Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/06—Buying, selling or leasing transactions

Definitions

• the present invention relates to a system and method for conducting trade activities and more particularly to a system and method for conducting trade activities electronically with the capability of achieving and optimizing complex trade objectives in the realm of electronic commerce.
• Procurement Organization A business or government agency may seek to perform a multi-million dollar procurement of various office supplies from a possibly large number of authorized suppliers.
• An example of a procurement objective is to minimize the total expenditure on the required quantities of office supplies, under the limitations of the allocated budget, and the maximal price per specific items the agency is ready to pay. It is desirable that the underlying E-commerce system would recommend the optimal trade, i.e., what items and in what quantities should be purchased from each authorized supplier and for what price. Buying each item from a supplier offering the minimal price per item may not be the best strategy, because of various deals, incentives and volume discounts that suppliers may be willing to offer.
• Supplier A computer hardware supplier offers a range of components and their configurations. One possible objective is maximizing its revenue, while maintaining at least a 17% profit margin, subject to limitations on the current inventory levels and capacity, and under the requirement that inventory turnover be at least 50% per month. Also, a supplier may be willing to offer numerous special deals and incentives to preferred volume buyers.
• a pharmaceutical manufacturer may seek to perform a complex transaction of selling a bundle of its products to a chain of drug stores, and, at the same time, purchasing a range of raw materials necessary to manufacture them. In doing so. the manufacturer may be trying to achieve the objective of maximizing the overall profit subject to the limitations on manufacturing production capacity, available manufacturing processes and the available cash.
• Surplus Seller An electronic device manufacturer may seek to eliminate useless surplus inventory. The objective here may be to maximize the sale price for the overall surplus, possibly selling it to more than one buyer.
• Internet-based electronic commerce services such as electronic malls and shops (e.g.,
• IMALL and Amazon.com electronic auctions (e.g., EBAY and Yahoo), and competitive shopping (e.g., PriceLine.com, using a reverse auction).
• EBAY electronic auctions
• competitive shopping e.g., PriceLine.com, using a reverse auction.
• Today, companies in that category mainly provide business-to-consumer and consumer-to-consumer services, but are also trying to expand into the business-to-business market. Products like IBM Net. Commerce and MS
• Site Server are suites of software productivity tools used to deploy a wide range of E- commerce solutions. However, they also lack the decision support capabilities necessary for achieving complex trade objectives.
• Current Internet-based trade systems only support simple trade objectives such as purchasing or selling specific items within a certain price range.
• EBAY allows the auctioning of specific items, i.e., iterative price-bids bounded by a floor price and a time deadline.
• IMALL supports selling specific products or services at a fixed price.
• PriceLine.com allows customers to bid their own price for a product or service, does comparative shopping and keeps the monetary difference.
• US Patent No. 4,903,201 discloses a computerized automated futures trading exchange.
• the traders in the exchange enter bids to purchase commodity contracts. They also enter offers to sell commodity contracts.
• the system automatically matches between bids and offers.
• the system automatically completes transactions between traders.
• the invention above lacks the capability to match an aggregation of partial bids to an aggregation of partial offers, where bids and offers are specified as ranges delimited by constraints.
• the trader lacks the capability to define an objective function and to perform optimization on the specified objective function.
• the invention above is limited to the futures markets.
• US Patent No. 5,077,665 discloses a matching system in which bids are automatically matched against offers for given trading instruments. Although the system provides match making between bids and offers of financial instruments, the system does not provide the trader the ability to specify objective function, to set constraints per specific financial instrument, and therefore to achieve a predefined business objective. The invention described therein is related only to financial markets and does not allow the user to specify other items for match making besides financial instruments.
• US Patent No. 5,283,731 discloses computer-based classified advertising. The system comprises a data processor and means for creating an advertising database available to each user in the system. The invention described therein restricts the matching capabilities to a single match and does not provide capabilities to perform optimization and to specify complex trading specifications, constraints and objectives.
• US Patent No. 5,710,887 discloses a computer system and method for electronic commerce.
• the system facilitates commercial transactions between a plurality of customers and at least one supplier of items over a computer driven network capable of providing communications between the supplier and at least one customer site associated with each customer.
• the system disclosed in the invention is suitable for a wide range of providers of goods and services, it does not posses the ability to specify particular items in a precise way, or to perform optimized match making.
• the invention described therein describes various business paradigms for electronic commerce, but does not allow performing "One-to-one" or "One-To-Many" electronic transactions based on optimized match making.
• the invention described therein does not allow specification of constraints on specific item parameters.
• US Patent No. 5,630,070 discloses the method for optimization of resources planning.
• the method described in the invention provides for an optimization of a manufacturing process by designating the amounts of various manufactured products to be produced.
• the method employs an objective function such as maximization of income in a situation where there are limitations on the inventory of raw materials and on the tools employed in the manufacturing process.
• the method does not allow specifying unique constraints on specific items participating in the manufacturing process.
• the method does not allow performing multiple transactions and does not allow performing match making of consumers * items with suppliers' items.
• the invention allows various traders to achieve optimal trade transactions.
• the ATS model allows to describe, in a precise and uniform way, trade parameters, constraints and objectives for a wide range of of traders, including procurement organizations, suppliers, manufacturers, resellers, surplus sellers, trade-in sellers, stock marker traders, general buyers and sellers, etc.
• Adaptive Trade Specification is a formal mathematical description of trader's objective and constraints, such as in the examples in the prior art section.
• ATS constraints include restrictions (on quantities, prices, totals, profits, revenues etc.) that must be satisfied to perform an optimal transaction, and the interconnection between various business parameters (such as profit, quantities, prices and costs).
• the core of each ATS is a specification of "items" the trader offers to GIVE as well as “items" to TAKE in return.
• a procurement organization may offer to GIVE the "item” money and wants to TAKE items of office supply.
• An office equipment supplier may have an ATS, in which all its catalog appears as GIVE items, and money as the only TAKE item.
• a manufacturer may have an ATS, in which all of its products appear as GIVE items, all raw materials and money (i.e., revenues for its products) as TAKE items.
• ATS is adaptive in that various numeric parameters such as quantities of items, prices, profit, revenue, totals etc. are not fixed, but could vary, provided that they satisfy the ATS constraints.
• Item specifications in an ATS are also constraint-based and not fixed.
• an ATS of a trader may include, as one of the TAKE item specifications, a hard disk that has at least 12 GB capacity and is compatible with a G7305E mother board; no exact model or vendor is necessary.
• the ATS model provides a uniform and expressive way to capture any conceivable trades that can be formulated in terms of given and taken items.
• a library of specialized wizards i.e., specialized "smart" interface templates
• the wizard would automatically construct an ATS from the user given set of trading parameters relevant to a trading scenario.
• the trader who uses a wizard would not need to understand the mathematical description of an ATS, but rather trading parameters and concepts that are familiar to the trader (e.g. availability, quantity, price, revenue, etc.).
• wizard library is described elsewhere in a complementary patent application cited above, and is not intended as a limitation on the present invention.
• MM optimization methods recommend specific transactions with other traders (i.e., against their ATS's) that are mutually agreeable and optimize the objective of the trader's ATS (e.g., minimal price, maximal profit etc.).
• the recommended set of transactions will indicate exactly with whom the transaction should be made, the exact GIVE and TAKE items and their quantities, as well as other relevant parameters (e.g., price and profit).
• the MM optimization methods recommend a set of suppliers' ATS's and the exact quantities of the items to be purchased from each, so that the procurement ATS objective, say the minimal total cost, is achieved.
• the MM optimization methods can recommend a set of ATS's of buyers interested in the manufacturer's products, and a set of ATS's of suppliers of raw materials, which are necessary to manufacture the products, so that the manufacturer's objective, say maximal profit, is achieved.
• the ATS-based match making and optimization are generic and work uniformly regardless of a specific wizard (or trader type) that generated them.
• Four exemplary MM optimization methods are set forth herein: 1.
• One-to-All MM optimization which has one optimizing ATS (i.e., whose objective is used for optimization) and which recommends a (multiple) transaction that may involve some or all of the committed ATS's; 3.
• One-to-One MM optimization which has one optimizing ATS and recommends a transaction that may involve exactly one committed ATS; and 4.
• One-to- K MM optimization where K is an integer number, which has one optimizing ATS and which recommends a multiple transaction that may involve K or less committed ATS's.
• FIG. 1 ATS-Based Trading Software System, describes a high level graphical summary of the suite of software tools related to the ATS-Based Trading Software System.
• FIG. 2 ATS-Based Match-Making and Optimization Hardware Architecture Diagram, describes a high level graphical summary of the hardware architecture of the system.
• FIG. 3 Item Specification and Adaptive Trade Specification (ATS) Class Diagram, presents a high level graphical summary of the Item Specification and Adaptive Trade Specification classes.
• FIGS. 4A-4E Functional Diagram of Match-Making and Optimization Method, present a high level graphical summary of five Mathematical Programming Optimization Methods used by the system.
• FIGS. 5A-5E Flow Charts of Specific Match-Making and Optimization Methods, present in greater detail the methods of Figs. 4A-4E. Detailed Description of the Preferred Embodiment
• An ATS-based electronic marketplace 101 can include one or more of an ATS-based electronic mall 103, an ATS-based electronic auction (forward or reverse) 105, and any other ATS-based commerce environment. As noted above, participants in the marketplace 101 form ATS's through various techniques.
• wizards 107 including one or more of a procurement wizard 109, a supplier wizard 1 11 , a manufacturing wizard 113, a surplus seller wizard 115, a reseller wizard 1 17, a generic buy and sell wizard 1 19, a generic buy wizard 121, a generic sell wizard 123, a trade-in wizard 125, and other wizards adapted to specific purposes.
• These wizards like those wizards that are known in the programming art, are utilities that guide a user through a specific task.
• the ATS's formed through use of the wizards 107 are input to the ATS match-maker 127, which uses matchmaking optimization methods to be described below.
• a Constraint Object Oriented Database (CSPACE) 129 uses an iterative query language (IQL) 131 and a constraint and optimization library 133 to perform the matchmaking and optimization.
• the CSPACE 129 communicates through an ODMG wrapper 135 with an ODMG-compliant database manager 137 and also communicates directly with a mixed integer programming (MIP) solver 139.
• MIP mixed integer programming
• the hardware architecture capable of running an ATS based match- making and optimization system includes several logical tiers, each one performing specific computational tasks. Each tier can be described in terms of specific tasks that it performs. From the hardware perspective, each tier can be built from computers having sufficient computational power.
• Tier 1 includes a database server 201, which is a power server machine (preferably dual or quad Pentium III machine) running one of the following network operating systems: Windows NT 4.0, Novell 5.0, UNIX.
• the database server 201 performs all tasks related to data persistency, data integrity and querying.
• the database server 201 runs one of the commercially available object oriented databases such as Poet, Objectivity, Object Store, etc.
• Tier 2 includes the application server 203, which is a power server machine (preferably dual or quad Pentium III machine) running one of the following network operating systems: Windows NT 4.0, Novell 5.0, UNIX etc.
• the application server 203 performs all tasks related to performing ATS-based match-making and optimization.
• the data are passed between layers via RMI , CORBA, DCOM or any other distributed computing protocol allowing remote method invocation and data transmission.
• Tier 3 includes a Web server 205, which is a computer that responds to requests from
• the Web server 205 transfers text files and corresponding graphics and data via HTTP to remote computers that are running Web browsers.
• the Web server 205 should have the functionality commonly associated with e-commerce Web servers, such as CGI (Common Gateway Interface) for performing searches and other dynamic HTML functions and SSL (Secure Socket Layer) for handling secure transactions.
• CGI Common Gateway Interface
• SSL Secure Socket Layer
• the servers 201, 203, and 205 communicate with another through an internal network. However, in order to be useful to users, the Web server 205 communicates via the
• the Web clients 209 running on users' premises and used as Web clients.
• the Web clients 209 are computers or other devices (such as WAP-enabled wireless devices) capable of running any standard off-the-shelf browser.
• the clients 209 run Web-based applications that will use information provided by the application server 203 and the Web server 205.
• ATS Adaptive Trade Specification
• Item-Specification is a class (i.e., a data structure with attached methods). Objects in this class (i.e., specific instances of the class data structure) can represent any "items" relevant in trade, such as material items (e.g., paper, electronic component, chemical), services (e.g., mail delivery, transportation, consulting time), money items (e.g., US dollars, French Francs etc.) or securities (e.g., stocks, bonds, etc.). Generally, an IS object may describe any "tradable item" that can have an associated quantity or amount. Many different implementations (i.e., in terms of exact attributes and methods) of the
• IS class are possible.
• the preferred embodiment provides two implementations. However, many other implementations are also possible, such as item specifications based on ontology hierarchies as well as a variety of emerging XML-based product description standards.
• the ATS model and the matchmaking optimization methods will work with any given IS class implementation, under the condition that the following binary Boolean function is also provided:
• the IS class contains the following attributes: 1. Non-Numeric-Properties, which are composed of: a. A set S of attribute names, e.g., "vendor”, “component-type”, “color”, “catalog ID” etc. b. A mapping that associates, with each attribute name in S, its corresponding value. For example, “supplier” can be mapped to "DGK”, “component-type” to “resistor”, “color” to "black”, and “catalog ID” to "Z123-74-A45".
• the Boolean function Give-Take-Item-Match(IS 1 , 1S2) is implemented as follows. It returns TRUE if and only if the following conditions hold: a. Every (non-numeric) attribute name Attr in IS2 appears also in ISI; and the value associated with Attr in ISI equals to the value associated with ⁇ 4ttr in IS2. b. Every (numeric) variable name Var in IS2 appears also in ISI; and the range associated with Var in ISI must contain the range associated with Var in IS2.
• ISI satisfies the requirements of IS2, and thus Give-Take-Item-Match(ISl ,IS2) must return TRUE.
• the IS class contains a single attribute Item-ID.
• the function GiveTakeItemMatch(ISl ,1S2) is implemented in such a way that it returns TRUE if and only if ISI and IS2 are identical.
• Give-Take-Item-Match defines a partial ordering, as required.
• An ATS is a class (i.e., a data structure with attached methods) that consists of the following attributes:
• Give-Item-Entries and Take-Item-Entries describe item specifications (IS) of items a trader is willing to give and take, respectively.
• Both Give-Item-Entries and Give-Item-Entries are of the same class (type) Item-Entries-Class, which has the following attributes: 1. A set Item-Specs of item specifications (IS). 2. A mapping that associates a quantity-range with each item specification (IS) in the set Item-Specs.
• Quantity [IS] Quantity
• Upper-bound[IS] must be either a non-negative numeric value or Infinity, meaning that no upper bound is requested.
• each quantity range has an indication whether the Quantity [IS] must be a integer (i.e., a whole number, such as 3 or 15) or any real number (e.g., 3.57 or 17.3894).
• the system must guarantee that object identifiers IS for each item specification is unique, and thus the corresponding variable Quantity[IS] is unique for that item specification.
• Constraints is an object of type Constraint-Class, which is a class (i.e., a data structure and attached methods) used to describe various mathematical restrictions on numeric parameters (variables) relevant to an ATS.
• Constraint-Class a class (i.e., a data structure and attached methods) used to describe various mathematical restrictions on numeric parameters (variables) relevant to an ATS.
• Total-Price 3.4 * Quantity [ISI] + ... + 15.7 * Quantity [ISn] is a constraint that defines the function Total-Price as the sum of all prices of the items ISI through ISn.
• Total-Price Unit-Price [ISI] * Quantity [ISI] + ... + Unit-Price [ISn] * Quantity [ISn] AND
• a set Vars of variable names (unknowns), such as Quantity [IS], Total-Price, Profit, Item-Price [IS] etc.
• a Boolean method Truth-Value. When applied to a Constraint object with argument of the class Variable-Instantiation, it returns a Boolean value TRUE or FALSE.
• Objective Objective is an object of the class Objective-Class, which has two attributes:
• Objective-Function which is a name of a parameter (variable) to be optimized (e.g., Profit, Total-Cost)
• FIG. 3 provides a high level graphical description of the classes Item Specification and Adaptive Trade Specification.
• An ATS class 301 includes four components: give-item- entries 303, take-item-entries 305, constraints 307 and an objective 309.
• the give-item- entries 303 identify what the particular user is willing to give in the trade and include one or more item specifications 31 1.
• the take-item-entries 305 identify what the user wants in return and include one or more item specifications 313.
• the constraints 307 set forth restrictions that must be satisfied before a transaction can be carried out, e.g., constraints on quantity or on time of delivery.
• the objective 309 indicates what the particular user wants to optimize; for example, a seller may want to optimize (maximize) profit, while a buyer may want to optimize (minimize) total cost.
• the MM Optimization methods recommend specific transactions with other traders (i.e., against their ATS's) that are mutually agreeable and optimize the objective of the trader's ATS (e.g., minimal price, maximal profit etc.).
• the recommended set of transactions will indicate exactly with whom the transaction should be made, the exact GIVE and TAKE items and their quantities, as well as other relevant parameters (e.g., price and profit).
• the MM optimization methods can recommend a set of suppliers ATS's and the exact quantities of the items to be purchased from each, so that the procurement ATS objective, say the minimal total cost, is achieved.
• the MM optimization methods can recommend a set of buyers ATS's interested in the manufacturer's products, and a set of ATS's suppliers of raw materials necessary to manufacture the products, so that the manufacturer's objective, say maximal profit, is achieved.
• the ATS-based match making and optimization are generic and work uniformly regardless of how or for what type of trader the input ATS's were generated (e.g., what "wizard" interface generated them).
• each of the methods returns as output an object Value-At-Point of the class Value-At-Point-Class, which has two attributes: 1. Optimal -Value (i.e., maximum or minimum) 2. Optimal-Variable-Instantiation, that is, a Variable-Instantiation that satisfies the Constraints, and at which the Optimum-Value is achieved.
• FIGS. 4A-4E provide a high level graphical description of the methods outlined below.
• Figs. 5A-5E provide corresponding low-level descriptions.
• Step 501 Construct Original- ATS-Constraints as Constraints o ⁇ Al AND Constraints of A2 AND AND Constraints of An.
• Step 503. Construct Give-Quantity-Constraints as follows: a. Initially, set Give-Quantity-Constraints to the empty conjunction (logical AND) of constraints, i.e. a constraint that is equivalent to TRUE. b. For each ATS A from the set ⁇ Al, ...,An) and
• Step 509 Construct the set All-Take-Item-Specs as follows: a. Set All-Take-Item-Specs to the empty set. b. For each ATS A from the input set ⁇ Al, ...,An ⁇ of ATS's do:
• Step 517 Construct Constraints as Original-Constraints AND Give-Quantity-Constraints AND Take-Quantity-Constraints AND Give-Zero-Sum-Constraints AND Take-Zero-Sum-Constraints Step 519. Return Constraints as output. End of Method. Generic Multiple MM Optimization Method (Figs. 4B and 5B)
• Input (409): 1. A set ⁇ Al, ..., An ⁇ of ATS's (411) 2. Objective of the class Objective-Class (recall: it includes an Objective-Function and an indication whether minimum or maximum is sought. (413)
• Construct MM-Constraints by applying the method Construct-ATS-MM- Constraints( ⁇ Al, ...,Anj) on the input set of ATS's ⁇ Al, ...,AnJ. Step 523. Construct Combined-Constraints as MM-Constraints AND Additional-Constraints Steps 525-529. If Objective indicates that minimum is sought (step 525), apply the method Minimize(Objective-Function, Combined-Constraints) (step 527) that returns the optimal Value-At-Point (Recall: it has the attributes Optimal-Value of the type Real and Optimal-Point of the class Variable-Instantiation-Class).
• Step 531 Initialize W inning- ATS-Set as ⁇ Al, ...,Anj. Step 533. For every ATS A in Winning-ATS-Set do: a. For every item specification IS in Give-Item-Entries of A do:
• Step 535 Return as output: a. Optimal-Variable-Instantiation which is the Variable-Instantiation which was returned in Value-At-Point. b. The Optimal-Value which was returned in Value-At-Point. c. Winning-ATS-Set End of method.
• Committed-ATS-Set which is a set of ATS's that are committed to perform a transaction 10 if and only if their Constraints are satisfied. The Objectives of the committed ATS's are not used in optimization. (431 ) Output (433):
• Step 541 Set ATS-Set to the union of Committed-ATS-Set and the singleton set ⁇ Optimizing-A TSj 5 Step 543.
• Step 547 Apply the method ATS-Multiple-MM-Optimization(ATS-Set, Objective, Additional-Constraints) to compute Optimal-Variable-Instantiation, Optimal-Value and Winning-ATS-Set.
• Step 549 Return Optimal-Variable-Instantiation, Optimal-Value and Winning- ATS-Set as output.
• Committed-ATS-Set which is a set of ATS's that are committed to perform a transaction if and only if their Constraints are satisfied. The Objectives of the committed ATS's are not used in optimization. (447) Output (449):
• Step 551. Set Current-Minimum to + infinity
• Step 553. Set Current-Variable-Instantiation to null (i.e., undefined).
• Step 555. Set Winning-ATS to null (i.e., undefined).
• Step 557 Apply ATS-Multiple-MM-Optimization on the set ⁇ Optimizing- ATS, A ⁇ of ATS ' s, the Objective of Optimizing-ATS, and the empty Additional- Constraints. Steps 559-565. If the returned Optimal-Value ⁇ Current-Minimum, as determined in step 559, do:
• Step 561 Set Current-Minimum to Optimal-Value; Step 563. Set Current-Variable-Instantiation to the returned Optimal- Variable-Instantiation. Step 565. Set Winning-ATS to the current ATS A.
• Step 567 Return as output: Winning- A TS
• Step 551. Set Current-Maximum to - infinity
• Step 553. Set Current-Variable-Instantiation to null (i.e., undefined).
• Step 555. Set Winning-ATS to null (i.e., undefined).
• Steps 559-565 If the returned Optimal-Value > Current-Minimum, as determined in step 559, do:
• Step 561 Set Current-Maximum to Optimal-Value; Step 563. Set Current-Variable-Instantiation to the returned Optimal- Variable-Instantiation.
• Step 565 Set Winning-ATS to the current ATS A.
• Step 567 Return as output:
• Optimizing-ATS which is an ATS whose Objective will be used for optimization.
• Committed-ATS-Set which is a set of ATS's that are committed to perform a transaction if and only if their Constraints are satisfied. The Objectives of the committed ATS's are not used in optimization.
• Step 571 For each K ATS's ⁇ Al, ...,Ak ⁇ in Committed-ATS-Set, perform ATS-One- to-AU-MM-optimization(Optimizing-ATS, ⁇ Al, ...,Ak ⁇ ).
• Step 573 Among all sets ⁇ Al, ...,Ak ⁇ , choose the one that has minimal (or maximal, as required in Optimizing-ATS) Optimal-Value.
• Step 575 Return as output the output of ATS-One-to-AU-MM-Optimization for the selected set ⁇ Al, ...,Ak ⁇ with the minimal (or maximal, as required in Optimizing-ATS) objective. End of Method.

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