-trend analyzer module 600, the components of which are illustrated in
Finally, an exemplary AI-based lot generation module 700 may use classical optimization techniques which optimizes unit quantity, profit margin and types of items in a lot. This module uses a metric based on the historical data from inventory, and auctions statistics for assessing that a generated lot is how likely to be sold. As is illustrated in
A user portal or interface may be provided for inventory, operations, reverse logistics and other personnel to manage and view the auction and liquidation processes from an item's entry into the system through receipt of payment for an auctioned lot. An exemplary user portal 102 is illustrated with the screen shots in
The system and method disclosed herein comprises a computing device and various hardware components and subsystems. A computing device may also be referred to as a computer or server. Software applications or modules, comprised of computer-executable instructions stored in computer-usable or computer-readable, non-transitory memory or non-transitory secondary storage for execution by a processor are operatively configured to perform the operations as described in the various embodiments. Any suitable computer-usable or computer-readable medium may be utilized. For example, and not limitation, the computer-usable or computer-readable medium may be an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus or device. The software applications may correspond with a single module or any number of modules. Modules of a computer system may be made from hardware, software, or a combination of the two. Generally, software modules are program code or instructions for controlling a computer processor to perform a particular method to implement the features or operations of the system. The modules may also be implemented using program products or a combination of software and specialized hardware components. In addition, the modules may be executed on multiple processors for processing a large number of transactions, if necessary or desired.
AI-based modules may be implemented from machine learning servers 2402, such as that illustrated in
Although the computer, computing device or server has been described with various components, it should be noted that such a computer, computing device or server can contain additional or different components and configurations. In addition, although aspects of an implementation consistent with the system disclosed are described as being stored in memory, these aspects can also be stored on or read from other types of computer program products or computer-readable media, such as secondary storage devices, a non-transitory carrier wave from the Internet or other network; or other forms of RAM or ROM. Furthermore, it should be recognized that computational resources can be distributed, and computing devices can be client or server computers. Client computers and devices (e.g.) are those used by end users to access information from a server over a network, such as the Internet or a local area network. These devices can be a desktop or laptop computer, a standalone desktop, or any other type of computing device. Servers are understood to be those computing devices that provide services to other machines, and can be (but are not required to be) dedicated to hosting applications or content to be accessed by any number of client computers. Operations may be performed from a single computing device or distributed across geographically or logically diverse locations.
Communications between subsystems may be driven by computing device executable code by some type of interface, such as ethernet, Bluetooth, USB, or other connection. Remote access by customers or users may be provided by web services or networks. A computing device may transmit network-based services requests to external systems networks via external networks. In various embodiments, external networks may encompass any suitable combination of networking hardware and protocols necessary to establish network-based communications between clients and service provider network. For example, a network may generally comprise various telecommunications networks and service providers that collectively implement the Internet. A network may also include private networks such as local area networks (LANs) or wide area networks (WANs) as well as public or private wireless networks. For example, both a given client and service provider network may be provisioned within enterprises having their own internal networks. In such an embodiment, a network may include the hardware (e.g., modems, routers, switches, load balancers, proxy servers, etc.) and software (e.g., protocol stacks, accounting software, firewall/security software, etc.) necessary to establish a link between a client and the Internet as well as between the Internet and a service provider network. It is noted that in some embodiments, clients may communicate with server provider network using a private network rather than the public Internet.
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other implementations will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
This patent application claims the benefit of U.S. Provisional patent application No. 63/076,173, filed Sep. 9, 2020 entitled “AUTOMATED LOT CONFIGURATION AND PRICING SYSTEM AND METHOD” which is incorporated herein by reference in its entirety and for all purposes. A claim of priority is made.
| Number | Date | Country | |
|---|---|---|---|
| 63076173 | Sep 2020 | US |
