DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the communications filed on August 20, 2026. The Applicant’s Amendment and Request for Reconsideration has been received and entered.
Claims 1, 4-8, 12-15, and 18-20 are pending and have been examined in this application. Claims 1, 8, and 15 have been amended. Claims 2, 3, 9-11, 16 and 17 have been cancelled.
Response to Arguments
Applicant’s amendments necessitated the new grounds of rejection.
Regarding the rejection of claims 1, 4-8, 12-15, and 18-20 under 35 USC 101, Applicant’s arguments have been fully considered but they are not persuasive for the reasons set forth infra.
Additionally, the claims recite commercial interactions between eCommerce buyers and sellers, including generating negotiation strategies, offers, counter-offers, concessions, negotiated terms, and ultimately matching as buyer with a seller based on those negotiated terms -- such activities constitute commercial/sales activities that fall within “certain methods of organizing human activity.” The fact that these commercial interactions are simulated or performed by AI does not alter the character of the underlying activity. While Applicant asserts that the claimed simulation provides a technological improvement because it permits an AI to improve its ability to engage in negotiations without necessitating engagement of actual negotiations by the AI before the AI is fully trained, the claims do not support these assertions. Indeed, the claims do not recite training the AI nor modifying or updating the machine-learning model based on the simulated negotiations. Rather the claimed machine learning model and predictive models are merely used to perform the negotiation activity and produce negotiated terms for matching buyers with sellers. Therefore, the alleged improvement to AI is not reflected in the claims.
The Examiner respectfully argues that “certain methods of organizing human activity” does not only apply to activities including at least one actual human. Rather “certain methods of organizing human activity” is a category which includes fundamental economic principles or practices, commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and managing personal behavior or relationships or interactions between people. Thus, matching the eCommerce buyer and seller set forth certain methods of organizing human activity, particularly commercial and legal interactions including advertising, marketing, and sales activities/behaviors.
Moreover, the Examiner respectfully argues that the human mind is capable of generating both sides of a negotiation and is commonly done in strategic preparation and provides evidentiary support in the attached Shonk article, which discusses “When engaging in negotiation preparation, it’s not sufficient to only look at your own needs and wants. To improve the odds of a mutually beneficial deal, you also need to figure out how much the other party may be willing to give. To do so, you need to analyze their BATNA. Ask yourself, “What will they do if our negotiation ends in impasse?” This will lead you to contemplate the other side’s reservation value.” Further, the Examiner respectfully notes that the legal standard for human mind does not hinge on whether human cognition requires a "singular" mind vs. a "multi-mind" structure.
Further, the Examiner respectfully argues that the claims do not disclose a number of iterations required to encompass simulated negotiations, but rather, merely state “a plurality of simulated negotiations” which may be as few as two simulated negotiations, which could practicably be performed in the human mind.
Thus, the 101 rejection is maintained.
Applicant’s remaining arguments have been fully considered but they are not persuasive. Particularly, Applicant’s arguments are directed to the instantly amended claims, and are thus moot in view of the new grounds of rejection.
The Examiner further notes that Krasadakis discloses a negotiation, specifically as seller AI negotiator prepares an adapted and negotiate offer and the buyer AI negotiator accepts, discards, or counters the offer or asks for a re-negotiation or new iteration based, possibly, on a specific ask (e.g., price, delivery method, etc.), this negotiation may be a dialogue presented on a web page, through text or e-mail messages, via mobile or desktop apps [0070] and through the system using natural language processing [0124].
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-8, 12-15, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A – Prong One. If the claims fall within one of the statutory categories, it must then be determined whether the claims recite an abstract idea, law of nature, or natural phenomenon.
Step 2A – Prong Two. If the claims recite an abstract idea, law of nature, or natural phenomenon, it must then be determined whether the claims recite additional elements that integrate the judicial exception into a practical application. If the claims do not recite additional elements that integrate the judicial exception into a practical application, then the claims are directed to a judicial exception.
Step 2B. If the claims are directed to a judicial exception, it must be evaluated whether the claims recite additional elements that amount to an inventive concept (i.e. “significantly more”) than the recited judicial exception.
In the instant case, claims 1 and 4-7 are directed to a process; claims 8 and 12-14 are directed to a process; and claims 15 and 18-20 are directed to a manufacture.
A claim “recites” an abstract idea if there are identifiable limitations that fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106. In the instant case, claim 1, and similarly claims 8 and 15, recites the steps of: obtaining prerequisite information related to an transaction, wherein the prerequisite information includes real-time market data, pricing trends and product availability; analyzing the prerequisite information using advance data analytic and algorithms to generate analyzed information responsive to market trends and demand patterns related to the transaction; conducting a plurality of simulated negotiations between a plurality of buyers and a plurality of sellers using a algorithm to generate negotiated terms between potential buyer-seller pairs, each potential buyer-seller pair being a combination of one buyer and one seller and each potential buyer-seller pair being distinct from each other buyer-seller pair, wherein conducting the plurality of simulated negotiations includes, for each simulated negotiation in the plurality of simulated negotiations, generating a negotiation strategy, including initial offer parameters, response tactics, and concession rates using game theory principles to anticipate potential party responses, and wherein generating the negotiation strategy further includes engaging in a simulated negotiation dialogue between the potential buyer-seller pairs using natural language and semantic analysis, wherein the simulated negotiation dialogue includes responses, counter-offers, and concessions between the potential buyer-seller pairs, and adjusting the simulated negotiation dialogue in real-time based on market changes, and wherein each simulated response, counter-offer, and concession between the potential buyer-seller pairs in the simulated negotiation dialogue is generated using a debater and matching an buyer with an seller based on a requirement of the buyer, an offering of the seller, and the negotiated terms of the simulated negotiation for the buyer-seller pair including the buyer and seller-- these claim limitations set forth certain methods of organizing human activity, particularly commercial and legal interactions including advertising, marketing, and sales activities/behaviors.
Additionally, these steps set forth mental processes, particularly concepts performed in the human mind or by a human using a pen and paper, including, inter alia, the observation and evaluation of information.
Further, the limitations of the claims are not indicative of integration into a practical application. Taking the independent claim elements separately, the additional elements of performing the steps via eCommerce, machine learning algorithms, predictive models, natural language processing, and using an application programming interface (API) -- merely implement the abstract idea on a computer environment. The dependent claims do not recite further additional elements. Considered in combination, the steps of Applicant’s method add nothing that is not already present when the steps are considered separately.
Thus, claims 1, 4-8, 12-15, and 18-20 are directed to an abstract idea.
Regarding the independent claims, the technical elements of performing the steps via eCommerce, machine learning algorithms, predictive models, simulated negotiation dialogue including simulated responses, and natural language processing merely implement the abstract idea on a computer environment. Additionally, the dependent claims do not recite further technical elements.
When considering the elements and combinations of elements, the claim(s) as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not amount to an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment; the claims merely amounts to the application or instructions to apply the abstract idea on a computer; or the claims amounts to nothing more than requiring a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.
The analysis above applies to all statutory categories of invention. Accordingly, claims 1, 4-8, 12-15, and 18-20 are rejected as ineligible for patenting under 35 USC 101 based upon the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 8, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Krasadakis (US PGP 2017/0287038) in view Kohavi (US PGP 2005/0119980).
As per claim 1, Krasadakis teaches [a] method for Autonomous Facilitation of Procurement Negotiation and Transaction in an eCommerce Environment, the method comprising:
obtaining prerequisite information related to an eCommerce transaction, wherein the prerequisite information includes real-time market data, pricing trends and product availability; (Krasadakis: Fig. 6 (604, 606, 608); Fig. 7 (732, 734, 736); [0089]-[0090] (The seller AI negotiator 208 retrieves and loads product definition information (either from a product catalog, online source, manual entry by the seller 174, a combination thereof, or some other source), inventory stock-availability information, sales targets (e.g., quantity to sell in a sales timeframe), profitability margins, and seller parameter elasticity, as shown at 604. The seller AI negotiator 208 scans the marketplace to perform a “demand analysis” for the product from the market data (e.g., based on any one or combination of the product data 212, pricing data 214, trends and social data 216, supply-and-demand data 218, and industry news 220 in the database cluster 204) and predicts the market viability selling potential for specific products and alternatives thereof. For example, a seller 174 of vehicles may notice that the price of a particular sport utility vehicle (SUV) is decreasing due to increased gasoline prices; whereas, a more fuel-efficient model (e.g., electric, hybrid, etc.) of the same SUV may be selling much faster due to the uptick in gasoline prices. This fuel-efficient model may be identified as a high-demand product to be selling in the current environment by the seller AI negotiator 208. Along these same lines, sales patterns and trend data on specific products and categories of products may be loaded to aid in shaping the demand analysis picture, as shown at 608.))
analyzing the prerequisite information using advance data analytic and machine learning algorithms to generate analyzed information responsive to eCommerce market trends and demand patterns related to the eCommerce transaction; (Krasadakis: Fig. 6 (608, 610); [0089]-[0091](The seller AI negotiator 208 scans the marketplace to perform a “demand analysis” for the product from the market data (e.g., based on any one or combination of the product data 212, pricing data 214, trends and social data 216, supply-and-demand data 218, and industry news 220 in the database cluster 204) and predicts the market viability selling potential for specific products and alternatives thereof. For example, a seller 174 of vehicles may notice that the price of a particular sport utility vehicle (SUV) is decreasing due to increased gasoline prices; whereas, a more fuel-efficient model (e.g., electric, hybrid, etc.) of the same SUV may be selling much faster due to the uptick in gasoline prices. This fuel-efficient model may be identified as a high-demand product to be selling in the current environment by the seller AI negotiator 208. Along these same lines, sales patterns and trend data on specific products and categories of products may be loaded to aid in shaping the demand analysis picture, as shown at 608. Product in industry news, user reviews, price statistics, and the like about the products—and alternative products—for sale by the retailer 174 may be automatically analyzed for a given sales campaign, as shown at 610. Based on the news, user reviews, and price statistics, an effective campaign execution timeframe is estimated. Additional or alternative criteria may be used in this estimation. For instance, product specifications, social media, online sources, industry news (e.g., Tesla announces a new production line with capacity of 1000 model S cars per month to service the Brazilian, Irish, and German markets), and any other market or buyer/seller parameter described herein may be used to estimate the sales campaign timeframe.))
conducting a plurality of . . . negotiations between a plurality of eCommerce buyers and a plurality of eCommerce sellers using a machine learning algorithm to generate negotiated terms between potential eCommerce buyer-seller pairs, each potential eCommerce buyer-seller pair being a combination of one eCommerce buyer and one eCommerce seller and each potential eCommerce buyer-seller pair being distinct from each other eCommerce buyer-seller pair, (Krasadakis: Fig. 6 (620-632); [0014] (The examples disclosed herein generally relate to an AI-powered framework whereby autonomous AI agents negotiate deals on behalf of buyers and sellers. . . . The disclosed examples allow a buyer to create a buyer AI negotiator for a product or service that interacts with the various seller AI negotiators of the retailers and identifies where to purchase the requested product or service based on buying parameters and elasticity of the consumer.); [0092]-[0094] (For each specific matching buying plan identified in the active buyer AI negotiators 206, the seller AI negotiator 208 identifies an offer price—as well as other offering terms (e.g., delivery, specification, etc.)—and prepares an adapted and negotiate offer for a (or multiple) buyer AI negotiator(s) 206, as shown at 620. The offer may then be submitted to the buyer AI negotiator 08, as shown at 622, to which the buyer AI negotiator 206 may accept, discard, or counter the or ask for a re-negotiation or new iteration based, possibly, on a specific ask (e.g., price, delivery method, etc.) from the seller 174. If the buyer AI negotiator 206 rejects the offer, work flow 600 repeats steps 620-622 to come up with a new offer, taking into account the recent rejection. In some examples, this portion (i.e., 620-624) of work flow 600 may be repeated until the buyer AI negotiator 206 accepts an offer from the seller AI negotiator 208, as shown by the NO pathway from decision box 624.); [0028]-[0030] (Once lists of seller AI negotiation agents are identified, in some examples, the buyer AI negotiation agent negotiates purchase details with the various seller AI negotiation agents to obtain the best deal for the buyer. . . . These back-and-forth negotiations enables the buyer and seller AI negotiation agents to quickly determine each other's allowable elasticity for the various buying and selling parameters, and such determinations may then be used by the buyer and seller AI negotiation agents to select optimal candidate (buyer and retailer) purchase a product or present to the buyers and sellers to purchase the product. . . . products may be discovered quickly through access to various AI seller agents, and the best purchase parameters may be secured through automatic AI agents that search and negotiate on behalf of the buyers. Similarly, sellers may set product selling campaigns that automatically locate, negotiate with, and secure potential product buyers, having explicitly stated their intention to buy what the sellers are trying to sell, without having to test standard predictive campaigns through conventional selling channels.);
wherein conducting the plurality of . . . negotiations includes, for each . . . negotiation in the plurality of . . . negotiations, generating a negotiation strategy, including initial offer parameters, response tactics, and concession rates using game theory principles and predictive models to anticipate potential party responses, and wherein generating the negotiation strategy further includes engaging in a . . . negotiation dialogue between the potential eCommerce buyer-seller pairs using natural language processing and semantic analysis, wherein the . . . negotiation dialogue includes . . . responses, counter-offers, and concessions between the potential eCommerce buyer-seller pairs, and adjusting the . . . negotiation dialogue in real-time based on market changes, and wherein each . . . response, counter-offer, and concession between the potential eCommerce buyer-seller pairs in the . . . negotiation dialogue is generated using a debater . . . ; (Krasadakis: [0092]-[0093] (The seller AI negotiators 208 may also have access to active product price statistics including competition pricing, offers and related insights of the specific and alternative products, either from previous sales of the seller 174, other seller AI negotiators 208, or buyer AI negotiators 206. The seller AI negotiator 208 analyzes active product price statistics from valid offers submitted to buyer AI negotiators 206, as shown at 616. The seller AI negotiator 208 also estimates price elasticity for the specific product being sold by the seller 174 based on the trends, sales statistics, profit margins, buying plan parameters, and the running performance of the sales campaign (e.g., how many products have sold and in what timeframe), as shown at 618. For each specific matching buying plan identified in the active buyer AI negotiators 206, the seller AI negotiator 208 identifies an offer price—as well as other offering terms (e.g., delivery, specification, etc.)—and prepares an adapted and negotiate offer for a (or multiple) buyer AI negotiator(s) 206, as shown at 620. The offer may then be submitted to the buyer AI negotiator 08, as shown at 622, to which the buyer AI negotiator 206 may accept, discard, or counter the or ask for a re-negotiation or new iteration based, possibly, on a specific ask (e.g., price, delivery method, etc.) from the seller 174. If the buyer AI negotiator 206 rejects the offer, work flow 600 repeats steps 620-622 to come up with a new offer, taking into account the recent rejection. In some examples, this portion (i.e., 620-624) of work flow 600 may be repeated until the buyer AI negotiator 206 accepts an offer from the seller AI negotiator 208, as shown by the NO pathway from decision box 624.); [0026]; [0042]-[0047]; [0057]-[0058]; [0124] (Machine learning, natural language processing, data mining, statistical modeling, predictive modeling, deep learning, neural networks, and game theory components may be implemented); Figs. 3-4; [0078]-[0081]; [0067] (Similarly, the seller AI negotiators 206 may dynamically adjust product deal terms for a product based on the negotiated or offered terms of multiple buyer AI negotiators 206. For example, if multiple buyer AI negotiators 206 are offering the same price for a product, and the overall response from the seller AI negotiators 208 proves to be smaller than expected implying limited supply for the product at this price, a new seller AI negotiator 210 may raise the starting price of the product and then start negotiation sessions with buyer AI negotiators 206 to determine which ones want the product as the new market-adjusted price. In the same example, existing seller AI negotiators 208 may increase the price based on the same signals, and request the engaged Buyer AI agents to approve it. This multi-stage negotiation session may occur with any of the selling parameters and may be based on the level of interest in products at certain deal parameters, the market conditions, or a combination thereof.); [0028]; [0049]; [0064]-[0068])
matching an eCommerce buyer with an eCommerce seller based on a requirement of the eCommerce buyer, an offering of the eCommerce seller, and the negotiated terms of the . . . negotiation for the eCommerce buyer-seller pair including the eCommerce buyer and the eCommerce seller. (Krasadakis: Fig. 6 (620-632); [0092]-[0094] (In some examples, the seller AI negotiator 208 on the application server 202 scans for active buyers 124 by scanning for associated buyer AI negotiators 206 that are attempting to negotiate buyer plans requesting the same or similar products than those offered by the seller 174, as shown at 614. . . . For each specific matching buying plan identified in the active buyer AI negotiators 206, the seller AI negotiator 208 identifies an offer price—as well as other offering terms (e.g., delivery, specification, etc.)—and prepares an adapted and negotiate offer for a (or multiple) buyer AI negotiator(s) 206, as shown at 620. The offer may then be submitted to the buyer AI negotiator 08, as shown at 622, to which the buyer AI negotiator 206 may accept, discard, or counter the or ask for a re-negotiation or new iteration based, possibly, on a specific ask (e.g., price, delivery method, etc.) from the seller 174. . . . If the seller 174 accepts the offer, a purchase order for the product may be created, as shown at 632, or a link to a checkout web page, including the identifiers mentioned above, or a coupon or other way to summarize the negotiated offer agreement in a claim ticket.))
Krasadakis does not explicitly disclose the following known techniques which are taught by Kohavi:
. . . simulated negotiation . . . (Kohavi: [0228]-[0230] (To encourage consumers to use this system, Lufthansa allows consumers to have three simulated negotiations, where they don't commit to buy anything.))
. . . simulated responses . . . (Kohavi: [0228]-[0230] (To encourage consumers to use this system, Lufthansa allows consumers to have three simulated negotiations, where they don't commit to buy anything. . . . Only after they did some simulated negotiation sessions where they verified the effect of the modifications . . .); [0011] (A customer or user of the system engages in dialog with a simulated merchant having a predefined algorithm. The simulated merchant responds to the customer's input data and agrees to a particular price according to the algorithm.); [0036])
. . . using a . . . application programming interface (API) (Kohavi: [0226]-[0227] (use the API and integrate the ANS and the in-house system. After this is done, the ANS approaches the in-house system with each reply it's about to give to a negotiating consumer, and the in-house system decides whether to leave the reply as is or to override it with the in-house system selected reply.))
This known technique is applicable to the method of Krasadakis as they both share characteristics and capabilities, namely, they are directed to electronic negotiations.
One of ordinary skill in the art at the time of filing would have recognized that applying the known technique of Kohavi would have yielded predictable results and resulted in an improved method. It would have been recognized that applying the technique of Kohavi to the teachings of Krasadakis would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such knowledge simulation and API features into similar methods. Further, applying the simulated negotiations, simulated responses, and API to the negotiations, responses, and software architecture of Krasadakis would have been recognized by those of ordinary skill in the art as resulting in an improved method that would help consumers get used to how such negotiations look before engaging in an obliging negotiation (Kohavi: [0229]) and allow integration and data sharing between systems (Kohavi: [0227]).
As per claim 4, Krasadakis/Kohavi teach wherein the eCommerce buyer is matched with the eCommerce seller based on an agreement being reached during the . . . negotiation dialogue (Krasadakis: Fig. 6 (620-632); [0092]-[0094] (In some examples, the seller AI negotiator 208 on the application server 202 scans for active buyers 124 by scanning for associated buyer AI negotiators 206 that are attempting to negotiate buyer plans requesting the same or similar products than those offered by the seller 174, as shown at 614. . . . For each specific matching buying plan identified in the active buyer AI negotiators 206, the seller AI negotiator 208 identifies an offer price—as well as other offering terms (e.g., delivery, specification, etc.)—and prepares an adapted and negotiate offer for a (or multiple) buyer AI negotiator(s) 206, as shown at 620. The offer may then be submitted to the buyer AI negotiator 08, as shown at 622, to which the buyer AI negotiator 206 may accept, discard, or counter the or ask for a re-negotiation or new iteration based, possibly, on a specific ask (e.g., price, delivery method, etc.) from the seller 174. . . . If the seller 174 accepts the offer, a purchase order for the product may be created, as shown at 632, or a link to a checkout web page, including the identifiers mentioned above, or a coupon or other way to summarize the negotiated offer agreement in a claim ticket.); [0064]-[0068])
. . . simulated negotiation . . . (Kohavi: [0228]-[0230] (To encourage consumers to use this system, Lufthansa allows consumers to have three simulated negotiations, where they don't commit to buy anything.))
The motivation for applying the known techniques of Kohavi to the teachings of Krasadakis is the same as that set forth above, in the rejection of Claim 1.
As per claim 8, this claim is substantially similar to claim 1 and is therefore rejected in the same manner as this claim, as set forth above. Additionally, Krasadakis/Kohavi teaches wherein the eCommerce buyer is matched with the eCommerce seller based on an agreement being reached during the . . . negotiation dialogue. (Krasadakis: Fig. 6 (620-632); [0092]-[0094]; [0026]; [0042]-[0049]; [0057]-[0058]; [0124]; Figs. 3-4; [0078]-[0081]; [0028]; [0064]-[0068])
. . . simulated negotiation . . . (Kohavi: [0228]-[0230] (To encourage consumers to use this system, Lufthansa allows consumers to have three simulated negotiations, where they don't commit to buy anything.))
The motivation for applying the known techniques of Kohavi to the teachings of Krasadakis is the same as that set forth above, in the rejection of Claim 1.
As per claims 15 and 18, these claims are substantially similar to claims 8 and 4, respectively, and are therefore rejected in the same manner as these claims, as set forth above.
Claims 5, 7, 12, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Krasadakis/Kohavi in view of Carstens (US PGP 2018/0197128).
As per claim 5, Krasadakis/Kohavi teach wherein matching the eCommerce buyer with the eCommerce seller includes . . . connecting potential supply relationships along a supply network with the eCommerce buyer and the eCommerce seller, wherein the links are based on the requirements of the eCommerce buyer and an ability of the eCommerce seller to meet the requirements of the eCommerce buyer. (Krasadakis: Fig. 6 (620-632); [0092]-[0094] (In some examples, the seller AI negotiator 208 on the application server 202 scans for active buyers 124 by scanning for associated buyer AI negotiators 206 that are attempting to negotiate buyer plans requesting the same or similar products than those offered by the seller 174, as shown at 614. . . . For each specific matching buying plan identified in the active buyer AI negotiators 206, the seller AI negotiator 208 identifies an offer price—as well as other offering terms (e.g., delivery, specification, etc.)—and prepares an adapted and negotiate offer for a (or multiple) buyer AI negotiator(s) 206, as shown at 620. The offer may then be submitted to the buyer AI negotiator 08, as shown at 622, to which the buyer AI negotiator 206 may accept, discard, or counter the or ask for a re-negotiation or new iteration based, possibly, on a specific ask (e.g., price, delivery method, etc.) from the seller 174. . . . If the seller 174 accepts the offer, a purchase order for the product may be created, as shown at 632, or a link to a checkout web page, including the identifiers mentioned above, or a coupon or other way to summarize the negotiated offer agreement in a claim ticket.); [0060]-[0068]( Negotiations between the buyer AI negotiators 206 and the seller AI negotiators 208 may be on matching what the buyers 124 plan to buy and what the sellers 174 plan to sell, taking into consideration the elasticity defined in both sides (e.g., specification, delivery, price, product information, etc.), the state of the market for the product, and in reference to the strategic goals or objectives of buyers or sellers (e.g., when the AI negotiators are looking to meet shared buying or selling targets). . . . In some examples, the buyers 124 and/or sellers 174 are presented with the deal parameters of negotiated offers for final acceptance. Alternatively, the buyer AI negotiator 206 and the seller AI negotiator 208 may be given autonomy to execute buying and selling on behalf of the buyer 124 and seller 174, respectively. To illustrate this latter scenario, a seller 174 may set a seller AI negotiator 208 to obtain at least a given profit margin on a particular product, given a cost of goods figure, for a set inventory of the product and may sell all of the inventory without seller 174 intervention, or may maximize, according to the strategy, the profit margin across a range of products within the same period; the volume of products sold; or combination thereof, including different targets for different products served by different seller AI negotiators 208. Likewise, the buyer 124 may, through his/her buying plan, set a buyer AI negotiator to purchase a particular product within a certain price so long as some mandatory product features (e.g., memory quantity, year/make of car, etc.) are included.))
Krasadakis/Kohavi do not explicitly disclose the following known technique which is taught by Carstens:
. . . generating a knowledge graph containing links connecting potential supply relationships along a supply network with the eCommerce buyer and the eCommerce seller . . . (Carstens: [0025]-[0026] (wherein each edge represents a directed supply relationship pointing from a supplier to a customer and directly relates items stored in the graph database; wherein the supply relations data is at least in part derived from a set of source data in electronic form and representing textual content comprising potential relation and risk phrases and/or numeric data; a risk scoring module adapted to access the first data set from the graph database, generate a set of scores related to the first data set, and store the set of scores in the graph database, wherein the risk scoring module generates the set of scores using both direct and transitive risk propagation along a plurality of nodes; and a supply graph generator adapted to access the first data set and the set of scores stored in the graph database and generate for presentation at a remote user computing device a directed graph comprising a plurality of interconnected nodes and edges representing a network of supply chain related entities.); [0016] (We represent relations between companies as a graph, where companies are represented as nodes and supply relations as directed edges, pointing from a supplier to a customer (or consignee). Not only does this allow us to interpret relations between companies in a formally defined manner, but it additionally provides the opportunity to investigate links between companies beyond their first-tier suppliers and customers. More specifically, we use this graph to identify peers of a company within its supply chain that are not only particularly relevant, but that are also exposed to certain risks and thus increase the potential for supply chain disruptions. Our graph-based model captures the connectedness of the supplier-consignee supply chain ecosystem in conjunction with the strength of the relationships and the risk exposure of each company entity, which transitively affects potentially large parts of the graph.))
This known technique is applicable to the method of Krasadakis/Kohavi as they share characteristics and capabilities, namely, they are directed to determining ability of sellers to meet buyer requirements.
One of ordinary skill in the art at the time of filing would have recognized that applying the known technique of Carstens would have yielded predictable results and resulted in an improved method. It would have been recognized that applying the technique of Carstens to the teachings of Krasadakis/Kohavi would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such knowledge graph features into similar methods. Further, applying the generating a knowledge graph containing links to the matching of Krasadakis/Kohavi would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow identification and mitigation of supply chain risk. (Carstens: [0013])
As per claim 7, Krasadakis/Kohavi/Carstens teach wherein the data is analyzed to assess supply risks, generate alternative actions in case of supply chain disruptions and to trace demand from an end user to a source of product materials and components. (Carstens: [0016]; [0021] (The present invention may be incorporated into an Enterprise Content Platform (ECP) that combines risk mining and supply chain graph information in a single database. This will provide supply chain risk mined from textual sources, and may include the results of risk mining using an SVP. The present invention may also be used as a component for event extraction application for detecting supply chain disruptions (e.g. Floods, explosions). The present invention may also be used in risk mining to automatically identify risks relating to suppliers in a supply chain.); [0025]; [0105] (A risk scoring module accesses supply relations data from the graph database at step 312, generates a set of scores related to the supply relations data at step 314, and stores the set of scores in the graph database at step 316. As shown in this example at step 315, the risk scoring module generates the set of scores using both direct and transitive risk propagation along a plurality of nodes.); [0045]; [0070]-[0078] (Scoring Method—The graph database described above facilitates the analysis of supply relations between companies within the context of a larger network. We now describe how we use the graph database to identify relevant suppliers of a customer through multiple tiers of the supply graph and score them according to two metrics, (1) importance and (2) risk. Importance, described in detail immediately below, scores suppliers of a company based on a combination of metrics, incorporating both the structure of the graph and the supplier's position in it, and attributes of the supplier itself. With it we aim to quantify the adverse impact that a disruption to the supply, e.g., of key components or materials, from a specific supplier would have on a specific customer. In this example, a high importance, e.g., a score close to 1, reflects a high potential adverse impact. Risk, described further below, is scored, for example, according the credit risk scores assigned to each company in the graph. . . . Replaceability (b): Represents a function of suppliers in the same business sector as sm, e.g., the sum of how many suppliers s operate in the same business sector (sϵS) as sm (based on TRBC codes): b=1−(Replaceability/(n−1)).); [0110] (For a given customer c having an associated nodec and having an identified set of suppliers s={s0, . . . sn} each having an associated node, the risk scoring module 124 may be further adapted to generate a set of risk scores R={r0, . . . rn} and a set of importance scores I={i0, . . . , in}. Each risk score rmϵR may be based on a single attribute of a node in the graph G, the single attribute representing, for example, a credit risk associated with supplier m. Each importance score im ϵ I may be an aggregate of a plurality of measures including, for example, at least two of the following measures: criticality; replaceability; centrality; and distance. Criticality may be a function of a proportion of goods, materials, commodities or other things received in business sector q associated with target company c as supplied from suppliers s={s0, . . . sn} operating in identified business sector associated with such suppliers s. Replaceability may be a function of the sum of the number of suppliers sϵ S that operate in the same business sector as sm.); Fig. 7; [0117]-[0120])
The motivation for applying the known techniques of Carstens to the teachings of Krasadakis/Kohavi is the same as that set forth above, in the rejection of Claim 5.
As per claims 12 and 14, these claims are substantially similar to claims 5 and 7, respectively, and are therefore rejected in the same manner as these claims, as set forth above.
As per claim 19, this claim is substantially similar to claim 5 and is therefore rejected in the same manner as this claim, as set forth above.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Krasadakis/Kohavi/Carstens in view of Riggs (US PGP 2005/0091100).
As per claim 6, Krasadakis/Kohavi/Carstens teach wherein vertices of the knowledge graph are augmented with data related to links between the potential supply relationships, wherein the data is updated in real-time . . . (Carstens: [0016] (We represent relations between companies as a graph, where companies are represented as nodes and supply relations as directed edges, pointing from a supplier to a customer (or consignee). Not only does this allow us to interpret relations between companies in a formally defined manner, but it additionally provides the opportunity to investigate links between companies beyond their first-tier suppliers and customers. More specifically, we use this graph to identify peers of a company within its supply chain that are not only particularly relevant, but that are also exposed to certain risks and thus increase the potential for supply chain disruptions. Our graph-based model captures the connectedness of the supplier-consignee supply chain ecosystem in conjunction with the strength of the relationships and the risk exposure of each company entity, which transitively affects potentially large parts of the graph.); [0025]-[0026]; [0070]-[0076]; Fig. 7; [0116]-[0120] (Now with reference to FIG. 7, an exemplary risk mapping 700 is provided comprised of supply risk mapping 702 for company C01 704 and supply risk mapping 706 for company C 02 708. The mappings of FIG. 7 illustrate the propagation of risk from node-to-node as contemplated in accordance with the present invention. This mapping visualization and interface provides a user with one effective manner of conveying risk associated with a related set of suppliers as the risk propagates along tier groups.); [0089] (In one manner, the supply data source 114 may comprise continuous feeds and may be updated, e.g., in near or close to real time (e.g., about 150 ms), allowing the SRRM 100 to automatically analyze content, update data based on “new” content in close to real-time, i.e., within approximately one second.))
The motivation for applying the known techniques of Carstens to the teachings of Krasadakis/Kohavi is the same as that set forth above, in the rejection of Claim 5.
Krasadakis/Kohavi/Carstens do not explicitly disclose the following known technique which is taught by Riggs:
. . .and includes transport cost, capacity, time, regulatory issues, and tariffs. (Riggs: [0054] (landed cost); [0053] (Routes, rates and transit times); [0087] (regulation data); [0091] (compliance regulations); [0097] (hazmat regulations); [0099] (regulations); [0043]-[0045] (tariffs))
This known technique is applicable to the method of Krasadakis/Kohavi/Carstens as they share characteristics and capabilities, namely, they are directed to data for provided products.
One of ordinary skill in the art at the time of filing would have recognized that applying the known technique of Riggs would have yielded predictable results and resulted in an improved method. It would have been recognized that applying the technique of Riggs to the teachings of Krasadakis/Kohavi/Carstens would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such transport cost, capacity, time, regulatory issues, and tariffs data features into similar methods. Further, applying the transport cost, capacity, time, regulatory issues, and tariffs to the data of Krasadakis/Kohavi/Carstens would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow for full logistics supply chain capability, providing advantages in circumstances where goods are transported across multiple modes leverage, and identifies differences between rates and negotiate intermediate rates which benefit all parties concerned by virtue of the various information available from the transport business interests (Riggs: [0005]-[0010], [0023], [0024]).
As per claim 13, this claim is substantially similar to claim 6 and is therefore rejected in the same manner as this claim, as set forth above.
As per claim 20, this claim is substantially similar to the limitations of claims 6 and 7 and is therefore rejected in the same manner as these claims, as set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Klaue, Susanne, Karl Kurbel, and Iouri Loutchko. "Automated negotiation on agent-based e-marketplaces: an overview." BLED 2001 Proceedings (2001): 8.
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/JENNIFER V LEE/Examiner, Art Unit 3688
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 9/28/2026
